利用ESAR极化数据的复杂地形区森林地上生物量估算
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摘要
利用机载E-SAR传感器获取的P-波段全极化SAR数据与实测林分样地数据,分析不同极化方式后向散射系数在地形起伏区与森林地上生物量(AGB)的响应关系,以改进的水云模型为基础,建立了融入地形因子的分析性模型。采用遗传算法确定模型的最优参数,并对模型在不同坡度情况下的可靠性、稳定性进行分析,同时通过与常用模型相对比,确定水云分析模型在复杂地形区估算AGB的优势。结果表明:在森林AGB处于较低值的情况下,后向散射系数(HH、HV、VV)变化趋势与AGB变化趋势保持一致,但随着AGB值的增大,这种一致性仅在HV极化方式下继续保持,因此相比之下,HV极化方式更适用于复杂地形区生物量的估算。地形对森林AGB的估算具有极大的影响,后向散射系数与AGB的相关性随着地形坡度的增加而减小。5种模型估算森林AGB的能力大小排序为:水云分析模型>二次模型>对数模型>指数模型>线性模型。地形起伏较小的地区估算稳定性排序为:水云分析模型>二次模型>对数模型>指数模型>线性模型。地形起伏较大的地区估算稳定性排序为。水云分析模型>二次模型>线性模型>指数模型>对数模型。利用水云分析模型对研究区AGB估算,其实测AGB与模型估算的生物量值决定系数为0.597,RMSE为30.876t/hm~2,拟合精度为77.40%。
The influence of the ground slope on radar backscatter has been proven to be greater for lower radar frequencies due to deeper canopy penetration.In order to solve this problem and obtain accurate estimation of forest above-ground biomass(AGB)in the region of rugged terrain,the analytic model integrating the topographic factors was presented based on the modified water-cloud model(WCM)and the relationship between different backscattering coefficients and the forest AGB using the airborne P-band full polarimetric SAR(PolSAR)data acquired by E-SAR.In this study,genetic algorithm(GA)was used to determine the optimal parameter values for the model,the terrain slope was divided into three grades(0~5°、5°~10°、≥10°).Then we analyzed the reliability and stability of the model under the condition of different slope.Meanwhile,in order to determine advantage of the water-cloud analysis model in evaluating AGB,we used common models include linear model、logarithm model、exponential model、quadratic model to comparison and analysis.Through the comparative analysis,we found that when the forest AGB at lower level,the variational trend of backscatter coefficients(HH、HV、VV)kept the same with the vatiational trend of AGB.With the increase of AGB values,this consistency in HV backscatter coefficient values to keep alone,therefore,HV polarization was the best to estimate biomass in the complex terrain region.The terrain has a great impact on estimating forest AGB,a phenomenon was that the correlation of backscatter coefficients and forest AGB decreased with the increase of ground slope.The capabilities of estimate biomass in the five models were different,from strong to weak was that water-cloud analysis model>quadratic model>logarithm model>exponential model>linear model.Meanwhile,through comparing the change of the determination coefficients(R2),these models were found that have different stabilities to estimate forest AGB in different slope levels.When the slope changed from 0~5°to5°~10°,the stability from strong to weak was water-cloud analysis model>quadratic model>logarithm model>exponential model>linear model.With the slope from 5°~10°to≥10°,this sequence became that water-cloud analysis model>exponential model>linear model>quadratic model>logarithm model.In addition,between 0~5°to≥10°,this sequence was water-cloud analysis model>quadratic model>linear model>exponential model>logarithm model respectively.Although,there was different sequence in five models,the stability of the water-cloud analysis model was higher than other models.So,we tried to use water-cloud analysis model to estimate forest AGB for the study area.The result showed that the R2 between the field AGB and estimated AGB was 0.597,the root mean squared error(RMSE)was 30.876 t/hm~2,the overall accuracy was 77.40%.
The influence of the ground slope on radar backscatter has been proven to be greater for lower radar frequencies due to deeper canopy penetration.In order to solve this problem and obtain accurate estimation of forest above-ground biomass(AGB)in the region of rugged terrain,the analytic model integrating the topographic factors was presented based on the modified water-cloud model(WCM)and the relationship between different backscattering coefficients and the forest AGB using the airborne P-band full polarimetric SAR(PolSAR)data acquired by E-SAR.In this study,genetic algorithm(GA)was used to determine the optimal parameter values for the model,the terrain slope was divided into three grades(0~5°、5°~10°、≥10°).Then we analyzed the reliability and stability of the model under the condition of different slope.Meanwhile,in order to determine advantage of the water-cloud analysis model in evaluating AGB,we used common models include linear model、logarithm model、exponential model、quadratic model to comparison and analysis.Through the comparative analysis,we found that when the forest AGB at lower level,the variational trend of backscatter coefficients(HH、HV、VV)kept the same with the vatiational trend of AGB.With the increase of AGB values,this consistency in HV backscatter coefficient values to keep alone,therefore,HV polarization was the best to estimate biomass in the complex terrain region.The terrain has a great impact on estimating forest AGB,a phenomenon was that the correlation of backscatter coefficients and forest AGB decreased with the increase of ground slope.The capabilities of estimate biomass in the five models were different,from strong to weak was that water-cloud analysis model>quadratic model>logarithm model>exponential model>linear model.Meanwhile,through comparing the change of the determination coefficients(R2),these models were found that have different stabilities to estimate forest AGB in different slope levels.When the slope changed from 0~5°to5°~10°,the stability from strong to weak was water-cloud analysis model>quadratic model>logarithm model>exponential model>linear model.With the slope from 5°~10°to≥10°,this sequence became that water-cloud analysis model>exponential model>linear model>quadratic model>logarithm model.In addition,between 0~5°to≥10°,this sequence was water-cloud analysis model>quadratic model>linear model>exponential model>logarithm model respectively.Although,there was different sequence in five models,the stability of the water-cloud analysis model was higher than other models.So,we tried to use water-cloud analysis model to estimate forest AGB for the study area.The result showed that the R2 between the field AGB and estimated AGB was 0.597,the root mean squared error(RMSE)was 30.876 t/hm~2,the overall accuracy was 77.40%.
引文
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[10]王新云,郭艺歌,何杰.基于多源遥感数据的草地生物量估算方法[J].农业工程学报,2014,30(11):159-166.WANG Xingyun,GUO Yige,HE Jie.Estimation of Aboveground Biomass of Grassland Based on Multi-source Remote Sensing Data[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(11):159-166.
[11] SINHA S,JEGANATHAN C,SHARMA L K,et al.A Review of Radar Remote Sensing for Biomass Estimation[J].International Journal of Environmental Science and Technology,2015,12(5):1779-1792.
[12] ALAPPAT V O,JOSHI A K,KRISHNAMURTHY Y V N.Tropical Dry Deciduous Forest Stand Variable Estimation Using SAR Data[J].Journal of the Indian Society of Remote Sensing,2011,39(4):583-589.
[13] HAMDAN O,AZIZ H K,RAHMAN K A.Remotely Sensed L-band SAR Data for Tropical Forest Biomass Estimation[J].Journal of Tropical Forest Science,2011,23(3):318-327.
[14] MINH D H T,TOAN T L,TEBALDINI S,et al.Assessment of the P-and L-band SAR Tomography for the Characterization of Tropical Forests[C]∥Proceedings of2015IEEE Transactions on Geoscience and Remote Sensing.Milan,Italy:IEEE,2015,15(2):2931-2934.
[15] SOJA M J,SANDBERG G,ULANDER L M H.Topographic Correction for Biomass Retrieval from P-band SAR Data in Boreal Forests[C]∥Proceedings of 2015IEEE International Geoscience and Remote Sensing Symposium. Honolulu:IEEE,2010,15(5):4776-4779.
[16] SMALL D.Flattening Gamma:Radiometric Terrain Correction for SAR Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(8):3081-3093.
[17] WEGMULLER U.Automated Terrain Corrected SAR Geocoding[C]∥Proceedings of 1999IEEE International Geoscience and Remote Sensing Symposium.Hamburg:IEEE,1999(3):1712-1714.
[18] LOEW A,MAUSER W.Generation of Geometrically and Radiometrically Terrain Corrected SAR Image Products[J].Remote Sensing of Environment,2007,106(3):337-349.
[19] FREY O,SANTORO M,WERNER C L,et al.DEMbased SAR Pixel-area Estimation for Enhanced Geocoding Refinement and Radiometric Normalization[J].IEEE Geoscience and Remote Sensing Letters,2013,10(1):48-52.
[20]冯琦,陈尔学,李增元,等.基于机载P-波段全极化SAR数据的复杂地形森林地上生物量估测方法[J].林业科学,2016,52(3):10-22.FENG Qi,CHEN Erxue,LI Zengyuan,et al.Forest Aboveground Biomass Estimation Method for Rugged Terrain Based on Airborne P-band PolSAR Data[J].Scientia Silvae Sinicae,2016,52(3):10-22.
[21] KUGLER F,LEE S K,HAJNSEK I,et al.Forest Height Estimation by Means of Pol-InSAR Data Inversion:The Role of the Vertical Wave Number[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(10):5294-5311.
[22] SANDBERG G,ULANDER L M H,FRANSSON J E S,et al.L-band and P-band Backscatter Intensity for Biomass Retrieval in Hemiboreal Forest[J].Remote Sensing of Environment,2011,115(11):2874-2886.
[23]黎夏,叶嘉安,王树功,等.红树林湿地植被生物量的雷达遥感估算[J].遥感学报,2006,10(3):388-396.LI Xia,YE Jiaan,WANG Shugong,et al.Estimating Mangrove Wetland Biomass Using Radar Remote Sensing[J].Journal of Remote Sensing,2006,10(3):388-396.
[24] ASKNE J,DAMMERT P B G,ULANDER L M H,et al.C-band Repeat-pass Interferometric SAR Observations of the Forest[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(1):25-35.
[25] SANTORO M,ASKNE J,SMITH G,et al.Stem Volume Retrieval in Boreal Forests from ERS-1/2Interferometry[J].Remote Sensing of Environment,2002,81(1):19-35.
[26] ASKNE J,SANTORO M.Multitemporal Repeat Pass SAR Interferometry of Boreal Forests[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(6):1219-1228.
[27]张风雷.遗传算法与最小二乘法在实验数据处理中的对比研究[J].大学物理,2007,26(6):32-34.ZHANG Fenglei.The Comparative Analysis Between the GA and the Method of Least Squares in Data Processing[J].College Physics,2007,26(6):32-34.
[2]解清华,汪长城,朱建军,等.顾及地形因素的S-RVOG模型和PD相干最优算法联合反演植被高度[J].测绘学报,2015,44(6):686-693,701.DOI:10.11947/j.AGCS.2015.20130731.XIE Qinghua,WANG Changcheng,ZHU Jianjun,et al.Forest Height Inversion by Combining S-RVOG Model with Terrain Factor and PD Coherence Optimization[J].Acta Geodaetica et Cartographica Sinica,2015,44(6):686-693,701.DOI:10.11947/j.AGCS.2015.20130731.
[3]刘茜,杨乐,柳钦火,等.森林地上生物量遥感反演方法综述[J].遥感学报,2015,19(1):62-74.LIU Qian,YANG Le,LIU Qinhuo,et al.Review of Forest Above Ground Biomass Inversion Methods Based on Remote Sensing Technology[J].Journal of Remote Sensing,2015,19(1):62-74.
[4]黄克标,庞勇,舒清态,等.基于ICESat GLAS的云南省森林地上生物量反演[J].遥感学报,2013,17(1):165-179.HUANG Kebiao,PANG Yong,SHU Qingtai,et al.Above Ground Forest Biomass Estimation Using ICESat GLAS in Yunnan,China[J].Journal of Remote Sensing,2013,17(1):165-179.
[5] GHASEMI N,SAHEBI M,MOHAMMADZADEH A.A Review on Biomass Estimation Methods Using Synthetic Aperture Radar Data[J].International Journal of Geomatics and Geosciences,2011,4(1):776-788.
[6]冯宗炜,陈楚莹,张家武,等.湖南会同地区马尾松林生物量的测定[J].林业科学,1982,18(2):127-134.FENG Zongwei,CHEN Chuying,ZHANG Jiawu,et al.Determination of Biomass of Pinus Massoniana Stand in Huitong County,Hunan Province[J].Scientia Silvae Sinicae,1982,18(2):127-134.
[7] BACCINI A,GOETZ S J,WALKER W S,et al.Estimated Carbon Dioxide Emissions from Tropical Deforestation Improved by Carbon-density Maps[J].Nature Climate Change,2012,2(3):182-185.
[8] BOUDREAU J,NELSON R F,MARGOLIS H A,et al.Regional Aboveground Forest Biomass Using Airborne and Spaceborne LiDAR in Québec[J].Remote Sensing of Environment,2008,112(10):3876-3890.
[9] KINDERMANN G E,MCCALLUM I,FRITZ S,et al.A Global Forest Growing Stock,Biomass and Carbon Map Based on FAO Statistics[J].Silva Fennica,2008,42(3):387-396.
[10]王新云,郭艺歌,何杰.基于多源遥感数据的草地生物量估算方法[J].农业工程学报,2014,30(11):159-166.WANG Xingyun,GUO Yige,HE Jie.Estimation of Aboveground Biomass of Grassland Based on Multi-source Remote Sensing Data[J].Transactions of the Chinese Society of Agricultural Engineering,2014,30(11):159-166.
[11] SINHA S,JEGANATHAN C,SHARMA L K,et al.A Review of Radar Remote Sensing for Biomass Estimation[J].International Journal of Environmental Science and Technology,2015,12(5):1779-1792.
[12] ALAPPAT V O,JOSHI A K,KRISHNAMURTHY Y V N.Tropical Dry Deciduous Forest Stand Variable Estimation Using SAR Data[J].Journal of the Indian Society of Remote Sensing,2011,39(4):583-589.
[13] HAMDAN O,AZIZ H K,RAHMAN K A.Remotely Sensed L-band SAR Data for Tropical Forest Biomass Estimation[J].Journal of Tropical Forest Science,2011,23(3):318-327.
[14] MINH D H T,TOAN T L,TEBALDINI S,et al.Assessment of the P-and L-band SAR Tomography for the Characterization of Tropical Forests[C]∥Proceedings of2015IEEE Transactions on Geoscience and Remote Sensing.Milan,Italy:IEEE,2015,15(2):2931-2934.
[15] SOJA M J,SANDBERG G,ULANDER L M H.Topographic Correction for Biomass Retrieval from P-band SAR Data in Boreal Forests[C]∥Proceedings of 2015IEEE International Geoscience and Remote Sensing Symposium. Honolulu:IEEE,2010,15(5):4776-4779.
[16] SMALL D.Flattening Gamma:Radiometric Terrain Correction for SAR Imagery[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(8):3081-3093.
[17] WEGMULLER U.Automated Terrain Corrected SAR Geocoding[C]∥Proceedings of 1999IEEE International Geoscience and Remote Sensing Symposium.Hamburg:IEEE,1999(3):1712-1714.
[18] LOEW A,MAUSER W.Generation of Geometrically and Radiometrically Terrain Corrected SAR Image Products[J].Remote Sensing of Environment,2007,106(3):337-349.
[19] FREY O,SANTORO M,WERNER C L,et al.DEMbased SAR Pixel-area Estimation for Enhanced Geocoding Refinement and Radiometric Normalization[J].IEEE Geoscience and Remote Sensing Letters,2013,10(1):48-52.
[20]冯琦,陈尔学,李增元,等.基于机载P-波段全极化SAR数据的复杂地形森林地上生物量估测方法[J].林业科学,2016,52(3):10-22.FENG Qi,CHEN Erxue,LI Zengyuan,et al.Forest Aboveground Biomass Estimation Method for Rugged Terrain Based on Airborne P-band PolSAR Data[J].Scientia Silvae Sinicae,2016,52(3):10-22.
[21] KUGLER F,LEE S K,HAJNSEK I,et al.Forest Height Estimation by Means of Pol-InSAR Data Inversion:The Role of the Vertical Wave Number[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(10):5294-5311.
[22] SANDBERG G,ULANDER L M H,FRANSSON J E S,et al.L-band and P-band Backscatter Intensity for Biomass Retrieval in Hemiboreal Forest[J].Remote Sensing of Environment,2011,115(11):2874-2886.
[23]黎夏,叶嘉安,王树功,等.红树林湿地植被生物量的雷达遥感估算[J].遥感学报,2006,10(3):388-396.LI Xia,YE Jiaan,WANG Shugong,et al.Estimating Mangrove Wetland Biomass Using Radar Remote Sensing[J].Journal of Remote Sensing,2006,10(3):388-396.
[24] ASKNE J,DAMMERT P B G,ULANDER L M H,et al.C-band Repeat-pass Interferometric SAR Observations of the Forest[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(1):25-35.
[25] SANTORO M,ASKNE J,SMITH G,et al.Stem Volume Retrieval in Boreal Forests from ERS-1/2Interferometry[J].Remote Sensing of Environment,2002,81(1):19-35.
[26] ASKNE J,SANTORO M.Multitemporal Repeat Pass SAR Interferometry of Boreal Forests[J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(6):1219-1228.
[27]张风雷.遗传算法与最小二乘法在实验数据处理中的对比研究[J].大学物理,2007,26(6):32-34.ZHANG Fenglei.The Comparative Analysis Between the GA and the Method of Least Squares in Data Processing[J].College Physics,2007,26(6):32-34.