基于无人机和MODIS数据的巴丹吉林沙漠植被分布特征与动态变化研究
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摘要
荒漠植被在减缓沙漠扩张、阻挡风沙入侵绿洲、维护绿洲稳定性方面具有重要意义。通过无人机搭载多光谱相机获取地面样地高分辨率NDVI数据以计算植被覆盖度,结合MODIS-NDVI数据,分析了MODIS-NDVI与无人机获取NDVI数据之间的一致性,进而建立MODIS-NDVI数据与样方植被覆盖度的空间反演模型,以研究巴丹吉林沙漠植被分布特征、动态变化及其对气温、降水的响应规律。结果表明:(1)基于无人机多光谱数据大样方植被覆盖度和MODIS-NDVI数据构建的植被覆盖度反演模型具有较高的模拟精度。(2)研究区总体植被覆盖度较低,空间上呈由东南向西北递减的趋势。(3)植被覆盖度总体呈现上升趋势,沙漠边缘的增速较沙漠内部更为明显,其中沙漠边缘增长率达1.53%/10a,而沙漠内部为0.84%/10a。沙漠边缘57.12%植被出现改善,而沙漠内部植被改善的面积约为21.26%。(4)植被生长主要受制于生长季降水量并呈现出明显的季节变化,此外植被对降水和气温的响应存在一定的滞后效应。
The desert vegetation is of great significance in restraining the desert expansion, preventing the sandstorm invasion and protecting the oasis stability. In this paper, we obtained the high resolution NDVI data by the multispectral camera on a UAV(Unmanned aerial vehicles) to calculate the vegetation coverage at sample area, and the vegetation NDVI data was extracted from the MODIS data. We analyzed the consistency between MODIS-NDVI and UAV-NDVI and constructed the spatial inversion model by combining the MODIS-NDVI data and the vegetation coverage, to study the distribution characteristics, the dynamic change of vegetation, and its response to the temperature and precipitation in Badain Jaran Desert and its marginal region. The results show that:(1) The inversion model in combination with the MODIS-NDVI data and the vegetation coverage based on multi-spectral data had high simulation accuracy.(2) As a whole, the vegetation coverage was relatively low, and the spatial pattern was gradually decreasing from southeast to northwest.(3) The annual average of vegetation coverage showed an upward trend, and the growth rate of the desert edge was more obvious than that of the desert interior, in which the growth rate was 1.53% per ten years in desert marginal regions while 0.84% per ten years in the desert interior regions. Simultaneously, 57.12% of the vegetation improved in marginal regions, while about 21.26% meliorated in interior desert regions.(4) The vegetation showed the seasonal changes and its growth was mainly constricted by precipitation in growing seasons, furthermore, the response of vegetation to precipitation and temperature had a certain lag effect.
The desert vegetation is of great significance in restraining the desert expansion, preventing the sandstorm invasion and protecting the oasis stability. In this paper, we obtained the high resolution NDVI data by the multispectral camera on a UAV(Unmanned aerial vehicles) to calculate the vegetation coverage at sample area, and the vegetation NDVI data was extracted from the MODIS data. We analyzed the consistency between MODIS-NDVI and UAV-NDVI and constructed the spatial inversion model by combining the MODIS-NDVI data and the vegetation coverage, to study the distribution characteristics, the dynamic change of vegetation, and its response to the temperature and precipitation in Badain Jaran Desert and its marginal region. The results show that:(1) The inversion model in combination with the MODIS-NDVI data and the vegetation coverage based on multi-spectral data had high simulation accuracy.(2) As a whole, the vegetation coverage was relatively low, and the spatial pattern was gradually decreasing from southeast to northwest.(3) The annual average of vegetation coverage showed an upward trend, and the growth rate of the desert edge was more obvious than that of the desert interior, in which the growth rate was 1.53% per ten years in desert marginal regions while 0.84% per ten years in the desert interior regions. Simultaneously, 57.12% of the vegetation improved in marginal regions, while about 21.26% meliorated in interior desert regions.(4) The vegetation showed the seasonal changes and its growth was mainly constricted by precipitation in growing seasons, furthermore, the response of vegetation to precipitation and temperature had a certain lag effect.
引文
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[21] Bian J,Li A,Zhang Z,et al.Grassland fractional vegetation cover monitoring using the composited HJ-1A/B time series images and unmanned aerial vehicles:a case study in Zoige wetland,China[C]//IEEE International Geoscience and Remote Sensing Symposium.IEEE,2016:7192-7195.
[22] Lu B,He Y.Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland[J].ISPRS Journal of Photogrammetry & Remote Sensing,2017,128:73-85.
[23] 葛静,孟宝平,杨淑霞,等.基于UAV技术和MODIS遥感数据的高寒草地盖度动态变化监测研究——以黄河源东部地区为例[J].草业学报,2017,26(3):1-12.
[24] 宋清洁,崔霞,张瑶瑶,等.基于小型无人机与MODIS数据的草地植被覆盖度研究——以甘南州为例[J].草业科学,2017,34(1):40-50.
[25] 周在明,杨燕明,陈本清.基于无人机遥感监测滩涂湿地入侵种互花米草植被覆盖度[J].应用生态学报,2016,27(12):3920-3926.
[26] 王涛.巴丹吉林沙漠形成演变的若干问题[J].中国沙漠,1990,10(1):29-40.
[27] 王一谋,王建华,祁元,等.中国1∶10万沙漠(沙地)分布数据集[Z].寒区旱区科学数据中心,2005.
[28] Justice C O,Townshend J R G,Vermote E F,et al.An overview of MODIS land data processing and product status[J].Remote Sensing of Environment,2002,83(1):3-15.
[29] 刘宪锋,杨勇,任志远,等.2000—2009年黄土高原地区植被覆盖度时空变化[J].中国沙漠,2013,33(4):1244-1249.
[30] 任世龙,宜树华,陈建军,等.基于不同数码相机和图像处理方法的高寒草地植被盖度估算的比较[J].草业科学,2014,31(6):1007-1013.
[31] 牛宝茹,刘俊蓉,王政伟.干旱半干旱地区植被覆盖度遥感信息提取研究[J].武汉大学学报(信息科学版),2005,30(1):27-30.
[32] 赵军,王小敏,李东成.基于MODIS的民勤绿洲植被覆盖变化定量分析[J].干旱区资源与环境,2012,26(10):91-96.
[33] 宋怡,马明国.基于SPOT VEGETATION数据的中国西北植被覆盖变化分析[J].中国沙漠,2007,27(1):89-93.
[34] 崔书珍,周金国.无人机航摄系统在1∶1 000地形图测绘中的应用[J].地矿测绘,2014,30(4):29-31.
[35] Dulava S,Bean W T,Richmond O M W.Environmental reviews and case studies:applications of Unmanned Aircraft Systems (UAS) for waterbird surveys[J].Environmental Practice,2015,17(3):201-210.
[36] Carlson T N,Ripley D A.On the relation between NDVI,fractional vegetation cover,and leaf area index [J].Remote Sensing of Environment,1997,62(3):241-252.
[37] 孟宝平,陈思宇,崔霞,等.基于多源遥感数据的高寒草地生物量反演模型精度——以夏河县桑科草原试验区为例[J].草业科学,2015,32(11):1730-1739.
[38] 刘冰,赵文智,常学向,等.黑河流域荒漠区土壤水分对降水脉动响应[J].中国沙漠,2011,31(3):716-722.
[39] 赵文智,刘鹄.干旱、半干旱环境降水脉动对生态系统的影响[J].应用生态学报,2011,22(1):243-249.
[40] 何明珠.阿拉善高原荒漠植被组成分布特征及其环境解释:Ⅴ.一年生植物层片物种多样性及其分布特征[J].中国沙漠,2010,30(3):528-533.
[41] 梁存柱,刘钟龄,朱宗元,等.阿拉善荒漠区一年生植物层片物种多样性及其分布特征[J].应用生态学报,2003,14(6):897-903.
[42] 李万元,吕世华,董治宝,等.巴丹吉林沙漠周边地区降水量的时空变化特征[J].中国沙漠,2015,35(1):94-105.
[2] 王涛.干旱区绿洲化、荒漠化研究的进展与趋势[J].中国沙漠,2009,29(1):1-9.
[3] 马明国,董立新,王雪梅.过去21 a中国西北植被覆盖动态监测与模拟[J].冰川冻土,2003,25(2):232-236.
[4] 左小安,赵学勇,张铜会,等.中国北方农牧交错带植被动态研究进展[J].水土保持研究,2005,12(1):162-166.
[5] 朱金峰,王乃昂,陈红宝,等.基于遥感的巴丹吉林沙漠范围与面积分析[J].地理科学进展,2010,29(9):1087-1094.
[6] 王承安,安春梅,杜斌,等.内蒙古阿拉善盟生态环境“3S”技术定量动态分析[J].测绘科学,2005,30(3):78-79.
[7] 张伟民,王涛.巴丹吉林沙漠高大沙山形成演化初步探讨[J].中国沙漠,2005,25(2):281-286.
[8] 邵天杰,赵景波,董治宝.巴丹吉林沙漠湖泊及地下水化学特征[J].地理学报,2011,66(5):662-672.
[9] 马宁,王乃昂,朱金峰,等.巴丹吉林沙漠周边地区近50 a来气候变化特征[J].中国沙漠,2011,31(6):1541-1547.
[10] 安志山,张克存,谭立海,等.论沙漠-绿洲过渡带的风沙防护效应[J].干旱区研究,2017,34(5):1196-1202.
[11] 赵文智,庄艳丽.中国干旱区绿洲稳定性研究[J].干旱区研究,2008,25(2):155-162.
[12] 王蒙,董治宝,逯军峰,等.巴丹吉林沙漠周边植被特征和物种多样性[J].中国沙漠,2015,35(5):1226-1233.
[13] 刘铮瑶,董治宝,王建博,等.巴丹吉林沙漠边缘地区植被特征[J].中国沙漠,2016,36(5):1348-1356.
[14] 程红芳,章文波,陈锋.植被覆盖度遥感估算方法研究进展[J].国土资源遥感,2008(1):13-18.
[15] 王玮,冯琦胜,郭铌,等.基于长时间序列NDVI资料的我国西北干旱区植被覆盖动态监测[J].草业科学,2015,32(12):1969-1979.
[16] 古丽·加帕尔,陈曦,包安明.干旱区荒漠稀疏植被覆盖度提取及尺度扩展效应[J].应用生态学报,2009,20(12):2925-2934.
[17] 马琳雅,崔霞,冯琦胜,等.2001—2011年甘南草地植被覆盖度动态变化分析[J].草业学报,2014,23(4):1-9.
[18] 汪沛,罗锡文,周志艳,等.基于微小型无人机的遥感信息获取关键技术综述[J].农业工程学报,2014,30(18):1-12.
[19] 孙中宇,陈燕乔,杨龙,等.轻小型无人机低空遥感及其在生态学中的应用进展[J].应用生态学报,2017,28(2):528-536.
[20] Berni J A J,Zarco-Tejada P J,Suarez L,et al.Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle[J].IEEE Transactions on Geoscience & Remote Sensing,2009,47(3):722-738.
[21] Bian J,Li A,Zhang Z,et al.Grassland fractional vegetation cover monitoring using the composited HJ-1A/B time series images and unmanned aerial vehicles:a case study in Zoige wetland,China[C]//IEEE International Geoscience and Remote Sensing Symposium.IEEE,2016:7192-7195.
[22] Lu B,He Y.Species classification using Unmanned Aerial Vehicle (UAV)-acquired high spatial resolution imagery in a heterogeneous grassland[J].ISPRS Journal of Photogrammetry & Remote Sensing,2017,128:73-85.
[23] 葛静,孟宝平,杨淑霞,等.基于UAV技术和MODIS遥感数据的高寒草地盖度动态变化监测研究——以黄河源东部地区为例[J].草业学报,2017,26(3):1-12.
[24] 宋清洁,崔霞,张瑶瑶,等.基于小型无人机与MODIS数据的草地植被覆盖度研究——以甘南州为例[J].草业科学,2017,34(1):40-50.
[25] 周在明,杨燕明,陈本清.基于无人机遥感监测滩涂湿地入侵种互花米草植被覆盖度[J].应用生态学报,2016,27(12):3920-3926.
[26] 王涛.巴丹吉林沙漠形成演变的若干问题[J].中国沙漠,1990,10(1):29-40.
[27] 王一谋,王建华,祁元,等.中国1∶10万沙漠(沙地)分布数据集[Z].寒区旱区科学数据中心,2005.
[28] Justice C O,Townshend J R G,Vermote E F,et al.An overview of MODIS land data processing and product status[J].Remote Sensing of Environment,2002,83(1):3-15.
[29] 刘宪锋,杨勇,任志远,等.2000—2009年黄土高原地区植被覆盖度时空变化[J].中国沙漠,2013,33(4):1244-1249.
[30] 任世龙,宜树华,陈建军,等.基于不同数码相机和图像处理方法的高寒草地植被盖度估算的比较[J].草业科学,2014,31(6):1007-1013.
[31] 牛宝茹,刘俊蓉,王政伟.干旱半干旱地区植被覆盖度遥感信息提取研究[J].武汉大学学报(信息科学版),2005,30(1):27-30.
[32] 赵军,王小敏,李东成.基于MODIS的民勤绿洲植被覆盖变化定量分析[J].干旱区资源与环境,2012,26(10):91-96.
[33] 宋怡,马明国.基于SPOT VEGETATION数据的中国西北植被覆盖变化分析[J].中国沙漠,2007,27(1):89-93.
[34] 崔书珍,周金国.无人机航摄系统在1∶1 000地形图测绘中的应用[J].地矿测绘,2014,30(4):29-31.
[35] Dulava S,Bean W T,Richmond O M W.Environmental reviews and case studies:applications of Unmanned Aircraft Systems (UAS) for waterbird surveys[J].Environmental Practice,2015,17(3):201-210.
[36] Carlson T N,Ripley D A.On the relation between NDVI,fractional vegetation cover,and leaf area index [J].Remote Sensing of Environment,1997,62(3):241-252.
[37] 孟宝平,陈思宇,崔霞,等.基于多源遥感数据的高寒草地生物量反演模型精度——以夏河县桑科草原试验区为例[J].草业科学,2015,32(11):1730-1739.
[38] 刘冰,赵文智,常学向,等.黑河流域荒漠区土壤水分对降水脉动响应[J].中国沙漠,2011,31(3):716-722.
[39] 赵文智,刘鹄.干旱、半干旱环境降水脉动对生态系统的影响[J].应用生态学报,2011,22(1):243-249.
[40] 何明珠.阿拉善高原荒漠植被组成分布特征及其环境解释:Ⅴ.一年生植物层片物种多样性及其分布特征[J].中国沙漠,2010,30(3):528-533.
[41] 梁存柱,刘钟龄,朱宗元,等.阿拉善荒漠区一年生植物层片物种多样性及其分布特征[J].应用生态学报,2003,14(6):897-903.
[42] 李万元,吕世华,董治宝,等.巴丹吉林沙漠周边地区降水量的时空变化特征[J].中国沙漠,2015,35(1):94-105.