林下水蚀区侵蚀过程与植被恢复度多角度遥感监测研究
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摘要
长期以来,水土流失一直是我国“头号”环境问题。植被是水土流失的关键控制因子,因而植树造林一度成为治理水土流失的首要措施。经过多年治理,我国人工林面积已接近世界三分之一,居世界首位,南方红壤区的森林覆盖率已达50%以上。但林下普遍缺少灌木或草本植被覆盖,水土流失(“林下水蚀区”)问题仍然突出,称之为“远看青山在,近看水土流”。“林下水蚀区”的存在,给水土保持工作提出了新的课题,“林下水蚀区”土壤侵蚀的发生、监测和评估研究是水土保持规划管理和学科发展的迫切需要。
在水力侵蚀地区,森林通过冠层截留降水以及枯落物和根系的防蚀作用发挥其水土保持功能,故林冠、枯落物和根系构成了森林独特的水土流失防护体系。林下枯落物及灌草层由于贴近地表,在消除雨滴击溅动能、降低坡面流速、延长径流历时和增加土壤入渗方面发挥着不可忽视的作用。我国的造林工作已取得巨大成果,但主要强调林冠覆盖,而森林覆盖率指标并不能反映林地的垂直层次结构,在表征林地水平覆盖方面亦有别于水土保持常用的植被覆盖度指标。在使用最为广泛的通用水土流失方程(USLE)中,植被覆盖和管理因子C值的计算精确与否,亦主要决定于所用的植被参数。由于不同地区、不同的研究者所用植被指标的差异,同类植被覆盖与管理因子C值的计算结果可能相差10-20倍。水土保持工作中理想的植被指标应综合表征植被的水平和垂直结构信息,在测量和应用方面具有统一的规范,以便于“林下水蚀区”土壤侵蚀的发生机理研究和区域植被的恢复重建与评估,并提升研究的科学性和通用性。
影响森林植被水土保持效应评估的另一个问题是植被参数遥感反演中的不确定性。应用遥感技术是目前大范围植被及生态调查的唯一途径。国内外研究表明,在植被参数的定量遥感研究中,遥感影像预处理(如辐射校正)、遥感信息提取(如各种植被指数)以及反演模型的建立和应用(包括经验模型和各种机理模型)这三个关键过程构成反演结果不确定性的重要来源。在植被结构指标与遥感植被指数的关系建模中,综合考虑上述不确定性因素,有利于充分挖掘遥感影像的信息,从而提高反演精度。
我国有关植被的水土保持效应研究已取得诸多成果,但大多研究集中在黄土丘陵区,这就限制了研究成果在其它土壤类型区的应用。福建省长汀县河田镇是我国南方典型的严重水土流失地区。在20世纪80年代初,为彻底根治河田镇的水土流失,福建省政府组织八个单位联合攻关,结合多种措施开展水土保持生态恢复工作。经过二十多年的努力,该镇的水土流失程度己逐步减轻。但是作为先锋树种的马尾松林分布广泛,“林下侵蚀”问题在该镇各地不同程度地存在。本文通过新建土壤侵蚀试验小区,选取叶面积指数、植被覆盖度和植被绿量3种植被结构指标,研究马尾松及其与速生草本植被宽叶雀稗结合的水土保持效应,并根据水土保持“最优”值和遥感反演的区域植被结构,评价研究区的植被恢复程度,以期为“林下水蚀区”的研究和治理提供理论基础和技术支撑。
本文基于福建省长汀县河田镇2010年初新建的12个土壤侵蚀试验小区,通过自然降雨条件下小区植被、产流和产沙连续一年共84次的观测,研究次降雨、季度和全年时间尺度下植被与产流、产沙的关系。使用叶面积指数(LAI)、植被覆盖度(VFC)和植被绿量(LVV)这3种植被结构指标表征植被的空间分布密度,并将稀疏马尾松纯林和林草结合小区的产流、产沙特征与对照小区比较,分析马尾松纯林和林草结合植被的水土保持效应；通过草地、灌木林、针叶林、针阔叶混交林和阔叶林5种植被演替群落结构指标的野外调查和多角度遥感反演,分析区域植被相对于水土保持“最优”值的恢复程度。主要结论如下：
(1)林、草和二者结合植被具有不同的水土保持效应。较之对照小区的地表径流量,季度和全年马尾松纯林小区可增加8%-22%,纯草小区减少3%-28%,林草结合小区减少18%-61%。较之对照小区的土壤侵蚀量,各季和全年马尾松纯林小区减少62%-75%,纯草及林草结合小区均减少97%-99%。林草结合植被的水土保持效应并非林、草植被水土保持效应之和,且水土保持效应具有时间变化特性,这表明对林、草及二者结合植被的结构特征和水土保持效应有必要进行深入的定量表征,以期系统研究二者的客观关系。
(2)在不同时间尺度下基于不同植被指标的林草植被水土保持效应计算模型精度不一。本研究表明,植被覆盖度(VFC)和绿量(LVV)可进行林地植被不同水土保持效应的表征。不同水土保持效应的计算模型或季度优于全年,或全年优于季度,或季度和全年相当,而在次降雨条件下,植被结构指标与植被水土保持效应的关系模型难以建立。
(3)遥感技术与水土保持研究的结合是侵蚀区植被恢复度监测和研究的可靠途径。精确的区域植被结构多角度遥感反演与植被的水土保持效应研究相结合,为侵蚀区植被相对于水土保持“最优”结构的恢复程度之监测与评估提供了基础。本研究表明,福建省长汀县河田镇植被的总体恢复度较低,在季度和全年尺度都仅为27%,绿量恢复度<20%的面积占该镇总面积的71%以上,而绿量恢复度>80%的面积仅占18%。
Water and soil loss is a focus of various ecological problems in China, and a root stock of ecological deterioration and poverty accumulation. The red soil area,covering2030000km2,21%of total land area in China, had once become a "red desert" in some regions for its widest and severest water and soil loss in southern China. With forest planting and conservation by forestry and soil and water conservation departments in recent decades, forest-coverage rate had increased obviously in many provinces, most of them exceeded50%, some were more than70%. Nevertheless, soil erosion occurred moderately or even intensely on forested areas, for the bareness of under-forest soil without close-cover of vegetation or residue. This under-forest soil erosion requires scientifically monitoring and calculating of water and soil conservation effects for vegetations on forested areas, through feasible vegetation indices indicating occurrence mechanism of under-forest soil erosion, and objectively evaluating of vegetation restoration degree on such water-eroded areas, so as to make a foundation for planning and management of water and soil conservation. On12experimental plots built in early2010in Hetian town, Changting county, Fujian province, this paper focuses on the analysis of the relationships between vegetation structure and its effects on water and soil conservation, with56measurements of vegetation, runoff, and soil erosion parameters, at3time scales, single rain fall, seasonal, and all-year. Three parameters of vegetation structures, leaf area index (LAI), vegetation fractional coverage (VFC), and live vegetation volumn (LVV,the production of LAI and VFC) were used to indicate spatial distribution density of vegetation, and the water and soil conservation effects of sparse Pinus massoniana and forest-grass combination were analyzed through comparing the runoff and soil erosion on the plots planted with sparse Pinus massoniana and forest-grass combination with those on the control plot; Through field measurement and remote-sensedly inversion of vegetation structures in different vegetation succession communities, grass, shrub, coniferous forest, coniferous-broad-leaf forest,and broad-leaf forest, local vegetation restoration degree (VRD) was evaluated based on "best" vegetation structures evaluated for water and soil conservation. The main conclusions are presented below:
(1) Water and soil conservation effects of forest, grass, and the combination of forest-grass are different. Compared to the control plot, the seasonal and all-year surface runoff increased by5%-16%on the plots planted with pure Pinus massoniana, while decreased by1%～33%on the plots planted with pure grass(Paspalurn wettsteinii), and decreased by15%～54%on the plots planted with the vegetation combination of tree (Pinus massoniana) and grass (Paspalurn wettsteinii). The seasonal and all-year soil loss decreased by56%～70%on the plots planted with pure Pinus massoniana,and decreased by91%～99%on the plots planted with pure Pinus massoniana or with the vegetation combination of tree (Pinus massoniana) and grass (Paspalurn wettsteinii). Water and soil conservation effects of forest-grass are not those additions of pure forest and grass, and there are temporal changes in the water and soil conservation effects. Consequently, it is required to finely and quantitatively indicate different vegetation structures and their effects on. water and soil conservation,so as to systematically study the relationships between them.
(2) The accuracies of results estimating water and soil conservation effects of forests and grasses are different for the models based on different vegetation indices and time scales. Vegetation fractional coverage (VFC) and live vegetation volumn (LVV) could be used to indicate the water and soil conservation effects of vegetation on forested area. Among various models calculating the water and soil conservation effects of forests and grasses, some seasonal models are better than all-year ones while others the reverse, or close to each other. Under single rainfall, however, it is difficult to establish models between vegetation structures and water and soil conservation effects.
(3) The union of remote sensing techniques and researches on water and soil conservation effects of vegetation is a credible means to monitor and study vegetation restoration degree on eroded areas. The monitoring and evaluation of local to regional vegetation restoration degree, compared with the "best" vegetation structures for water and soil conservation,are supported by accurate inversion of vegetation structures by remote sensing, and studies on water and soil conservation effects of vegetation on eroded areas. Our study showed that, vegetation restoration degree of the study area are at the low level, and are only30%at both seasonal and all-year time scales. The area where live vegetation volumn (LVV) restoration degree<20%and>80%respectively covered more than60%and less than20%of the total area in Hetian town, Changting county, Fujian province.
在水力侵蚀地区,森林通过冠层截留降水以及枯落物和根系的防蚀作用发挥其水土保持功能,故林冠、枯落物和根系构成了森林独特的水土流失防护体系。林下枯落物及灌草层由于贴近地表,在消除雨滴击溅动能、降低坡面流速、延长径流历时和增加土壤入渗方面发挥着不可忽视的作用。我国的造林工作已取得巨大成果,但主要强调林冠覆盖,而森林覆盖率指标并不能反映林地的垂直层次结构,在表征林地水平覆盖方面亦有别于水土保持常用的植被覆盖度指标。在使用最为广泛的通用水土流失方程(USLE)中,植被覆盖和管理因子C值的计算精确与否,亦主要决定于所用的植被参数。由于不同地区、不同的研究者所用植被指标的差异,同类植被覆盖与管理因子C值的计算结果可能相差10-20倍。水土保持工作中理想的植被指标应综合表征植被的水平和垂直结构信息,在测量和应用方面具有统一的规范,以便于“林下水蚀区”土壤侵蚀的发生机理研究和区域植被的恢复重建与评估,并提升研究的科学性和通用性。
影响森林植被水土保持效应评估的另一个问题是植被参数遥感反演中的不确定性。应用遥感技术是目前大范围植被及生态调查的唯一途径。国内外研究表明,在植被参数的定量遥感研究中,遥感影像预处理(如辐射校正)、遥感信息提取(如各种植被指数)以及反演模型的建立和应用(包括经验模型和各种机理模型)这三个关键过程构成反演结果不确定性的重要来源。在植被结构指标与遥感植被指数的关系建模中,综合考虑上述不确定性因素,有利于充分挖掘遥感影像的信息,从而提高反演精度。
我国有关植被的水土保持效应研究已取得诸多成果,但大多研究集中在黄土丘陵区,这就限制了研究成果在其它土壤类型区的应用。福建省长汀县河田镇是我国南方典型的严重水土流失地区。在20世纪80年代初,为彻底根治河田镇的水土流失,福建省政府组织八个单位联合攻关,结合多种措施开展水土保持生态恢复工作。经过二十多年的努力,该镇的水土流失程度己逐步减轻。但是作为先锋树种的马尾松林分布广泛,“林下侵蚀”问题在该镇各地不同程度地存在。本文通过新建土壤侵蚀试验小区,选取叶面积指数、植被覆盖度和植被绿量3种植被结构指标,研究马尾松及其与速生草本植被宽叶雀稗结合的水土保持效应,并根据水土保持“最优”值和遥感反演的区域植被结构,评价研究区的植被恢复程度,以期为“林下水蚀区”的研究和治理提供理论基础和技术支撑。
本文基于福建省长汀县河田镇2010年初新建的12个土壤侵蚀试验小区,通过自然降雨条件下小区植被、产流和产沙连续一年共84次的观测,研究次降雨、季度和全年时间尺度下植被与产流、产沙的关系。使用叶面积指数(LAI)、植被覆盖度(VFC)和植被绿量(LVV)这3种植被结构指标表征植被的空间分布密度,并将稀疏马尾松纯林和林草结合小区的产流、产沙特征与对照小区比较,分析马尾松纯林和林草结合植被的水土保持效应；通过草地、灌木林、针叶林、针阔叶混交林和阔叶林5种植被演替群落结构指标的野外调查和多角度遥感反演,分析区域植被相对于水土保持“最优”值的恢复程度。主要结论如下：
(1)林、草和二者结合植被具有不同的水土保持效应。较之对照小区的地表径流量,季度和全年马尾松纯林小区可增加8%-22%,纯草小区减少3%-28%,林草结合小区减少18%-61%。较之对照小区的土壤侵蚀量,各季和全年马尾松纯林小区减少62%-75%,纯草及林草结合小区均减少97%-99%。林草结合植被的水土保持效应并非林、草植被水土保持效应之和,且水土保持效应具有时间变化特性,这表明对林、草及二者结合植被的结构特征和水土保持效应有必要进行深入的定量表征,以期系统研究二者的客观关系。
(2)在不同时间尺度下基于不同植被指标的林草植被水土保持效应计算模型精度不一。本研究表明,植被覆盖度(VFC)和绿量(LVV)可进行林地植被不同水土保持效应的表征。不同水土保持效应的计算模型或季度优于全年,或全年优于季度,或季度和全年相当,而在次降雨条件下,植被结构指标与植被水土保持效应的关系模型难以建立。
(3)遥感技术与水土保持研究的结合是侵蚀区植被恢复度监测和研究的可靠途径。精确的区域植被结构多角度遥感反演与植被的水土保持效应研究相结合,为侵蚀区植被相对于水土保持“最优”结构的恢复程度之监测与评估提供了基础。本研究表明,福建省长汀县河田镇植被的总体恢复度较低,在季度和全年尺度都仅为27%,绿量恢复度<20%的面积占该镇总面积的71%以上,而绿量恢复度>80%的面积仅占18%。
Water and soil loss is a focus of various ecological problems in China, and a root stock of ecological deterioration and poverty accumulation. The red soil area,covering2030000km2,21%of total land area in China, had once become a "red desert" in some regions for its widest and severest water and soil loss in southern China. With forest planting and conservation by forestry and soil and water conservation departments in recent decades, forest-coverage rate had increased obviously in many provinces, most of them exceeded50%, some were more than70%. Nevertheless, soil erosion occurred moderately or even intensely on forested areas, for the bareness of under-forest soil without close-cover of vegetation or residue. This under-forest soil erosion requires scientifically monitoring and calculating of water and soil conservation effects for vegetations on forested areas, through feasible vegetation indices indicating occurrence mechanism of under-forest soil erosion, and objectively evaluating of vegetation restoration degree on such water-eroded areas, so as to make a foundation for planning and management of water and soil conservation. On12experimental plots built in early2010in Hetian town, Changting county, Fujian province, this paper focuses on the analysis of the relationships between vegetation structure and its effects on water and soil conservation, with56measurements of vegetation, runoff, and soil erosion parameters, at3time scales, single rain fall, seasonal, and all-year. Three parameters of vegetation structures, leaf area index (LAI), vegetation fractional coverage (VFC), and live vegetation volumn (LVV,the production of LAI and VFC) were used to indicate spatial distribution density of vegetation, and the water and soil conservation effects of sparse Pinus massoniana and forest-grass combination were analyzed through comparing the runoff and soil erosion on the plots planted with sparse Pinus massoniana and forest-grass combination with those on the control plot; Through field measurement and remote-sensedly inversion of vegetation structures in different vegetation succession communities, grass, shrub, coniferous forest, coniferous-broad-leaf forest,and broad-leaf forest, local vegetation restoration degree (VRD) was evaluated based on "best" vegetation structures evaluated for water and soil conservation. The main conclusions are presented below:
(1) Water and soil conservation effects of forest, grass, and the combination of forest-grass are different. Compared to the control plot, the seasonal and all-year surface runoff increased by5%-16%on the plots planted with pure Pinus massoniana, while decreased by1%～33%on the plots planted with pure grass(Paspalurn wettsteinii), and decreased by15%～54%on the plots planted with the vegetation combination of tree (Pinus massoniana) and grass (Paspalurn wettsteinii). The seasonal and all-year soil loss decreased by56%～70%on the plots planted with pure Pinus massoniana,and decreased by91%～99%on the plots planted with pure Pinus massoniana or with the vegetation combination of tree (Pinus massoniana) and grass (Paspalurn wettsteinii). Water and soil conservation effects of forest-grass are not those additions of pure forest and grass, and there are temporal changes in the water and soil conservation effects. Consequently, it is required to finely and quantitatively indicate different vegetation structures and their effects on. water and soil conservation,so as to systematically study the relationships between them.
(2) The accuracies of results estimating water and soil conservation effects of forests and grasses are different for the models based on different vegetation indices and time scales. Vegetation fractional coverage (VFC) and live vegetation volumn (LVV) could be used to indicate the water and soil conservation effects of vegetation on forested area. Among various models calculating the water and soil conservation effects of forests and grasses, some seasonal models are better than all-year ones while others the reverse, or close to each other. Under single rainfall, however, it is difficult to establish models between vegetation structures and water and soil conservation effects.
(3) The union of remote sensing techniques and researches on water and soil conservation effects of vegetation is a credible means to monitor and study vegetation restoration degree on eroded areas. The monitoring and evaluation of local to regional vegetation restoration degree, compared with the "best" vegetation structures for water and soil conservation,are supported by accurate inversion of vegetation structures by remote sensing, and studies on water and soil conservation effects of vegetation on eroded areas. Our study showed that, vegetation restoration degree of the study area are at the low level, and are only30%at both seasonal and all-year time scales. The area where live vegetation volumn (LVV) restoration degree<20%and>80%respectively covered more than60%and less than20%of the total area in Hetian town, Changting county, Fujian province.
引文
[1]安勇,卓丽环.哈尔滨市紫丁香绿量.东北林业大学学报,2004,32(6)：81-84.
[2]卜兆宏,孙金庄,周伏键,唐万龙.水土流失定量遥感方法及其应用.土壤学报,1997,34(3)：235-244.
[3]卜兆宏,赵宏夫,刘绍清,陈明华.用于土壤流失量遥感监测的植被因子算法的初步研究.遥感技术与应用,1993,8(4)：16-22.
[4]蔡强国,刘纪根.关于我国土壤侵蚀模型研究进展.地理科学进展,2003,22(3)：242-250.
[5]陈冬林.红壤坡地不同森林恢复类型水土保持效应及其机制研究.湖南农业大学硕士学位论文,2004.
[6]江洪.长汀县水土流失遥感监测及其生态安全评价.福州大学硕士学位论文,2005
[7]陈芳,周志翔,王鹏程,李海防,钟颖飞.武汉钢铁公司厂区绿地绿量的定量研究.应用生态学报,2006,17(4)：592-596.
[8]陈引珍.三峡库区森林植被水源涵养及其保土功能研究.北京林业大学博士学位论文,2007.
[9]陈志清.福建省长汀县河田镇的水蚀漠化及其治理.地理科学进展.1998,17(2)：65-70.
[10]丁军.红壤丘陵区人工林地生态学特征及其水土保持机理研究.浙江大学博士学位论文,2003.
[11]傅集贤,龙俊和,彭文海.湘中丘陵花岗岩侵蚀劣地水土保持林效益研究.水土保持通报,1998,18(6)：49-53.
[12]高兆蔚.福建长汀河田水土流失综合治理成效与继续治理对策建议.福建水土保持,2004,16(4)：11-13.
[13]郭忠升.水土保持林有效覆盖率及其确定方法的研究.土壤侵蚀与水土保持学报,1996,2(3)：67-71.
[14]郭忠升.最佳森林覆盖率的初步研究.西北林学院学报,1998,13(3)：23-27.
[15]韩冰,吴钦孝,刘向东.林地枯枝落叶层对溅蚀影响的研究.防护林科技,1994, (2)：7-10.
[16]胡江波.不同植被恢复模式的水土保持效果及土壤水肥生态效应研究.西北农林科技大学硕士学位论文,2007.
[17]胡良军,李锐,杨勤科.基于RS和GIS的区域水土流失快速定量评价方法.水土保持通报,2000,20(6)：42-44.
[18]黄秉维.再谈森林的作用(四).地理知识,1982,4(2)：11-18.
[19]惠凤鸣,田庆久,金震宇,李海涛.植被指数与叶面积指数关系研究及定量化分析.遥感信息,2003,2：10-12.
[20]蒋定生.黄土高原水土流失与治理模式.北京：中国水利水电出版社.1997,(4)：8-26.
[21]蒋芳市.不同治理措施对侵蚀退化生态系统恢复的影响.福建农林大学硕士学位论文,2007.
[22]康冰.广西大青山退化森林植被特征及生态恢复研究.西北农林科技大学博士学位论文,2007.
[23]宋永昌.植被生态学.上海：华东师范大学出版社,2001,598-599.
[24]雷环清.兴国县花岗岩区林下水土流失及其防治.中国水土保持,2007,3：58-59.
[25]雷瑞德.华山松林冠对降雨动能的影响.见：林业部科技司编,森林生态系统定位研究.哈尔滨：东北林业大学出版社,1994.
[26]雷廷武,张晴雯,姚春梅等.WEPP模型中细沟可蚀性参数估计方法误差的理论分析.农业工程学报,2005,21(1)：9-12.
[27]李锐,杨勤科,温仲明等.区域土地利用变化环境效应研究综述.水土保持通报,2002,22(2)：65-70.
[28]李文华,何永涛,杨丽锡.森林对径流影响研究的回顾和展望.自然资源学报,2001,11(5)：390-406.
[29]李先琨,黄玉清,苏宗明,冯玲,叶惊春,覃德深.广西水土流失重点区域生态恢复试验研究.水土保持通报,1997,17(6)：1-6.
[30]廖善刚,赵昭蛹.长汀河田风化花岗岩坡面水土流失规律和预测方程.福建师范大学学报(自然科学版),1990,6(1)：105-109.
[31]刘宝元,谢云,张科利.土壤侵蚀预报模型.中国科学技术出版社,北京。2001.
[32]刘秉正,吴发启.土壤侵蚀.西安：陕西人民出版社,1997,23-35.
[33]刘定辉,李勇.植物根系提高土壤抗侵蚀性机理研究.水土保持学报,2003,17(3)：34-37.
[34]刘光崧.土壤理化分析与剖面描述.中国标准出版社,1996,(6)：PP：266.
[35]刘国彬.黄土高原草地土壤抗冲性及其机理研究.水土保持学报,1998(1)：93-96.
[36]刘洪生.生态修复在长汀水土流失治理的几种应用模式分析.亚热带水土保持,2005,17(3)：31-33.
[37]刘伦辉,刘文耀.滇中山地主要植被群落水土保持效益比较.水土保持学报,1990,4(1)：36-42.
[38]刘文耀,刘伦辉,郑征等.滇中常绿阔叶林及云南松林水文作用的初步研究.植物生态学与地植物学学报,1991,15(2)：159-167.
[39]刘向东,吴钦孝,赵鸿雁.森林植被垂直截留作用与水土保持.水土保持研究,1994,1(3)：8-13.
[40]刘旭东.森林环境资源动态监测指标及信息系统框架.福建农林大学硕士学位论文,2005.
[41]刘志韬.山西管涔山林区森林对径流的影响.水土保持通报,1981,1(4)：56-61.
[42]吕妙儿,蒲英霞,黄杏元.城市绿地监测遥感应用.中国园林,2000,16(5)：41-44.
[43]潘成忠,上官周平.黄土区次降雨条件下林地径流和侵蚀产沙形成机制-以人工油松林和次生山杨林为例.应用生态学报,2005,16(9)：1597-1602.
[44]秦富仓.黄土地区流域森林植被格局对侵蚀产沙过程的调控研究.北京林业大学博士学位论文,2006.
[45]邱雪红,杨洁.江西省林地水土流失状况调查及对策.亚热带水土保持,2007,19(4)：35-37.
[46]Richard Lee,张建列译.森林水文学.哈尔滨：东北林业大学出版社,1984.
[47]邵云.沙棘人工林水土保持效益分析.辽宁林业科技,1995,(4)：62-64.
[48]沈中原.坡面植被格局对水土流失影响的实验研究.西安理工大学硕士学位论文,2006.
[49]史德明.如何正确理解有关水土保持术语的讨论.土壤侵蚀与水土保持学报.1998,4(4)：89-91.
[50]史德明.土壤侵蚀对生态环境的影响及其防治对策.水土保持学报,1991,5(3)：1-8.
[51]史德明,史学正,梁音.我国不同空间尺度土壤侵蚀的动态变化.水土保持通报,2005,25(5)：85-89.
[52]史学正,梁音,于东升.“土壤水库”的合理调用与防洪减灾.土壤侵蚀与水土保持学报,1999,5(3)：6-10.
[53]史学正,于东升,邢廷炎.用田间实测法研究我国亚热带土壤的可蚀性K值.土壤学报.1997,34(4)：399-405.
[54]王礼先,解明曙.山地防护林水土保持水文生态效益及其信息系统.北京：中国林业出版社,1997.
[55]王礼先,张志强.森林植被变化的水文生态效益研究进展.世界林业研究,1998,(6)：14-23.
[56]王希群,马履一,贾忠奎,徐程扬.叶面积指数的研究和应用进展.生态学杂志,2005,24(5)：537-541.
[57]王信理.在作物干物质积累的动态模拟中如何合理运用Logistic方程.农业气象,1986,7(1)：14-19.
[58]王震洪,张玲,段昌群,肖林,王克勤,梁国江,张世彪,起连春.滇中三种典型人工林群落结构与土壤侵蚀关系研究.水土保持研究,2001,8(2)：74-79.
[59]魏霞.福建长汀重建生态系统马尾松叶片气体交换与叶绿素荧光特征研究.福建师范大学植物学硕士学位论文,2007.
[60]吴钦孝,刘向东,苏宁虎.山杨次生林枯枝落叶蓄积量及其水文作用.水土保持学报,1992,6(1)：71-76.
[61]谢锦升.植被恢复对退化红壤易变碳及土壤呼吸的影响.福建农林大学博士学位论文,2005：27-29.
[62]杨大三.鄂西三峡库区防护林研究.武汉：湖北科学技术出版社,1996.
[63]于东升,史学正,王宁.用人工模拟降雨研究亚热带坡耕地土壤的沟蚀和沟间侵蚀.土壤学报,2001,38(2)：160-166.
[64]余坤勇,刘健,赖玫妃,庞闽志,陈振平.基于防止土壤侵蚀为目标富屯溪最佳森林覆盖率的确定.江西农业大学学报,2007,29(3)：398-403.
[65]喻荣岗,左长清,杨洁,谢颂华,李小强,王昭艳.红壤侵蚀区几种水土保持林水文效应研究.水土保持通报,2007,27(6)：194-198.
[66]余作岳,彭少麟.热带亚热带退化生态系统植被恢复生态学研究.广州；广东科技出版社,1996.
[67]袁东海.红壤小流域水土流失规律及防治措施的研究.浙江大学博士学位论文,2001.
[68]曾河水,岳辉.长汀县以河田为中心的花岗岩强度水土流失区植被重建的主要模式.福建水土保持,2004,16(4)：16-18.
[69]曾思齐,佘济云.马尾松水土保持林水文功能计量研究：Ⅰ.林冠截留与土壤贮水能力.中南林学院学报,1996,16(3)：1-8.
[70]曾志远.卫星遥感图像计算机分类与地学应用研究.北京：科学出版社,2004.
[71]张保华,王喜,刘子亭,何毓蓉.贡嘎山天然林和盐亭人工林土壤侵蚀研究.中国水土保持科学,2006.
[72]张颖.黄土地区森林植被对坡面土壤侵蚀过程影响机理研究.北京林业大学博士学位论文,2007.
[73]赵鸿雁,吴钦孝,陈云明.黄土高原不同处理人工油松林地水土流失研究.西北农林科技大学学报,2002,30(6)：171-173.
[74]赵良平.燕山山地森林植被恢复与重建理论和技术研究.南京林业大学博士学位论文,2007.
[75]赵其国.我国红壤的退化问题.土壤,1995,27(6)：281-286.
[76]赵其国.我国南方当前水土流失与生态安全中值得重视的问题.水土保持通报,2006,26(2)：1-8.
[77]郑粉莉,王占礼,杨勤科.我国水蚀预报模型研究的现状、挑战与任务.中国水土保持科学,2005,3(1)：7-14.
[78]郑华,欧阳志云,王效科,苗鸿,赵同谦,彭廷柏.不同森林恢复类型对南方红壤侵蚀区土壤质量的影响.生态学报,2004,24(9)：1994-2002.
[79]郑怀舟.福建长汀重建生态系统植物树干液流特征研究.福建师范大学硕士学位论文,2007.
[80]郑巍伟.三峡库区森林和土壤侵蚀关系研究.防护林科技,2006,6：45-47.
[81]郑巍伟,李白勤.三峡库区秭归县森林与土壤侵蚀的相关性研究.西部林业科学,2006,35(3)：62-67.
[82]中野秀章,李云森译.森林水文学.北京：中国林业出版社,1983.
[83]周坚华,孙天纵.三维绿色生物量的遥感模式研究与绿化环境效益估算.环境遥感,1995,10(3)：162-174.
[84]周跃,付玉彬,徐清艳,曾镇涛.林草植被土壤侵蚀控制作用的初步研究—以云南新平大红山铁矿为例.昆明理工大学学报(理工版),2005,30(6)：68-72.
[85]周跃,李宏伟,徐强.云南松林的林冠对十壤侵蚀的影响.山地学报,1999,17(4)：324-328.
[86]周跃,李玉辉,Watts D. Potential of Yunnan pine forest on soil erosion control in the Hotiaoxia Gorge area, SW China.植物生态学报,2000,24(1)：74-81.
[87]周跃,Watts David.高山峡谷区云南松林土壤侵蚀控制的水文效应.土壤侵蚀与水土保持学报,1998,4(3)：31-38.
[88]朱显漠.黄土地区植被因素对水土流失的影响.土壤学报,1960,8(2)：110-121.
[89]朱显漠.黄土高原脱贫致富之道——二论黄土高原的国土整治.水土保持学报,1998(4)：1-5.
[90]朱显漠,田积莹.强化黄土高原土壤渗透性及抗冲性的研究.水土保持学报,1993,7(3)：1-10.
[91]曾河水,岳辉.长汀县以河田为中心的花岗岩强度水土流失区植被重建的主要模式.福建水土保持,2004,16(4)：16-18.
[92]Abdou,W.A., Pilorz,S.H., Helmlinger,.M.C., Conel,J.E., Diner,D.J.,and Bruegge,C.J.,et al. Sua Pan surface bidirectional reflectance:A case study to evaluate the effect of atmospheric correction on the surface products of the Multi-angle Imaging SpectroRadiometer (MISR) during SAFARI 2000. IEEE Transactions on geoscience and remote sensing,2006,44(7):1699-1706.
[93]Andres Vina, A., Gitelson A., Anthony L. Nguy-Robertson, Yi P. Comparison of different vegetation indices for the remote assessment of green leaf area index of crop. Remote sensing of Environment,2011,115:3468-3478.
[94]Anatoly,A.G,Yoram,J.K., Robert, S.,and Don,R. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment.2002,80(1): 76-87.
[95]Baret F, Guyot G Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment.1991,35:161-173
[96]Beasley, D.B., Huggins, L.F., and Monke, E.J. ANSWERS:a model for watershed planning. Transactions of the ASAE,1980,938-944.
[97]Bonan G B. Land-atmosphere interactions for climate system models:coupling biophysical, biogeochemical, and ecosystem dynamical processes. Remote Sensing of Environment.1995,51(1):57-73.
[98]Bornnann, F.H., and Likens, G E. Pattern and Processes in a freseted ecosystem. Springer-Verlag New York,1979.
[99]Broge, N.H., MortenEn, J.V.,2002. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Ensing of Environment 81 (1),45-57
[100]Canisius, F., Chen, J. Retrieving forest background reflectance in a boreal region from Multi-angle Imaging SpectroRadiometer(MISR) data. Remote Ensing of Environment.2007,107:312-321
[101]Carlson, T. N., Perry, E. M., and Schmugge,T. J. Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agricultural and Forest Meteorology,1990,52:45-69.
[102]Chao,Y.L., Wen,T.L., and Wen,C.C. Soil erosion prediction and sediment yield estimation:the Taiwan experience. Soil and Tillage Research,2002,68:143-152.
[103]Chen, J.M.,and Black,T.A.Defining leaf area index for non-flat leaves.Plant Cell and Environment,1992,15:421-429.
[104]Chen, J.M., Cihlar, J. Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM images. Remote Sensing of Environment,1996,55:153-162.
[105]Chen, J.M., Pavlic, G, Brown, L., Cihlar, J., Leblanc, S.G, White, H.P., Hall, R.J., Peddle, D.R., King, D.J., Trofymow, J.A. Derivation and validation of Canada-wide coarE-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote sensing of Environment,2002,80(1):165-184.
[106]Chen,Q. Multisensor comparisons for validation of MODIS vegetation indices. Pedosphere,2006,16 (3):362-370.
[107]Chen, X.X., Lee V., Eric,R.and Thomas,D. Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+vegetation cover estimates in a ponderosa pine forest.Remote Sensing of Environment,2004,91:14-26.
[108]Cohen,W.B., Maiersperger, T.K., Gower, S.T., Turner, D.P. An improved strategy for regression of biophysical variables and Landsat ETM+data. Remote sensing of Environment.2003,84 (4),561-571.
[109]Colombo R., Bellingeri D., Fasolini D., Carlo M. Marino, Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Ensing of Environment,2003,86:120-131.
[110]Conghe,S. Spectral mixture analysis for subpixel vegetation fractions in the urban environment:How to incorporate endmember variability.Remote Sensing of Environment.2005,95:248-263.
[111]Darcy,K. M. and Pierre, Y. J. Estimation of upland erosion using GIS. Computers & Geosciences,1998,24:183-192.
[112]Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Ensing of environment.2008,112(5):2592-2604.
[113]Darvishzadeh, R., Skidmore, A., Atzberger, C, van Wieren, S. Estimation of vegetation LAI from hyperspectral reflectance data:Effects of soil type and plant architecture, International journal of applied earth observation and geoinformation. 2008,10(3),358-373.
[114]De Roo, A. P. J. Soil Erosion Assessment using G.I.S. in V. P. Singh and M. Fiorentino (eds), Geographical Information Systems in Hydrology, Kluwer Academic Publishers, Water Science and Technology Library, Dordrecht,1996, 26:339-356.
[115]Demarty J., Chevallier F., Friend A. D., Viovy N., Piao S. L., Ciais P. Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model, Geophycial Research Letters,2007,34:15402.
[116]Delegido J., Fernandez G, Gandia S., Moreno J. Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques:application to PROBA/CHRIS data. International of Journal of Remote sensing,2008,29(24):7107-7127
[117]Dinguirard, M.,and Slater,P.N. Calibration of Space-Multispectral Imaging Sensors:A Review. Remote Sensing of Environment,1999,68:194-205.
[118]Dissmeyer, G.E., Foster, G.R. A guide for predicting sheet and rill erosion on forested land. Tech Publ SA-TP 11, USD A Forest Service, West Lafayette,1980, pp: 40.
[119]Dissmeyer, G.E, Foster, GR. Estimating the cover management factor in the USLE for forest conditions. Journal of Soil Water Conservation,1981,36 (4):235-240.
[120]Donald, G, Greet, G, Wouter, S., and Ilse, R. Assessment of USLE cover-management C-factors for 40 crop rotation systems on arable farms in the Kemmelbeek watershed, Belgium. Soil and Tillage Research,2003,74:47-53.
[121]Duan A W. Measuring leaf area index of crop colony. Irrigation and Drainage.1996, 15:50-53.
[122]Dymond,J.R., Stephens,P.R., Newsome,P.F.and Wide,R.H. Percent vegetation cover of a degrading rangeland from SPOT.International Journal of Remote Sensing,1992, 13(11):1999-2007.
[123]Eastwood, J.A., Yates,M.G.,Thomson,A.G, and Fuller,R.M. The reliability of vegetation indices for monitoring saltmarsh vegetation cover. International Journal of Remote Sensing,1997,18(18):3901-3907.
[124]Edward J. H., Scott J. G. Comparison and sensitivity analysis of instruments and radiometric methods for LAI estimation:assessments from a boreal forest site. Agricultural and Forest Meteorology.2004,122:157-174.
[125]Ellision,W.D. Soil Erosion Study Part II:Soil detachment hazard by raindrop splash. Aric. Eng.1947,28:197-201.
[126]Eriksson, H., Eklundh, L., Kuusk, A., Nilson, T. Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI eatimates. Remote sensing of Environment.2006,103:408-418.
[127]Fan W. J., Xu X. R., Liu X. C, Yan B. Y, Cui Y. K. Accurate LAI retrieval method baEd on PROBA/CHRIS data. Hydrology and Earth System Sciences,2010,14: 1499-1507.
[128]Fernandez N., Paruelo J. M., Delibes M. Ecosystem functioning of protected and altered Mediterranean environments:A remote sensing classification in Donana, Spain. Remote Sensing of Environmen.2010,114:211-220.
[129]Fernandes R., Butson C., Leblanc S., Latifovic R. Landsat-5 TM and Landsat-7 ETM+based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data. Canadian Journal of Remote Sensing.2003,29: 241-258
[130]Follett, R.F., Stewart, B.A., Soil erosion and crop productivity. Madison (WT): American Society of Agronomy and Crop Science Society of America.1985.
[131]Fontaine, T. A., Cruickshank, T. S., Aronld, J. G, et al. Development of a snowfall2snowmelt routine for mountainous terrain for the soil water assessment tool (SWAT). Journal of Hydrology,2002,262:209-223.
[132]Foster, G.R., Modeling the erosion process. In:Haan, C.T., Johnson, H.P., Brakensiek, D.L. (Eds.),Hydrologic Modeling of Small Watersheds, ASAE Monograph, vol.5. ASAE (American Society of Agricultural Engineers), St. Joseph, MI,1982,pp:297-380.
[133]Galvao, L. S., Ponzoni, F. J., Epiphanio, J. C. N., Rudorff, B. F. T., Formaggio, A. R. Sun and view angle effects on NDVI determination of land cover types in the Brazilian Amazon region with hyperspectral data. International Journal of Remote sensing.2004,25:1861-1879.
[134]Ganguly S., Schull M. A., Samanta A., Shabanov N. V., Milesi C., Nemani R. R., Knyazikhin Y., Myneni R. B. Generating vegetation leaf area index earth system data record from multiple sensors, Part1:Theory. Remote Sensing of Environment.2008,112:4333-4343.
[135]Gemmell, F., Vajo, J., and Strandstrom, M. Estimating forest cover in a boreal forest test site using thematic mapper data from two dates. Remote Sensing of Environment,2001,77 (2),197-211.
[136]Gitelson, A. A., Kaufman, Y. J., Stark, R., and Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 2002,80(1):76-87.
[137]Goel, N.S. Inversion of canopy reflectance models for estimation of biophysical parameters from reflectance data. In:Asrar, G. (Ed.), Theory and Applications of Optical Remote Ensing.Wiley & Sons, New York, etc.,1989, pp.205-251.
[138]Gonsamo A., Pellikka P. The sensitivity based estimation of leaf area index from spectral vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing.2012,70:15-25.
[139]Govaerts, Y. M., Verstraete, M. M., Pinty, B.,& Gobron, N. Designing optimal spectral indices:A feasibility and proof of concept study. International Journal of Remote Ensing.1999,20,1853-1873.
[140]Gower S. T., Kucharik C. J., Norman J. M. Direct and indirect estimation of leaf area index, fAPAR and net production of terrestrial ecosystem. Remote Sensing of Environment.1999,70:29-51.
[141]Graetz,R.D.,Pech,R.R.,and Davis,A.W. The assessment and monitoring of sparsely vegetated rangelands using calibrated Landsat data.International Journal of Remote Sensing,1988,9:1201-1222.
[142]Gu, Z.J., Zeng, Z.Y., Shi, X.Z., Li, L., Yu, D.S., et al. Assessing factors influencing vegetation coverage calculation with remote sensing imagery.International journal of remote sensing,2009,30(10):2479-2489.
[143]Gu, Z.J., Zeng, Z.Y., Shi, X.Z., Li, L., Weindorf D.C., Zha, Y, Yu, D.S.,et al. A Model for Estimating Total Forest Coverage with Ground-Based Digital Photography, PEDOPHERE.2010,20(3):318-325.
[144]GU Z. J., Shi X. Z., Lin Li, Yu D. S., Liu L. S., Zhang W. T. Using multiple radiometric correction images to estimate leaf area index, International Journal of Remote Ensing,2011,32(24):9441-9454.
[145]Gu Z. J., Ju W. M., Liu Y. B., Li D. Q., Fan W. L. Applicability of spectral and spatial information from IKONOS-2 Imagery in retrieving Leaf Area Index of forests in the urban area of Nanjing, China. Journal of Applied Remote Sensing.2012,6(1):1-14.
[146]Gutman, G, and A. Ignatov. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International journal of remote sensing,1998,19 (8):1533-1543.
[147]Hairsine, P.B., and Rose, C.W. Rainfall detachment and deposition:sediment transport in the absence of flowdriven processes. Soil Science Society of America Journal,1991,55,320-324.
[148]Hassen,M., Fekadu, Y. and Gete, Z. Validation of agricultural non-point source (AGNPS) pollution model in Kori watershed, South Wollo, Ethiopia. International Journal of Applied Earth Observation and Geoinformation,2004,6:97-109.
[149]Hasegawa K., Matsuyama H., Tsuzuki H., Sweda T. Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures. Remote Ensing of Environment,2010,114:514-519
[150]Heiskanen J. Tree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data. Remote Sensing of Environment.2006,103:97-114.
[151]Heiskanen J., Rautiainen M., Korhonen L., Mottus M. Pauline Stenberg, Retrieval boreal forest LAI using a forest reflectance model and empirical regressions, International Journal of Applied Earth Observation and Geoinformation, 2011,13:595-606.
[152]Holscher, D., Mackensen, J.,and Roberts, J. M. Forest recovery in the humid tropics: Changes in vegetation structure, nutrient pools and the hydrological cycle. International. Hydrological Service,2005,598-621.
[153]Houborg R., Soegaard H., Boegh E. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Ensing of Environment.2007,106: 39-58.
[154]Huete, A. R., and Jackson, R. D. Soil and atmosphere influences on the spectra of partial canopies. Remote Sensing of Environment,1988,25(1):89-105.
[155]Huete, A. R., Hua, G., Qi, J., Chehbouni, A., Van Leeuwen, W. J. D. Normalization of multidirectional red and NIR reflectances with the SAVI. Remote sensing of Environment.1992,41,143-154.
[156]Ishii, H.T., Tanabe, S., and Hiura, T.Exploring the relationships among canopy structure, stand productivity, and biodiversity of temperature forest ecosystems. Forest Science,2004,50 (3):342-355.
[157]Jennifer L.R. JenEn, Karen S. Humes, Andrew T. Hudak, Lee A. Vierling, Eric Delmelle, Evaluation of the MODIS LAI product using independent lidar-derived LAI:A caE study in mixed conifer forest. Remote Ensing of Environment,2011, 115:3625-3639.
[158]Jin, Z., Tian, Q., Chen, J.M.,Chen, M. Spatial scaling between leaf area index maps of different resolutions. Journal of Environmental Management, doi:10.1016/ j.jenvman.2006.08.016.
[159]Jin Y., Gao F., Schaaf C. B., Li X., Strahler A. H., Bruegge C. J. Improving MODIS surface BRDF/albedo retrieval with MISR multiangle observation. IEEE Transactions on Geoscience and Remote Sensing.2002,40:1593-1604.
[160]Jordan,C.F. Derivation of leaf area index from quality of light on the forest floor. Ecology,1969,50:663-666.
[161]Josef,K., Tomas, D., Anton, V.R., Jiri,V. and Karel, V. Reservoirs'siltation measurments and sediment transport assessment in the Czech Republic, the Vrchlice catchment study. CATENA,2005,64:348-362.
[162]Kaufman Y. J., Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote sensing.1992,30: 261-270.
[163]Keeling, E.G, Sala, A., and DeLuca,T. H. Effects of fire exclusion on forest structure and composition in unlogged ponderosa pine/Douglas-fir forests. Forest Ecology and Management,2006,237 (1-3):418-428.
[164]Laflen, J.M.,et al. WEPP soil erodiblity experiments for range-land and cropland soils Journal of Soil and Water Conservation,1991,46(1):40-44.
[165]Langbein, W. B.,and Schumn, S. A. Yield of Ediment in relation to mean annual precipitation.Transaction, American Geophysical Union,1958,39:1078-1084.
[166]Langbein, W. B.,and Schumn, S. A. Yield of sediment in relation to mean annual precipitation. Transaction, American Geophysical Union,1958,39:1078-1084.
[167]Leblanc, S. G., Chen, J. M., Cihlar, J. NDVI directionality in boreal forests:A model interpretation of measurements. Canadian Journal of Remote sensing.1997,23, 369-380.
[168]Li, X. B., Chen,Y.H., Yang,H.and Zhang,Y.X. Improvement, comparison, and application of field measurement methods for grassland vegetation fractional overage.Journal of Integrative Plant Biology,2005,47(9):1074-1083.
[169]Liang,S.L. Quantitative remote sensing of land surfaces. John Wiley &Sons, Inc, 2003, pp:560.
[170]Liang, S.L., Fang, H.L., Morisette, J.T., et al. Atmospheric correction of Landsat ETM+land surface imagery. II. Validation and applications.Geoscience and Remote Sensing,2002,40(12):2736-2746.
[171]Lu, H., Raupach, M. R., McVicar, T. R.et al. Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series. Remote Sensing of Environment,2003,86(1):1-18.
[172]Machito, M., Naoyuki, Y, Takashi,U. Application of USLE for the prediction of nutrient losses in soil erosion processes. Paddy and Water Environment,2005, 3(2):111-119.
[173]Marcelo F., Breunig, L. S. G., Antonio R. F., Jose C.N. E., Directional effects on NDVI and LAI retrievals from MODIS:A caE study in Brazil with soybean. International Journal of Applied Earth Observation and Geoinformation,2011, 13:34-42.
[174]Matthew, J., Cohen, Keith D.,et al. Empirical reformulation of the universal soil loss equation for erosion risk assessment in a tropical watershed. Geoderma,2005, 124:235-252.
[175]Mcallister D. M., Valeo C. A. Robust new method for the remote estimation of LAI in montane and boreal forests. International Journal of Remote Sensing.2007,28: 1891-1905.
[176]Mcdaniel, K. C., and Haas, R. H..Assessing mesquite-grass vegetation condition from Landsat. Photogrammetric Engineering and Remote Sensing,1982, 48:441-450.
[177]McGwire,K., Minor,T.and Fenstermaker,L. Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments.Remote Sensing of Environment.2000,72(3):360-374.
[178]Mikhailova E A, Bryant R B, Schwager S J.,et al.Predicting rainfall erosivity in Honduras.Soil Science Society of American Journal,1997,61:273-279.
[179]Minomura,M., Kuze,H., Takeuchi,N. Adjacency effect in the atmospheric correction of satellite remote sensing data:Evaluation of the influence of aerosol extinction profiles. Optical review,2001,8 (2):133-141.
[180]Mishra, S.K., J.V. Tyagi, V.P. Singh and Ranvir Singh. SCS-CN-based modeling of sediment yield. Journal of Hydrology,2005,15:352-357.
[181]Moehansyah, Maheshwari, H. B. L.,and Armstrong, J. Field Evaluation of Selected Soil Erosion Models for Catchment Management in Indonesia. Biosystems Engineering,2004,88:491-506.
[182]Moore, I. D., Wilson, J. P. Length-slope factors for the revised universal soil loss equation:simplified method of estimation. Journal of Soil and Water Conservation, 1992,47(5),423-428.
[183]Morgan R.P.C., Soil erosion and conservation. London:Longman, Essex.,1995.145 Morgan, R.P.C., Quinton, J.N., Smith, R.E., Govers, G, Poesen, J.W.A., Auerswald, K., Chisci, G., Torri,D., and Styczen, M.E. The European soil erosion model (EUROSEM):a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surface Processes and Landforms,1998,23:527-544.
[184]Nolin A. W. Towards retrieval of forest cover density over snow from the Multi-angle Imaging SpectroRadiometer (MISR). Hydrological Processes.2004,18: 3623-3636.
[185]North,P.R.J. Estimation of f(APAR), LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sensing of Environment,2002,80:114-121.
[186]Okan, F., and Nilgun, B. H. Integration of GIS with USLE in Assessment of Soil Erosion. Water Resources Management,2002,16(6):447-467.
[187]Pearson, R. L.,& Miller, L. D. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado.In Proceedings of the 8th International symposium on Remote Ensing of the Environment.1972, vol. II(pp.1355-1379)(Ann Arbor, MI).
[188]Pinty B., Widlowski J. L., Gobron N., Verstraete M. M., Diner D. J. Uniqueness of multiangular measurements-Part Ⅰ:An indicator of subpixel surface heterogeneity from MISR. IEEE Transactions on Geoscience and Remote Sensing.2002,40: 1560-1573.
[189]Pocewicz A., Lee A. Vierling, Leigh B. Lentile, Smith R. View angle effects on relationships between MISR vegetation indices and leaf area index in a recently burned ponderosa pine forest. Remote sensing of Environment,2007,107:322-333
[190]Qi, J., Chenbouni, A., Huete, A. R., Kerr,Y.H. and Soroonshian,S.A modified soil adjusted vegetation index (MSAVI). Remote sensing of environment,1994,48 (2): 119-126.
[191]Qi, J., Moran, M. S., Cabot, F., Dedieu, G. Normalization of sun/view angle effects using spectral albedo-baEd vegetation indices. Remote sensing of Environment. 1995,52:207-217.
[192]Rautiainen M., Stenberg P., Nilson T., Kuusk A. The effect of crown shape on the reflectance of coniferous stands. Remote sensing of Environment.2004,89:41-52.
[193]Rautiainen M., Retrieval of leaf area index for a coniferous forest by inverting a forest reflectance model. Remote sensing of Environment,2005,99:295-303.
[194]Rautiainen, M., Lang, M., Mottus, M., Kuusk, A., Nilson, T., Kuusk, J., Lukk, T. Multi-angular reflectance properties of a hemiboreal forest:An analysis using CHRIS PROBA data. Remote sensing of environment.2008,112(5),2627-2642.
[195]Renard,K. G, Foster, G.R., Weesies,G.A., Porter,J. P. RUSLE revised universal soil loss equation. Journal of Soil and Water Conservation.1991,46(1):30-33.
[196]Renard,K G, Foster, G. R., Weesies, G. A., et al. Predicting soil erosion by water:a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Handbook No.703. U. S, Department of Agriculture, Washington D. C.,1997.
[197]Richardson, A. J.,& Wiegand, C. L. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote sensing,1977,43, 1541-1552.
[198]Ringrose, S., Matheson, W., Wolski, P., and Huntsman-Mapila, P. Vegetation cover trends along the Botswana Kalahari transect. Journal of Arid Environment,2003,54:297-317.
[199]Rouse, J.W.,and Haas, R.H. Monitoring vegetation systems in the great plain with ERTS. Third ERTS Symposium, Washington, DC,1973:309-317.
[200]Rouse J. W., Haas R. H., Schell J. A., Deering D. W., Harlan J. C. Monitoring the vernal advancement of retrogradation of natural vegetation. Greenbelt, MD: NASA/GSFC (Type III, Final Report).1974, p.371.
[201]Roue, J. W., Haas, R. H., Schell, J. A., Deering, D. W.,& Harlan, J. C. Monitoring the vernal advancement of retrogradation of natural vagetation (p.371).1974, Greenbelt, MD:NASA/GSFC (Type III, Final Report).
[202]Running, S.W., Nemani, R., Peterson, D.L., Band, L.E., Potts, D.F., Pierce, L.L., Spanner, M.A. Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation. Ecology.1989,70(4),1090-1101.
[203]Saro, L. Soil erosion assessment and its verification using the Universal Soil Loss Equation and Geographic Information System:a case study at Boun, Korea. Environmental Geology,2004,45(4):457-465.
[204]Schadlich, S., Gottsche,F.M., and Olesen, F.S. Influence of land surface parameters and atmosphere on METEOSAT brightness temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sensing of Environment,2001,75 (1):39-46.
[205]Schlerf, M., Atzberger, C., Hill, J. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote sensing of Environment.2005,95 (2),177-194.
[206]Sharpley, A.N., Williams, J.R. (Eds.). EPIC-Erosion Productivity Impact Calculator: 1. Model Documentation. US Department of Agriculture Technical Bulletin No. 1768. US Department of Agriculture, Temple, TX,1990.
[207]Simon, W., Jonathan, L., Gordon, H. An evaluation of grid size uncertainty in empirical soil loss modeling with digital elevation models. Environmental Modeling and Assessment,2005,10(1):33-42.
[208]Smith B., Knorr W., Widlowski J. L., Pinty B., Gobron N. Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances. Forest Ecology and Management.2008,255:3985-3994.
[209]Soudani,K., Francois,C., le Maire,G, Le Dantec,V., and Dufrene,E. Comparative analysis of IKONOS, SPOT, and ETM+data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment,2006,102:161-175.
[210]Spanner, M. A.,et al. Soil Loss Prediction in A Geographic Information System Format. In Proc of the Inter.Sym.on Remote Sensing of Environment,1983.
[211]Stedinick, J. D.Monitoring the effect of timber harvest on annual water yield. Journal of Hydrology,1996,176:79-95.
[212]Steven,D. W., Helena M., Matthew G. H., Sheldon L., Felib Y. I., Thomas, S. R., and Gary,M. S. Validation of a 3-D enhancement of the Universal Soil Loss Equation for prediction of soil erosion and sediment deposition. CATENA,2005, 64(30):281-296.
[213]Suleyman Ozhan, A. Nihat Balci, Necdet Ozyuvaci, Ahmet Hizal, Ferhat Gokbulak and Yusuf Serengil. Cover and management factors for the Universal Soil-Loss Equation for forest ecosystems in the Marmara region, Turkey. Forest Ecology and Management,2005,214(3):118-123.
[214]Sun,P. S., Liu, S. R. Large scale eco-hydrological model and its integration with GIS. Acta Ecologica Sinica,2003,23(10):2115-2124.
[215]Thomas, D.B., Erikson, A., Grunder, M., Mburu, J.K. Soil and Water Conservation Manual for Kenya. Soil and Water Conservation Branch, Ministry of Agriculture. Livestock Development and Marketing, Republic of Kenya,1997.
[216]Tickle,P. K., Lee, A., Lucas, R. M., Austin, J., and Witte,C. Quantifying Australian forest floristics and structure using small footprint LiDAR and large scale aerial photography. Forest Ecology and Management,2006,223(1-3):379-394.
[217]Van Rompaey, A.J.J., Verstraeten, G.,Van Oost K.,Govers, G,Poesen, J. Modelling Mean Annual Sediment Yield Using a Distributed Approach, Earth Surface Processes and Landforms,2001,26:1221-1236.
[218]Verrelst, J. Schaepman, M. E., Koetz, B. Kneubuhler, M. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote sensing of environment.2008,112(5),2341-2353.
[219]Vina, A.,& Gitelson, A. A. New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophysical Research Letters.2005,32,(17) L17403.
[220]Vohland M., Mader S., Dorigo W. Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT+SAIL. International Journal of Applied Earth ObErvation and Geoinformation,2010,12:71-80.
[221]Vuolo F., Dini L., Durso G. Retrieval of leaf area index from CHRIS/PROBA data: an analysis of the directional and spectral information content. International Journal of Remote sensing,2008,29(17):5063-5072
[222]Watson,F.GR.,Grayson,R. B.,Vertessy, R. A.Large scale distribution modeling and the utility of detailed ground data,Hydrological Processes,1998,12:873-888.
[223]Warren B. Cohen, Thomas K. Maiersperger, Stith T. Gower, David P. Turner. An improved strategy for regression of biophysical variables and Landsat ETM+data. Remote Ensing of Environment,2003,84:561-571.
[224]Wischmeier, W. H., Johnson, C. B.,Cross, B.V. A soil erodibility monograph for farmland construction sites. Journal of Soil and Water Conservation.1971,26: 189-193.
[225]White, M. A., Asner, G. P., Nemani, R. R., Privette, J. L.,& Running, S. W. Measuring fractional cover and leaf area index in arid ecosystem:Digital camera, radiation transmittance, and laser altimetry methods. Remote Sensing of Environment,2000,74(1):45-57.
[226]Williams, J. R. Sediment yield predictions with Univeral Equation using runoff energy factor. In:Present and prospective Technoligy for predicting sediment yields and sources USDA-ARS-5-40. Agricultural. Research Service, USDA. Washington D. C.1975,pp:244-252.
[227]Williams, J.R., Renard,K. G, Dyke,P.T. EPIC, a new method for assessing erosion's effect on soil productivity. Journal of Soil and Water Conservation,1983,38(5):381-383.
[228]Wischmeier,W.H.,Smith et al. Predicting rainfall erosion losses-A guide to Conservation planning, Agricultrual Handbook 537, USDA,Washington, D.C.,1978. pp.1-58.
[229]Wischemeier, W. H.,Smith, D. D.Rainfall energy and its relationship to soil loss. Transactions, American Geophysical Union,1958,39:285-291.
[230]Wittich,K.P.,and Hansing,O.Area-averaged vegetative cover fraction estimated from satellite data.International Journal of Biometerology,l995,38 (3):209-215.
[231]Xiao,J.F.and Moody,A.A. comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA.Remote Sensing of Environment.2005,98 (2-3):237-250.
[232]Yao Y. J., Liu Q. H., Liu Q., Li X. W. LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages. Remote Ensing of Environment, 2008,112:94-106.
[233]Young, R.A., Onstad, C.A., Bosch, D.D., Anderson, W.P., AGNPS:A nonpoint-source pollution model for evaluating agricultural watersheds. Journal of Soil and Water Conservation,1989,44 (2):4522-4561.
[234]Zeng, Z. Y. A new method of data transformation for satellite images:II. Transformation equations for SPOT, NOAA, IKONOS, QuickBird, ASTER, MSS and other images and application. International Journal of Remote Sensing,2007,28 (18):4125-4155.
[235]Zhang Z. M., He G. J., Wang X. Q., Jiang H. Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery. International Journal of Remote sensing,2011,32(19):5365-5379.
[236]Zhang X., Walling D.E., Quine T.A., Wen A., Use of reservoir deposits and caesium-137 measurements to investigate the erisonal response of a small drainage basin in the rolling loess plateau region of China. Land Degrad Devel,1997,8:1-16.
[237]Zhou,Q., and Robson,M. Automated rangeland vegetation cover and density estimation using ground digital images and a spectral-contextual classifier. Remote Sensing,2001,22(17):3457-3470.
[238]Zhu, J. J., Matsuzaki, I.,and Gonda, Y. Optical stratification porosity as a measure of vertical canopy structure in a Japanese coastal forest. Forest Ecology and Management,2003,173(1-3):89-104.
[2]卜兆宏,孙金庄,周伏键,唐万龙.水土流失定量遥感方法及其应用.土壤学报,1997,34(3)：235-244.
[3]卜兆宏,赵宏夫,刘绍清,陈明华.用于土壤流失量遥感监测的植被因子算法的初步研究.遥感技术与应用,1993,8(4)：16-22.
[4]蔡强国,刘纪根.关于我国土壤侵蚀模型研究进展.地理科学进展,2003,22(3)：242-250.
[5]陈冬林.红壤坡地不同森林恢复类型水土保持效应及其机制研究.湖南农业大学硕士学位论文,2004.
[6]江洪.长汀县水土流失遥感监测及其生态安全评价.福州大学硕士学位论文,2005
[7]陈芳,周志翔,王鹏程,李海防,钟颖飞.武汉钢铁公司厂区绿地绿量的定量研究.应用生态学报,2006,17(4)：592-596.
[8]陈引珍.三峡库区森林植被水源涵养及其保土功能研究.北京林业大学博士学位论文,2007.
[9]陈志清.福建省长汀县河田镇的水蚀漠化及其治理.地理科学进展.1998,17(2)：65-70.
[10]丁军.红壤丘陵区人工林地生态学特征及其水土保持机理研究.浙江大学博士学位论文,2003.
[11]傅集贤,龙俊和,彭文海.湘中丘陵花岗岩侵蚀劣地水土保持林效益研究.水土保持通报,1998,18(6)：49-53.
[12]高兆蔚.福建长汀河田水土流失综合治理成效与继续治理对策建议.福建水土保持,2004,16(4)：11-13.
[13]郭忠升.水土保持林有效覆盖率及其确定方法的研究.土壤侵蚀与水土保持学报,1996,2(3)：67-71.
[14]郭忠升.最佳森林覆盖率的初步研究.西北林学院学报,1998,13(3)：23-27.
[15]韩冰,吴钦孝,刘向东.林地枯枝落叶层对溅蚀影响的研究.防护林科技,1994, (2)：7-10.
[16]胡江波.不同植被恢复模式的水土保持效果及土壤水肥生态效应研究.西北农林科技大学硕士学位论文,2007.
[17]胡良军,李锐,杨勤科.基于RS和GIS的区域水土流失快速定量评价方法.水土保持通报,2000,20(6)：42-44.
[18]黄秉维.再谈森林的作用(四).地理知识,1982,4(2)：11-18.
[19]惠凤鸣,田庆久,金震宇,李海涛.植被指数与叶面积指数关系研究及定量化分析.遥感信息,2003,2：10-12.
[20]蒋定生.黄土高原水土流失与治理模式.北京：中国水利水电出版社.1997,(4)：8-26.
[21]蒋芳市.不同治理措施对侵蚀退化生态系统恢复的影响.福建农林大学硕士学位论文,2007.
[22]康冰.广西大青山退化森林植被特征及生态恢复研究.西北农林科技大学博士学位论文,2007.
[23]宋永昌.植被生态学.上海：华东师范大学出版社,2001,598-599.
[24]雷环清.兴国县花岗岩区林下水土流失及其防治.中国水土保持,2007,3：58-59.
[25]雷瑞德.华山松林冠对降雨动能的影响.见：林业部科技司编,森林生态系统定位研究.哈尔滨：东北林业大学出版社,1994.
[26]雷廷武,张晴雯,姚春梅等.WEPP模型中细沟可蚀性参数估计方法误差的理论分析.农业工程学报,2005,21(1)：9-12.
[27]李锐,杨勤科,温仲明等.区域土地利用变化环境效应研究综述.水土保持通报,2002,22(2)：65-70.
[28]李文华,何永涛,杨丽锡.森林对径流影响研究的回顾和展望.自然资源学报,2001,11(5)：390-406.
[29]李先琨,黄玉清,苏宗明,冯玲,叶惊春,覃德深.广西水土流失重点区域生态恢复试验研究.水土保持通报,1997,17(6)：1-6.
[30]廖善刚,赵昭蛹.长汀河田风化花岗岩坡面水土流失规律和预测方程.福建师范大学学报(自然科学版),1990,6(1)：105-109.
[31]刘宝元,谢云,张科利.土壤侵蚀预报模型.中国科学技术出版社,北京。2001.
[32]刘秉正,吴发启.土壤侵蚀.西安：陕西人民出版社,1997,23-35.
[33]刘定辉,李勇.植物根系提高土壤抗侵蚀性机理研究.水土保持学报,2003,17(3)：34-37.
[34]刘光崧.土壤理化分析与剖面描述.中国标准出版社,1996,(6)：PP：266.
[35]刘国彬.黄土高原草地土壤抗冲性及其机理研究.水土保持学报,1998(1)：93-96.
[36]刘洪生.生态修复在长汀水土流失治理的几种应用模式分析.亚热带水土保持,2005,17(3)：31-33.
[37]刘伦辉,刘文耀.滇中山地主要植被群落水土保持效益比较.水土保持学报,1990,4(1)：36-42.
[38]刘文耀,刘伦辉,郑征等.滇中常绿阔叶林及云南松林水文作用的初步研究.植物生态学与地植物学学报,1991,15(2)：159-167.
[39]刘向东,吴钦孝,赵鸿雁.森林植被垂直截留作用与水土保持.水土保持研究,1994,1(3)：8-13.
[40]刘旭东.森林环境资源动态监测指标及信息系统框架.福建农林大学硕士学位论文,2005.
[41]刘志韬.山西管涔山林区森林对径流的影响.水土保持通报,1981,1(4)：56-61.
[42]吕妙儿,蒲英霞,黄杏元.城市绿地监测遥感应用.中国园林,2000,16(5)：41-44.
[43]潘成忠,上官周平.黄土区次降雨条件下林地径流和侵蚀产沙形成机制-以人工油松林和次生山杨林为例.应用生态学报,2005,16(9)：1597-1602.
[44]秦富仓.黄土地区流域森林植被格局对侵蚀产沙过程的调控研究.北京林业大学博士学位论文,2006.
[45]邱雪红,杨洁.江西省林地水土流失状况调查及对策.亚热带水土保持,2007,19(4)：35-37.
[46]Richard Lee,张建列译.森林水文学.哈尔滨：东北林业大学出版社,1984.
[47]邵云.沙棘人工林水土保持效益分析.辽宁林业科技,1995,(4)：62-64.
[48]沈中原.坡面植被格局对水土流失影响的实验研究.西安理工大学硕士学位论文,2006.
[49]史德明.如何正确理解有关水土保持术语的讨论.土壤侵蚀与水土保持学报.1998,4(4)：89-91.
[50]史德明.土壤侵蚀对生态环境的影响及其防治对策.水土保持学报,1991,5(3)：1-8.
[51]史德明,史学正,梁音.我国不同空间尺度土壤侵蚀的动态变化.水土保持通报,2005,25(5)：85-89.
[52]史学正,梁音,于东升.“土壤水库”的合理调用与防洪减灾.土壤侵蚀与水土保持学报,1999,5(3)：6-10.
[53]史学正,于东升,邢廷炎.用田间实测法研究我国亚热带土壤的可蚀性K值.土壤学报.1997,34(4)：399-405.
[54]王礼先,解明曙.山地防护林水土保持水文生态效益及其信息系统.北京：中国林业出版社,1997.
[55]王礼先,张志强.森林植被变化的水文生态效益研究进展.世界林业研究,1998,(6)：14-23.
[56]王希群,马履一,贾忠奎,徐程扬.叶面积指数的研究和应用进展.生态学杂志,2005,24(5)：537-541.
[57]王信理.在作物干物质积累的动态模拟中如何合理运用Logistic方程.农业气象,1986,7(1)：14-19.
[58]王震洪,张玲,段昌群,肖林,王克勤,梁国江,张世彪,起连春.滇中三种典型人工林群落结构与土壤侵蚀关系研究.水土保持研究,2001,8(2)：74-79.
[59]魏霞.福建长汀重建生态系统马尾松叶片气体交换与叶绿素荧光特征研究.福建师范大学植物学硕士学位论文,2007.
[60]吴钦孝,刘向东,苏宁虎.山杨次生林枯枝落叶蓄积量及其水文作用.水土保持学报,1992,6(1)：71-76.
[61]谢锦升.植被恢复对退化红壤易变碳及土壤呼吸的影响.福建农林大学博士学位论文,2005：27-29.
[62]杨大三.鄂西三峡库区防护林研究.武汉：湖北科学技术出版社,1996.
[63]于东升,史学正,王宁.用人工模拟降雨研究亚热带坡耕地土壤的沟蚀和沟间侵蚀.土壤学报,2001,38(2)：160-166.
[64]余坤勇,刘健,赖玫妃,庞闽志,陈振平.基于防止土壤侵蚀为目标富屯溪最佳森林覆盖率的确定.江西农业大学学报,2007,29(3)：398-403.
[65]喻荣岗,左长清,杨洁,谢颂华,李小强,王昭艳.红壤侵蚀区几种水土保持林水文效应研究.水土保持通报,2007,27(6)：194-198.
[66]余作岳,彭少麟.热带亚热带退化生态系统植被恢复生态学研究.广州；广东科技出版社,1996.
[67]袁东海.红壤小流域水土流失规律及防治措施的研究.浙江大学博士学位论文,2001.
[68]曾河水,岳辉.长汀县以河田为中心的花岗岩强度水土流失区植被重建的主要模式.福建水土保持,2004,16(4)：16-18.
[69]曾思齐,佘济云.马尾松水土保持林水文功能计量研究：Ⅰ.林冠截留与土壤贮水能力.中南林学院学报,1996,16(3)：1-8.
[70]曾志远.卫星遥感图像计算机分类与地学应用研究.北京：科学出版社,2004.
[71]张保华,王喜,刘子亭,何毓蓉.贡嘎山天然林和盐亭人工林土壤侵蚀研究.中国水土保持科学,2006.
[72]张颖.黄土地区森林植被对坡面土壤侵蚀过程影响机理研究.北京林业大学博士学位论文,2007.
[73]赵鸿雁,吴钦孝,陈云明.黄土高原不同处理人工油松林地水土流失研究.西北农林科技大学学报,2002,30(6)：171-173.
[74]赵良平.燕山山地森林植被恢复与重建理论和技术研究.南京林业大学博士学位论文,2007.
[75]赵其国.我国红壤的退化问题.土壤,1995,27(6)：281-286.
[76]赵其国.我国南方当前水土流失与生态安全中值得重视的问题.水土保持通报,2006,26(2)：1-8.
[77]郑粉莉,王占礼,杨勤科.我国水蚀预报模型研究的现状、挑战与任务.中国水土保持科学,2005,3(1)：7-14.
[78]郑华,欧阳志云,王效科,苗鸿,赵同谦,彭廷柏.不同森林恢复类型对南方红壤侵蚀区土壤质量的影响.生态学报,2004,24(9)：1994-2002.
[79]郑怀舟.福建长汀重建生态系统植物树干液流特征研究.福建师范大学硕士学位论文,2007.
[80]郑巍伟.三峡库区森林和土壤侵蚀关系研究.防护林科技,2006,6：45-47.
[81]郑巍伟,李白勤.三峡库区秭归县森林与土壤侵蚀的相关性研究.西部林业科学,2006,35(3)：62-67.
[82]中野秀章,李云森译.森林水文学.北京：中国林业出版社,1983.
[83]周坚华,孙天纵.三维绿色生物量的遥感模式研究与绿化环境效益估算.环境遥感,1995,10(3)：162-174.
[84]周跃,付玉彬,徐清艳,曾镇涛.林草植被土壤侵蚀控制作用的初步研究—以云南新平大红山铁矿为例.昆明理工大学学报(理工版),2005,30(6)：68-72.
[85]周跃,李宏伟,徐强.云南松林的林冠对十壤侵蚀的影响.山地学报,1999,17(4)：324-328.
[86]周跃,李玉辉,Watts D. Potential of Yunnan pine forest on soil erosion control in the Hotiaoxia Gorge area, SW China.植物生态学报,2000,24(1)：74-81.
[87]周跃,Watts David.高山峡谷区云南松林土壤侵蚀控制的水文效应.土壤侵蚀与水土保持学报,1998,4(3)：31-38.
[88]朱显漠.黄土地区植被因素对水土流失的影响.土壤学报,1960,8(2)：110-121.
[89]朱显漠.黄土高原脱贫致富之道——二论黄土高原的国土整治.水土保持学报,1998(4)：1-5.
[90]朱显漠,田积莹.强化黄土高原土壤渗透性及抗冲性的研究.水土保持学报,1993,7(3)：1-10.
[91]曾河水,岳辉.长汀县以河田为中心的花岗岩强度水土流失区植被重建的主要模式.福建水土保持,2004,16(4)：16-18.
[92]Abdou,W.A., Pilorz,S.H., Helmlinger,.M.C., Conel,J.E., Diner,D.J.,and Bruegge,C.J.,et al. Sua Pan surface bidirectional reflectance:A case study to evaluate the effect of atmospheric correction on the surface products of the Multi-angle Imaging SpectroRadiometer (MISR) during SAFARI 2000. IEEE Transactions on geoscience and remote sensing,2006,44(7):1699-1706.
[93]Andres Vina, A., Gitelson A., Anthony L. Nguy-Robertson, Yi P. Comparison of different vegetation indices for the remote assessment of green leaf area index of crop. Remote sensing of Environment,2011,115:3468-3478.
[94]Anatoly,A.G,Yoram,J.K., Robert, S.,and Don,R. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment.2002,80(1): 76-87.
[95]Baret F, Guyot G Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment.1991,35:161-173
[96]Beasley, D.B., Huggins, L.F., and Monke, E.J. ANSWERS:a model for watershed planning. Transactions of the ASAE,1980,938-944.
[97]Bonan G B. Land-atmosphere interactions for climate system models:coupling biophysical, biogeochemical, and ecosystem dynamical processes. Remote Sensing of Environment.1995,51(1):57-73.
[98]Bornnann, F.H., and Likens, G E. Pattern and Processes in a freseted ecosystem. Springer-Verlag New York,1979.
[99]Broge, N.H., MortenEn, J.V.,2002. Deriving green crop area index and canopy chlorophyll density of winter wheat from spectral reflectance data. Remote Ensing of Environment 81 (1),45-57
[100]Canisius, F., Chen, J. Retrieving forest background reflectance in a boreal region from Multi-angle Imaging SpectroRadiometer(MISR) data. Remote Ensing of Environment.2007,107:312-321
[101]Carlson, T. N., Perry, E. M., and Schmugge,T. J. Remote estimation of soil moisture availability and fractional vegetation cover for agricultural fields. Agricultural and Forest Meteorology,1990,52:45-69.
[102]Chao,Y.L., Wen,T.L., and Wen,C.C. Soil erosion prediction and sediment yield estimation:the Taiwan experience. Soil and Tillage Research,2002,68:143-152.
[103]Chen, J.M.,and Black,T.A.Defining leaf area index for non-flat leaves.Plant Cell and Environment,1992,15:421-429.
[104]Chen, J.M., Cihlar, J. Retrieving Leaf Area Index of Boreal Conifer Forests Using Landsat TM images. Remote Sensing of Environment,1996,55:153-162.
[105]Chen, J.M., Pavlic, G, Brown, L., Cihlar, J., Leblanc, S.G, White, H.P., Hall, R.J., Peddle, D.R., King, D.J., Trofymow, J.A. Derivation and validation of Canada-wide coarE-resolution leaf area index maps using high-resolution satellite imagery and ground measurements. Remote sensing of Environment,2002,80(1):165-184.
[106]Chen,Q. Multisensor comparisons for validation of MODIS vegetation indices. Pedosphere,2006,16 (3):362-370.
[107]Chen, X.X., Lee V., Eric,R.and Thomas,D. Using lidar and effective LAI data to evaluate IKONOS and Landsat 7 ETM+vegetation cover estimates in a ponderosa pine forest.Remote Sensing of Environment,2004,91:14-26.
[108]Cohen,W.B., Maiersperger, T.K., Gower, S.T., Turner, D.P. An improved strategy for regression of biophysical variables and Landsat ETM+data. Remote sensing of Environment.2003,84 (4),561-571.
[109]Colombo R., Bellingeri D., Fasolini D., Carlo M. Marino, Retrieval of leaf area index in different vegetation types using high resolution satellite data. Remote Ensing of Environment,2003,86:120-131.
[110]Conghe,S. Spectral mixture analysis for subpixel vegetation fractions in the urban environment:How to incorporate endmember variability.Remote Sensing of Environment.2005,95:248-263.
[111]Darcy,K. M. and Pierre, Y. J. Estimation of upland erosion using GIS. Computers & Geosciences,1998,24:183-192.
[112]Darvishzadeh, R., Skidmore, A., Schlerf, M., Atzberger, C. Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Ensing of environment.2008,112(5):2592-2604.
[113]Darvishzadeh, R., Skidmore, A., Atzberger, C, van Wieren, S. Estimation of vegetation LAI from hyperspectral reflectance data:Effects of soil type and plant architecture, International journal of applied earth observation and geoinformation. 2008,10(3),358-373.
[114]De Roo, A. P. J. Soil Erosion Assessment using G.I.S. in V. P. Singh and M. Fiorentino (eds), Geographical Information Systems in Hydrology, Kluwer Academic Publishers, Water Science and Technology Library, Dordrecht,1996, 26:339-356.
[115]Demarty J., Chevallier F., Friend A. D., Viovy N., Piao S. L., Ciais P. Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model, Geophycial Research Letters,2007,34:15402.
[116]Delegido J., Fernandez G, Gandia S., Moreno J. Retrieval of chlorophyll content and LAI of crops using hyperspectral techniques:application to PROBA/CHRIS data. International of Journal of Remote sensing,2008,29(24):7107-7127
[117]Dinguirard, M.,and Slater,P.N. Calibration of Space-Multispectral Imaging Sensors:A Review. Remote Sensing of Environment,1999,68:194-205.
[118]Dissmeyer, G.E., Foster, G.R. A guide for predicting sheet and rill erosion on forested land. Tech Publ SA-TP 11, USD A Forest Service, West Lafayette,1980, pp: 40.
[119]Dissmeyer, G.E, Foster, GR. Estimating the cover management factor in the USLE for forest conditions. Journal of Soil Water Conservation,1981,36 (4):235-240.
[120]Donald, G, Greet, G, Wouter, S., and Ilse, R. Assessment of USLE cover-management C-factors for 40 crop rotation systems on arable farms in the Kemmelbeek watershed, Belgium. Soil and Tillage Research,2003,74:47-53.
[121]Duan A W. Measuring leaf area index of crop colony. Irrigation and Drainage.1996, 15:50-53.
[122]Dymond,J.R., Stephens,P.R., Newsome,P.F.and Wide,R.H. Percent vegetation cover of a degrading rangeland from SPOT.International Journal of Remote Sensing,1992, 13(11):1999-2007.
[123]Eastwood, J.A., Yates,M.G.,Thomson,A.G, and Fuller,R.M. The reliability of vegetation indices for monitoring saltmarsh vegetation cover. International Journal of Remote Sensing,1997,18(18):3901-3907.
[124]Edward J. H., Scott J. G. Comparison and sensitivity analysis of instruments and radiometric methods for LAI estimation:assessments from a boreal forest site. Agricultural and Forest Meteorology.2004,122:157-174.
[125]Ellision,W.D. Soil Erosion Study Part II:Soil detachment hazard by raindrop splash. Aric. Eng.1947,28:197-201.
[126]Eriksson, H., Eklundh, L., Kuusk, A., Nilson, T. Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI eatimates. Remote sensing of Environment.2006,103:408-418.
[127]Fan W. J., Xu X. R., Liu X. C, Yan B. Y, Cui Y. K. Accurate LAI retrieval method baEd on PROBA/CHRIS data. Hydrology and Earth System Sciences,2010,14: 1499-1507.
[128]Fernandez N., Paruelo J. M., Delibes M. Ecosystem functioning of protected and altered Mediterranean environments:A remote sensing classification in Donana, Spain. Remote Sensing of Environmen.2010,114:211-220.
[129]Fernandes R., Butson C., Leblanc S., Latifovic R. Landsat-5 TM and Landsat-7 ETM+based accuracy assessment of leaf area index products for Canada derived from SPOT-4 VEGETATION data. Canadian Journal of Remote Sensing.2003,29: 241-258
[130]Follett, R.F., Stewart, B.A., Soil erosion and crop productivity. Madison (WT): American Society of Agronomy and Crop Science Society of America.1985.
[131]Fontaine, T. A., Cruickshank, T. S., Aronld, J. G, et al. Development of a snowfall2snowmelt routine for mountainous terrain for the soil water assessment tool (SWAT). Journal of Hydrology,2002,262:209-223.
[132]Foster, G.R., Modeling the erosion process. In:Haan, C.T., Johnson, H.P., Brakensiek, D.L. (Eds.),Hydrologic Modeling of Small Watersheds, ASAE Monograph, vol.5. ASAE (American Society of Agricultural Engineers), St. Joseph, MI,1982,pp:297-380.
[133]Galvao, L. S., Ponzoni, F. J., Epiphanio, J. C. N., Rudorff, B. F. T., Formaggio, A. R. Sun and view angle effects on NDVI determination of land cover types in the Brazilian Amazon region with hyperspectral data. International Journal of Remote sensing.2004,25:1861-1879.
[134]Ganguly S., Schull M. A., Samanta A., Shabanov N. V., Milesi C., Nemani R. R., Knyazikhin Y., Myneni R. B. Generating vegetation leaf area index earth system data record from multiple sensors, Part1:Theory. Remote Sensing of Environment.2008,112:4333-4343.
[135]Gemmell, F., Vajo, J., and Strandstrom, M. Estimating forest cover in a boreal forest test site using thematic mapper data from two dates. Remote Sensing of Environment,2001,77 (2),197-211.
[136]Gitelson, A. A., Kaufman, Y. J., Stark, R., and Rundquist, D. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 2002,80(1):76-87.
[137]Goel, N.S. Inversion of canopy reflectance models for estimation of biophysical parameters from reflectance data. In:Asrar, G. (Ed.), Theory and Applications of Optical Remote Ensing.Wiley & Sons, New York, etc.,1989, pp.205-251.
[138]Gonsamo A., Pellikka P. The sensitivity based estimation of leaf area index from spectral vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing.2012,70:15-25.
[139]Govaerts, Y. M., Verstraete, M. M., Pinty, B.,& Gobron, N. Designing optimal spectral indices:A feasibility and proof of concept study. International Journal of Remote Ensing.1999,20,1853-1873.
[140]Gower S. T., Kucharik C. J., Norman J. M. Direct and indirect estimation of leaf area index, fAPAR and net production of terrestrial ecosystem. Remote Sensing of Environment.1999,70:29-51.
[141]Graetz,R.D.,Pech,R.R.,and Davis,A.W. The assessment and monitoring of sparsely vegetated rangelands using calibrated Landsat data.International Journal of Remote Sensing,1988,9:1201-1222.
[142]Gu, Z.J., Zeng, Z.Y., Shi, X.Z., Li, L., Yu, D.S., et al. Assessing factors influencing vegetation coverage calculation with remote sensing imagery.International journal of remote sensing,2009,30(10):2479-2489.
[143]Gu, Z.J., Zeng, Z.Y., Shi, X.Z., Li, L., Weindorf D.C., Zha, Y, Yu, D.S.,et al. A Model for Estimating Total Forest Coverage with Ground-Based Digital Photography, PEDOPHERE.2010,20(3):318-325.
[144]GU Z. J., Shi X. Z., Lin Li, Yu D. S., Liu L. S., Zhang W. T. Using multiple radiometric correction images to estimate leaf area index, International Journal of Remote Ensing,2011,32(24):9441-9454.
[145]Gu Z. J., Ju W. M., Liu Y. B., Li D. Q., Fan W. L. Applicability of spectral and spatial information from IKONOS-2 Imagery in retrieving Leaf Area Index of forests in the urban area of Nanjing, China. Journal of Applied Remote Sensing.2012,6(1):1-14.
[146]Gutman, G, and A. Ignatov. The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International journal of remote sensing,1998,19 (8):1533-1543.
[147]Hairsine, P.B., and Rose, C.W. Rainfall detachment and deposition:sediment transport in the absence of flowdriven processes. Soil Science Society of America Journal,1991,55,320-324.
[148]Hassen,M., Fekadu, Y. and Gete, Z. Validation of agricultural non-point source (AGNPS) pollution model in Kori watershed, South Wollo, Ethiopia. International Journal of Applied Earth Observation and Geoinformation,2004,6:97-109.
[149]Hasegawa K., Matsuyama H., Tsuzuki H., Sweda T. Improving the estimation of leaf area index by using remotely sensed NDVI with BRDF signatures. Remote Ensing of Environment,2010,114:514-519
[150]Heiskanen J. Tree cover and height estimation in the Fennoscandian tundra-taiga transition zone using multiangular MISR data. Remote Sensing of Environment.2006,103:97-114.
[151]Heiskanen J., Rautiainen M., Korhonen L., Mottus M. Pauline Stenberg, Retrieval boreal forest LAI using a forest reflectance model and empirical regressions, International Journal of Applied Earth Observation and Geoinformation, 2011,13:595-606.
[152]Holscher, D., Mackensen, J.,and Roberts, J. M. Forest recovery in the humid tropics: Changes in vegetation structure, nutrient pools and the hydrological cycle. International. Hydrological Service,2005,598-621.
[153]Houborg R., Soegaard H., Boegh E. Combining vegetation index and model inversion methods for the extraction of key vegetation biophysical parameters using Terra and Aqua MODIS reflectance data. Remote Ensing of Environment.2007,106: 39-58.
[154]Huete, A. R., and Jackson, R. D. Soil and atmosphere influences on the spectra of partial canopies. Remote Sensing of Environment,1988,25(1):89-105.
[155]Huete, A. R., Hua, G., Qi, J., Chehbouni, A., Van Leeuwen, W. J. D. Normalization of multidirectional red and NIR reflectances with the SAVI. Remote sensing of Environment.1992,41,143-154.
[156]Ishii, H.T., Tanabe, S., and Hiura, T.Exploring the relationships among canopy structure, stand productivity, and biodiversity of temperature forest ecosystems. Forest Science,2004,50 (3):342-355.
[157]Jennifer L.R. JenEn, Karen S. Humes, Andrew T. Hudak, Lee A. Vierling, Eric Delmelle, Evaluation of the MODIS LAI product using independent lidar-derived LAI:A caE study in mixed conifer forest. Remote Ensing of Environment,2011, 115:3625-3639.
[158]Jin, Z., Tian, Q., Chen, J.M.,Chen, M. Spatial scaling between leaf area index maps of different resolutions. Journal of Environmental Management, doi:10.1016/ j.jenvman.2006.08.016.
[159]Jin Y., Gao F., Schaaf C. B., Li X., Strahler A. H., Bruegge C. J. Improving MODIS surface BRDF/albedo retrieval with MISR multiangle observation. IEEE Transactions on Geoscience and Remote Sensing.2002,40:1593-1604.
[160]Jordan,C.F. Derivation of leaf area index from quality of light on the forest floor. Ecology,1969,50:663-666.
[161]Josef,K., Tomas, D., Anton, V.R., Jiri,V. and Karel, V. Reservoirs'siltation measurments and sediment transport assessment in the Czech Republic, the Vrchlice catchment study. CATENA,2005,64:348-362.
[162]Kaufman Y. J., Tanre D. Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Transactions on Geoscience and Remote sensing.1992,30: 261-270.
[163]Keeling, E.G, Sala, A., and DeLuca,T. H. Effects of fire exclusion on forest structure and composition in unlogged ponderosa pine/Douglas-fir forests. Forest Ecology and Management,2006,237 (1-3):418-428.
[164]Laflen, J.M.,et al. WEPP soil erodiblity experiments for range-land and cropland soils Journal of Soil and Water Conservation,1991,46(1):40-44.
[165]Langbein, W. B.,and Schumn, S. A. Yield of Ediment in relation to mean annual precipitation.Transaction, American Geophysical Union,1958,39:1078-1084.
[166]Langbein, W. B.,and Schumn, S. A. Yield of sediment in relation to mean annual precipitation. Transaction, American Geophysical Union,1958,39:1078-1084.
[167]Leblanc, S. G., Chen, J. M., Cihlar, J. NDVI directionality in boreal forests:A model interpretation of measurements. Canadian Journal of Remote sensing.1997,23, 369-380.
[168]Li, X. B., Chen,Y.H., Yang,H.and Zhang,Y.X. Improvement, comparison, and application of field measurement methods for grassland vegetation fractional overage.Journal of Integrative Plant Biology,2005,47(9):1074-1083.
[169]Liang,S.L. Quantitative remote sensing of land surfaces. John Wiley &Sons, Inc, 2003, pp:560.
[170]Liang, S.L., Fang, H.L., Morisette, J.T., et al. Atmospheric correction of Landsat ETM+land surface imagery. II. Validation and applications.Geoscience and Remote Sensing,2002,40(12):2736-2746.
[171]Lu, H., Raupach, M. R., McVicar, T. R.et al. Decomposition of vegetation cover into woody and herbaceous components using AVHRR NDVI time series. Remote Sensing of Environment,2003,86(1):1-18.
[172]Machito, M., Naoyuki, Y, Takashi,U. Application of USLE for the prediction of nutrient losses in soil erosion processes. Paddy and Water Environment,2005, 3(2):111-119.
[173]Marcelo F., Breunig, L. S. G., Antonio R. F., Jose C.N. E., Directional effects on NDVI and LAI retrievals from MODIS:A caE study in Brazil with soybean. International Journal of Applied Earth Observation and Geoinformation,2011, 13:34-42.
[174]Matthew, J., Cohen, Keith D.,et al. Empirical reformulation of the universal soil loss equation for erosion risk assessment in a tropical watershed. Geoderma,2005, 124:235-252.
[175]Mcallister D. M., Valeo C. A. Robust new method for the remote estimation of LAI in montane and boreal forests. International Journal of Remote Sensing.2007,28: 1891-1905.
[176]Mcdaniel, K. C., and Haas, R. H..Assessing mesquite-grass vegetation condition from Landsat. Photogrammetric Engineering and Remote Sensing,1982, 48:441-450.
[177]McGwire,K., Minor,T.and Fenstermaker,L. Hyperspectral mixture modeling for quantifying sparse vegetation cover in arid environments.Remote Sensing of Environment.2000,72(3):360-374.
[178]Mikhailova E A, Bryant R B, Schwager S J.,et al.Predicting rainfall erosivity in Honduras.Soil Science Society of American Journal,1997,61:273-279.
[179]Minomura,M., Kuze,H., Takeuchi,N. Adjacency effect in the atmospheric correction of satellite remote sensing data:Evaluation of the influence of aerosol extinction profiles. Optical review,2001,8 (2):133-141.
[180]Mishra, S.K., J.V. Tyagi, V.P. Singh and Ranvir Singh. SCS-CN-based modeling of sediment yield. Journal of Hydrology,2005,15:352-357.
[181]Moehansyah, Maheshwari, H. B. L.,and Armstrong, J. Field Evaluation of Selected Soil Erosion Models for Catchment Management in Indonesia. Biosystems Engineering,2004,88:491-506.
[182]Moore, I. D., Wilson, J. P. Length-slope factors for the revised universal soil loss equation:simplified method of estimation. Journal of Soil and Water Conservation, 1992,47(5),423-428.
[183]Morgan R.P.C., Soil erosion and conservation. London:Longman, Essex.,1995.145 Morgan, R.P.C., Quinton, J.N., Smith, R.E., Govers, G, Poesen, J.W.A., Auerswald, K., Chisci, G., Torri,D., and Styczen, M.E. The European soil erosion model (EUROSEM):a dynamic approach for predicting sediment transport from fields and small catchments. Earth Surface Processes and Landforms,1998,23:527-544.
[184]Nolin A. W. Towards retrieval of forest cover density over snow from the Multi-angle Imaging SpectroRadiometer (MISR). Hydrological Processes.2004,18: 3623-3636.
[185]North,P.R.J. Estimation of f(APAR), LAI, and vegetation fractional cover from ATSR-2 imagery. Remote Sensing of Environment,2002,80:114-121.
[186]Okan, F., and Nilgun, B. H. Integration of GIS with USLE in Assessment of Soil Erosion. Water Resources Management,2002,16(6):447-467.
[187]Pearson, R. L.,& Miller, L. D. Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado.In Proceedings of the 8th International symposium on Remote Ensing of the Environment.1972, vol. II(pp.1355-1379)(Ann Arbor, MI).
[188]Pinty B., Widlowski J. L., Gobron N., Verstraete M. M., Diner D. J. Uniqueness of multiangular measurements-Part Ⅰ:An indicator of subpixel surface heterogeneity from MISR. IEEE Transactions on Geoscience and Remote Sensing.2002,40: 1560-1573.
[189]Pocewicz A., Lee A. Vierling, Leigh B. Lentile, Smith R. View angle effects on relationships between MISR vegetation indices and leaf area index in a recently burned ponderosa pine forest. Remote sensing of Environment,2007,107:322-333
[190]Qi, J., Chenbouni, A., Huete, A. R., Kerr,Y.H. and Soroonshian,S.A modified soil adjusted vegetation index (MSAVI). Remote sensing of environment,1994,48 (2): 119-126.
[191]Qi, J., Moran, M. S., Cabot, F., Dedieu, G. Normalization of sun/view angle effects using spectral albedo-baEd vegetation indices. Remote sensing of Environment. 1995,52:207-217.
[192]Rautiainen M., Stenberg P., Nilson T., Kuusk A. The effect of crown shape on the reflectance of coniferous stands. Remote sensing of Environment.2004,89:41-52.
[193]Rautiainen M., Retrieval of leaf area index for a coniferous forest by inverting a forest reflectance model. Remote sensing of Environment,2005,99:295-303.
[194]Rautiainen, M., Lang, M., Mottus, M., Kuusk, A., Nilson, T., Kuusk, J., Lukk, T. Multi-angular reflectance properties of a hemiboreal forest:An analysis using CHRIS PROBA data. Remote sensing of environment.2008,112(5),2627-2642.
[195]Renard,K. G, Foster, G.R., Weesies,G.A., Porter,J. P. RUSLE revised universal soil loss equation. Journal of Soil and Water Conservation.1991,46(1):30-33.
[196]Renard,K G, Foster, G. R., Weesies, G. A., et al. Predicting soil erosion by water:a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). Handbook No.703. U. S, Department of Agriculture, Washington D. C.,1997.
[197]Richardson, A. J.,& Wiegand, C. L. Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote sensing,1977,43, 1541-1552.
[198]Ringrose, S., Matheson, W., Wolski, P., and Huntsman-Mapila, P. Vegetation cover trends along the Botswana Kalahari transect. Journal of Arid Environment,2003,54:297-317.
[199]Rouse, J.W.,and Haas, R.H. Monitoring vegetation systems in the great plain with ERTS. Third ERTS Symposium, Washington, DC,1973:309-317.
[200]Rouse J. W., Haas R. H., Schell J. A., Deering D. W., Harlan J. C. Monitoring the vernal advancement of retrogradation of natural vegetation. Greenbelt, MD: NASA/GSFC (Type III, Final Report).1974, p.371.
[201]Roue, J. W., Haas, R. H., Schell, J. A., Deering, D. W.,& Harlan, J. C. Monitoring the vernal advancement of retrogradation of natural vagetation (p.371).1974, Greenbelt, MD:NASA/GSFC (Type III, Final Report).
[202]Running, S.W., Nemani, R., Peterson, D.L., Band, L.E., Potts, D.F., Pierce, L.L., Spanner, M.A. Mapping regional forest evapotranspiration and photosynthesis by coupling satellite data with ecosystem simulation. Ecology.1989,70(4),1090-1101.
[203]Saro, L. Soil erosion assessment and its verification using the Universal Soil Loss Equation and Geographic Information System:a case study at Boun, Korea. Environmental Geology,2004,45(4):457-465.
[204]Schadlich, S., Gottsche,F.M., and Olesen, F.S. Influence of land surface parameters and atmosphere on METEOSAT brightness temperatures and generation of land surface temperature maps by temporally and spatially interpolating atmospheric correction. Remote Sensing of Environment,2001,75 (1):39-46.
[205]Schlerf, M., Atzberger, C., Hill, J. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data. Remote sensing of Environment.2005,95 (2),177-194.
[206]Sharpley, A.N., Williams, J.R. (Eds.). EPIC-Erosion Productivity Impact Calculator: 1. Model Documentation. US Department of Agriculture Technical Bulletin No. 1768. US Department of Agriculture, Temple, TX,1990.
[207]Simon, W., Jonathan, L., Gordon, H. An evaluation of grid size uncertainty in empirical soil loss modeling with digital elevation models. Environmental Modeling and Assessment,2005,10(1):33-42.
[208]Smith B., Knorr W., Widlowski J. L., Pinty B., Gobron N. Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances. Forest Ecology and Management.2008,255:3985-3994.
[209]Soudani,K., Francois,C., le Maire,G, Le Dantec,V., and Dufrene,E. Comparative analysis of IKONOS, SPOT, and ETM+data for leaf area index estimation in temperate coniferous and deciduous forest stands. Remote Sensing of Environment,2006,102:161-175.
[210]Spanner, M. A.,et al. Soil Loss Prediction in A Geographic Information System Format. In Proc of the Inter.Sym.on Remote Sensing of Environment,1983.
[211]Stedinick, J. D.Monitoring the effect of timber harvest on annual water yield. Journal of Hydrology,1996,176:79-95.
[212]Steven,D. W., Helena M., Matthew G. H., Sheldon L., Felib Y. I., Thomas, S. R., and Gary,M. S. Validation of a 3-D enhancement of the Universal Soil Loss Equation for prediction of soil erosion and sediment deposition. CATENA,2005, 64(30):281-296.
[213]Suleyman Ozhan, A. Nihat Balci, Necdet Ozyuvaci, Ahmet Hizal, Ferhat Gokbulak and Yusuf Serengil. Cover and management factors for the Universal Soil-Loss Equation for forest ecosystems in the Marmara region, Turkey. Forest Ecology and Management,2005,214(3):118-123.
[214]Sun,P. S., Liu, S. R. Large scale eco-hydrological model and its integration with GIS. Acta Ecologica Sinica,2003,23(10):2115-2124.
[215]Thomas, D.B., Erikson, A., Grunder, M., Mburu, J.K. Soil and Water Conservation Manual for Kenya. Soil and Water Conservation Branch, Ministry of Agriculture. Livestock Development and Marketing, Republic of Kenya,1997.
[216]Tickle,P. K., Lee, A., Lucas, R. M., Austin, J., and Witte,C. Quantifying Australian forest floristics and structure using small footprint LiDAR and large scale aerial photography. Forest Ecology and Management,2006,223(1-3):379-394.
[217]Van Rompaey, A.J.J., Verstraeten, G.,Van Oost K.,Govers, G,Poesen, J. Modelling Mean Annual Sediment Yield Using a Distributed Approach, Earth Surface Processes and Landforms,2001,26:1221-1236.
[218]Verrelst, J. Schaepman, M. E., Koetz, B. Kneubuhler, M. Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote sensing of environment.2008,112(5),2341-2353.
[219]Vina, A.,& Gitelson, A. A. New developments in the remote estimation of the fraction of absorbed photosynthetically active radiation in crops. Geophysical Research Letters.2005,32,(17) L17403.
[220]Vohland M., Mader S., Dorigo W. Applying different inversion techniques to retrieve stand variables of summer barley with PROSPECT+SAIL. International Journal of Applied Earth ObErvation and Geoinformation,2010,12:71-80.
[221]Vuolo F., Dini L., Durso G. Retrieval of leaf area index from CHRIS/PROBA data: an analysis of the directional and spectral information content. International Journal of Remote sensing,2008,29(17):5063-5072
[222]Watson,F.GR.,Grayson,R. B.,Vertessy, R. A.Large scale distribution modeling and the utility of detailed ground data,Hydrological Processes,1998,12:873-888.
[223]Warren B. Cohen, Thomas K. Maiersperger, Stith T. Gower, David P. Turner. An improved strategy for regression of biophysical variables and Landsat ETM+data. Remote Ensing of Environment,2003,84:561-571.
[224]Wischmeier, W. H., Johnson, C. B.,Cross, B.V. A soil erodibility monograph for farmland construction sites. Journal of Soil and Water Conservation.1971,26: 189-193.
[225]White, M. A., Asner, G. P., Nemani, R. R., Privette, J. L.,& Running, S. W. Measuring fractional cover and leaf area index in arid ecosystem:Digital camera, radiation transmittance, and laser altimetry methods. Remote Sensing of Environment,2000,74(1):45-57.
[226]Williams, J. R. Sediment yield predictions with Univeral Equation using runoff energy factor. In:Present and prospective Technoligy for predicting sediment yields and sources USDA-ARS-5-40. Agricultural. Research Service, USDA. Washington D. C.1975,pp:244-252.
[227]Williams, J.R., Renard,K. G, Dyke,P.T. EPIC, a new method for assessing erosion's effect on soil productivity. Journal of Soil and Water Conservation,1983,38(5):381-383.
[228]Wischmeier,W.H.,Smith et al. Predicting rainfall erosion losses-A guide to Conservation planning, Agricultrual Handbook 537, USDA,Washington, D.C.,1978. pp.1-58.
[229]Wischemeier, W. H.,Smith, D. D.Rainfall energy and its relationship to soil loss. Transactions, American Geophysical Union,1958,39:285-291.
[230]Wittich,K.P.,and Hansing,O.Area-averaged vegetative cover fraction estimated from satellite data.International Journal of Biometerology,l995,38 (3):209-215.
[231]Xiao,J.F.and Moody,A.A. comparison of methods for estimating fractional green vegetation cover within a desert-to-upland transition zone in central New Mexico, USA.Remote Sensing of Environment.2005,98 (2-3):237-250.
[232]Yao Y. J., Liu Q. H., Liu Q., Li X. W. LAI retrieval and uncertainty evaluations for typical row-planted crops at different growth stages. Remote Ensing of Environment, 2008,112:94-106.
[233]Young, R.A., Onstad, C.A., Bosch, D.D., Anderson, W.P., AGNPS:A nonpoint-source pollution model for evaluating agricultural watersheds. Journal of Soil and Water Conservation,1989,44 (2):4522-4561.
[234]Zeng, Z. Y. A new method of data transformation for satellite images:II. Transformation equations for SPOT, NOAA, IKONOS, QuickBird, ASTER, MSS and other images and application. International Journal of Remote Sensing,2007,28 (18):4125-4155.
[235]Zhang Z. M., He G. J., Wang X. Q., Jiang H. Leaf area index estimation of bamboo forest in Fujian province based on IRS P6 LISS 3 imagery. International Journal of Remote sensing,2011,32(19):5365-5379.
[236]Zhang X., Walling D.E., Quine T.A., Wen A., Use of reservoir deposits and caesium-137 measurements to investigate the erisonal response of a small drainage basin in the rolling loess plateau region of China. Land Degrad Devel,1997,8:1-16.
[237]Zhou,Q., and Robson,M. Automated rangeland vegetation cover and density estimation using ground digital images and a spectral-contextual classifier. Remote Sensing,2001,22(17):3457-3470.
[238]Zhu, J. J., Matsuzaki, I.,and Gonda, Y. Optical stratification porosity as a measure of vertical canopy structure in a Japanese coastal forest. Forest Ecology and Management,2003,173(1-3):89-104.
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