基于反射光谱与Landsat 8 OLI多光谱数据的艾比湖湿地土壤盐分估算
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摘要
土壤盐渍化是农业生产中最关键的生态问题之一,是一种降低土壤质量,严重影响作物产出的缓慢土壤退化过程。因此,土壤盐分的监测及评估对干旱区的土地管理与恢复至关重要。选取艾比湖湿地为研究区,基于2016年干湿两季(5月/9月)的Landsat8 OLI遥感影像,147个土壤实测样点的电导率(Electrical Conductivity, EC)数据及其对应的室内反射光谱数据,通过光谱重采样技术,建立该研究区土壤EC的偏最小二乘(partial least-squares regression, PLSR)定量估算模型,并尝试对干湿两季的土壤盐渍化状况进行对比分析。结果表明:(1)艾比湖湿地土壤盐渍化较为严重,湿季土壤EC(23.90 mS·cm~(-1))高于干季(11.62 mS·cm~(-1));(2)基于重采样处理后的光谱数据及13个光谱指数所建立的PLSR模型具有较好的精度(R2=0.91,RMSE=6.48mS·cm~(-1),RPD=2.45),说明该模型可以有效地对区域尺度的土壤EC进行定量估算。(3)从2016年5月至9月,艾比湖湿地的中度和重度盐渍土面积增加,轻度盐渍土和盐土面积减少。本研究将两种不同分辨率的数据进行联合建模,既提升了传统光学遥感影像模型的精度,又将高光谱数据扩展至像元尺度上,为土壤盐分信息的遥感提取提供了科学参考。
【Objective】Soil salinization, one of the most critical ecological problems in agriculture,is a progressive soil degradation process that reduces soil quality and hence crop yield and agricultural production. Therefore, it is necessary to monitor soil salinity for prevention and mitigation of land degradation in the arid regions. Producers and decision-makers also require updated accurate soil salinity maps of agronomically and environmentally relevant regions. 【Method】A total of 147 soil samples were collected from the Ebinur Lake wetland, Xinjiang Uyghur Autonomous Region of China, during the rainy season(May) and dry season(September) in 2017 for analysis of electrical conductivity(EC) when prepared into suspensions, 1∶5 in soil and distilled water ratio, and for acquisition of Vis-NIR(visible-near infrared)reflectance spectra in the laboratory. Spectra were resampled in line with Landsat8 OLI sensor's resolution,i.e., band 1(Coastal) 433～453 nm, band 2(Blue) 450～515 nm, band 3(Green) 525～600 nm, band 4(Red) 630～680 nm, band 5(Red) 845～885 nm, band 6(SWIR 1) 1 560～1 660 nm, and band 6(SWIR7) 2 100～2 300 nm. Furthermore, NDSI(Normalized Difference Vegetation Index), SI(Salinity Index),SI1(Salinity Index 1), SI2(Salinity Index 2), SI3(Salinity Index 3), S1(Salinity Index, S1)、S2(Salinity Index), S3(Salinity Index), S5(Salinity Index), S6(Salinity Index), Int1(Intensity Index 1), Int2(Intensity index 2) and COSRI(Combined Spectral Response Index) were also calculated in this study. A quantitative estimating model was constructed based on partial least squares regression(PLSR), and evaluated in light of its root mean square error(RMSE), determination coefficient(R2) and ratio of performance to deviation(RPD). 【Result】Results show that the surface soil of the Ebinur lake wetland was strongly salt-affected,with soil salinity during the rainy season(23.90 mS·cm~(-1)) being much higher than that during the dry season(11.62 mS·cm~(-1)). 2) The PLSR model based on resampled spectral data and 13 spectral indexes performed quite ideally in predicting soil EC in the study area, with quite high accuracy(R2 = 0.91, RMSE= 6.48 mS·cm~(-1), and RPD = 2.45), which indicates that the model constructed in the study could be used to predict quantitatively EC in the Ebinur Lake wetland; and 3), the areas of slightly saline soil and saline soil decreased, while those of moderately and heavily salinized soils increased during the study period(from May to September). 【Conclusion】In the present study, the model, established by combining two types of remote sensing data different in resolution, has obviously improved the traditional optical remote sensing(Landsat8 OLI) model in precison, as well as elevated the Vis-NIR spectral data to the pixel scale,thus providing certain scientific reference for remote sensing extraction of soil salinity information. The performance of Vis-NIR-based prediction of soil salinity might be affected by adsorption capacity of soluble salts in these electromagnetic ranges being lower than that of: water, soil iron, organic matter, certain types of clay minerals, and some other soil components. To further improve the prediction accuracy, further efforts should be done to define the most dominated factor affecting spectral reflectance of soils different in salinity degree.
【Objective】Soil salinization, one of the most critical ecological problems in agriculture,is a progressive soil degradation process that reduces soil quality and hence crop yield and agricultural production. Therefore, it is necessary to monitor soil salinity for prevention and mitigation of land degradation in the arid regions. Producers and decision-makers also require updated accurate soil salinity maps of agronomically and environmentally relevant regions. 【Method】A total of 147 soil samples were collected from the Ebinur Lake wetland, Xinjiang Uyghur Autonomous Region of China, during the rainy season(May) and dry season(September) in 2017 for analysis of electrical conductivity(EC) when prepared into suspensions, 1∶5 in soil and distilled water ratio, and for acquisition of Vis-NIR(visible-near infrared)reflectance spectra in the laboratory. Spectra were resampled in line with Landsat8 OLI sensor's resolution,i.e., band 1(Coastal) 433～453 nm, band 2(Blue) 450～515 nm, band 3(Green) 525～600 nm, band 4(Red) 630～680 nm, band 5(Red) 845～885 nm, band 6(SWIR 1) 1 560～1 660 nm, and band 6(SWIR7) 2 100～2 300 nm. Furthermore, NDSI(Normalized Difference Vegetation Index), SI(Salinity Index),SI1(Salinity Index 1), SI2(Salinity Index 2), SI3(Salinity Index 3), S1(Salinity Index, S1)、S2(Salinity Index), S3(Salinity Index), S5(Salinity Index), S6(Salinity Index), Int1(Intensity Index 1), Int2(Intensity index 2) and COSRI(Combined Spectral Response Index) were also calculated in this study. A quantitative estimating model was constructed based on partial least squares regression(PLSR), and evaluated in light of its root mean square error(RMSE), determination coefficient(R2) and ratio of performance to deviation(RPD). 【Result】Results show that the surface soil of the Ebinur lake wetland was strongly salt-affected,with soil salinity during the rainy season(23.90 mS·cm~(-1)) being much higher than that during the dry season(11.62 mS·cm~(-1)). 2) The PLSR model based on resampled spectral data and 13 spectral indexes performed quite ideally in predicting soil EC in the study area, with quite high accuracy(R2 = 0.91, RMSE= 6.48 mS·cm~(-1), and RPD = 2.45), which indicates that the model constructed in the study could be used to predict quantitatively EC in the Ebinur Lake wetland; and 3), the areas of slightly saline soil and saline soil decreased, while those of moderately and heavily salinized soils increased during the study period(from May to September). 【Conclusion】In the present study, the model, established by combining two types of remote sensing data different in resolution, has obviously improved the traditional optical remote sensing(Landsat8 OLI) model in precison, as well as elevated the Vis-NIR spectral data to the pixel scale,thus providing certain scientific reference for remote sensing extraction of soil salinity information. The performance of Vis-NIR-based prediction of soil salinity might be affected by adsorption capacity of soluble salts in these electromagnetic ranges being lower than that of: water, soil iron, organic matter, certain types of clay minerals, and some other soil components. To further improve the prediction accuracy, further efforts should be done to define the most dominated factor affecting spectral reflectance of soils different in salinity degree.
引文
[1]Metternicht G I, Zinck J A. Remote sensing of soil salinity:Potentials and constraints. Remote Sensing of Environment, 2003,85(1):1-20
[2]Gorji T, Sertel E, Tanik A. Monitoring soil salinity via remote sensing technology under data scarce conditions:A case study from Turkey. Ecological Indicators, 2017, 74:384-391
[3]李晓明,杨劲松,杨奇勇.基于电磁感应的南疆典型田块土壤盐分空间异质性研究.水土保持学报,2011, 25(1):167-170Li X M, Yang J S, Yang Q Y. Spatial heterogeneity of soil salinity in a typical field in south Xinjiang based on electromagnetic induction(In Chinese). Journal of Soil and Water Conservation, 2011, 25(1):167-170
[4]Ma L G, Ma F L, Li J D, et al. Characterizing and modeling regional-scale variations in soil salinity in the arid oasis of Tarim Basin, China. Geoderma,2017, 305:1-11
[5]Scudiero F, Skaggs T H, Corwin D L. Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA. Geoderma Regional, 2014, 2:82-90
[6]Ding J L, Yu D L. Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan-Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments.Geoderma, 2014, 235:316-322
[7]丁建丽,姚远,王飞.干旱区土壤盐渍化特征空间建模.生态学报,2014,34(16):4620-4631Ding J L, Yao Y, Wang F. Detecting soil salinization in arid regions using spectral feature space derived from remote sensing data(In Chinese). Acta Ecologica Sinica, 2014, 34(16):4620-4631
[8]李新国,李和平,任云霞,等.开都河流域下游绿洲土壤盐渍化特征及其光谱分析.土壤通报,2012, 43(1):166-170Li X G, Li H P, Ren Y X, et al. Analysis on the characteristics of the Oasis soil salinization and soil spectrum in the lower reaches of Kaidu River Basin,Xinjiang(In Chinese). Chinese Journal of Soil Science, 2012, 43(1):166-170
[9]Nawar S, Buddenbaum H, Hill J, et al. Modeling and mapping of soil salinity with reflectance spectroscopy and Landsat data using two quantitative methods(PLSR and MARS). Remote Sensing,2014, 6(11):10813-10834
[10]Meng L, Zhou S, Zhang H, et al. Estimating soil salinity in different landscapes of the Yellow River Delta through Landsat OLI/TIRS and ETM+Data.Journal of Coastal Conservation, 2016, 20(4):271-279
[11]Wang X P, Zhang F, Ding J L, et al. Estimation of soil salt content(SSC)in the Ebinur Lake Wetland National Nature Reserve(ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices. Science of the Total Environment, 2018, 615:918-930
[12]厉彦玲,赵庚星,常春艳,等.OLI与HSI影像融合的土壤盐分反演模型.农业工程学报,2017, 33(21):173-180Li Y L, Zhao G X, Chang C Y, et al. Soil salinity retrieval model based on OLI and HSI image fusion(In Chinese). Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(21):173-180
[13]王爽,丁建丽,王璐,等.基于地表光谱建模的区域土壤盐渍化遥感监测研究.干旱区地理,2016, 39(1):190-198Wang S,Ding J L, Wang L,et al. Remote sensing monitoring of soil salinization based on surface spectral modeling(In Chinese). Arid Land Geography,2016, 39(1):190-198
[14]王明宽,莫宏伟,陈红艳.基于多光谱影像反演土壤盐分的建模方法研究.土壤通报,2016, 47(5):1036-1041Wang M K, Mo H W, Chen H Y. Study on model method of inversion of soil salt based on multispectral image(In Chinese). Chinese Journal of Soil Science,2016,47(5):1036-1041
[15]Wang J,Zhang F, Kung H, et al. Linkage analysis of land use/cover patterns and hydro-chemical characteristics in different seasons in Ebinur Lake Watershed, China. Water, 2017, 9(11):888
[16]任岩,张飞,王娟,等.新疆艾比湖流域地表水丰水期和枯水期水质分异特征及污染源解析.湖泊科学,2017, 29(5):1143-1157Ren Y, Zhang F, Wang J, et al. Spatio-temporal characteristics and source identification of surface water pollutants in Lake Ebinur Watershed, Xinjiang(In Chinese). Journal of Lake Sciences, 2017, 29(5):1143-1157
[17]杨雅楠,阿里木江·卡斯木.“一带一路”背景下新疆城镇交通优势度与区域经济发展水平的关系分析.干旱区地理,2017,40(3):680-691Yang Y N,Alimujiang-Kasimu. Relationship between regional transportation advantage and development of regional economy in Xinjiang under the background of the national strategy of the Belt and Road(In Chinese). Arid Land Geography, 2017, 40(3):680-691
[18]杨爱霞,丁建丽.新疆艾比湖湿地土壤有机碳含量的光谱测定方法对比.农业工程学报,2015,31(18):162-168Yang A X,Ding J L. Comparative assessment of two methods for estimation of soil organic carbon content by Vis-NIR spectra in Xinjiang Ebinur Lake Wetland(In Chinese). Transactions of the Chinese Society of Agricultural Engineering, 2015,31(18):162-168
[19]鄢雪英,丁建丽,李鑫,等.艾比湖湿地退化对盐尘暴发生及运移路径的影响.生态学报,2015, 35(17):5856-5865Yan X Y, Ding J L,Li X,et al. Effect of salt dust storm migration pathways on degradation of Ebinur Lake Wetland(In Chinese). Acta Ecologica Sinica,2015, 35(17):5856-5865
[20]秦璐,傅德平,杨军,等.艾比湖湿地自然保护区典型群落土壤理化性质分析.干旱区资源与环境,2011, 25(8):144-151Qin L,F u D P,Yang J, et al. The soil physiochemical properties of typical communities in Ebinur Lake Wetlands nature reserve(In Chinese).Journal of Arid Land Resources and Environment,2011, 25(8):144-151
[21]徐涵秋,唐菲.新一代Landsat系列卫星:Landsat 8遥感影像新增特征及其生态环境意义.生态学报,2013, 33(11):3249-3257Xu H Q, Tang F. Analysis of new characteristics of the first Landsat 8 image and their eco-environmental significance(In Chinese). Acta Ecologica Sinica,2013, 33(11):3249-3257
[22]Fourati H T,Bouaziz M,Benzina M, et al. Modeling of soil salinity within a semi-arid region using spectral analysis. Arabian Journal of Geosciences, 2015,8(12):11175-11182
[23]曹雷,丁建丽,玉米提·哈力克,等.基于国产高分一号卫星数据的区域土壤盐渍化信息提取与建模.土壤学报,2016,53(6):1399-1409Cao L,Ding J L, Umut Halik,et al. Extraction and modeling of regional soil salinization based on data from GF-1 satellite(In Chinese). Acta Pedologica Sinica, 2016, 53(6):1399-1409
[24]Allbed A, Kumar L,Aldakheel Y Y. Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:Applications in a date palm dominatedregion. Geoderma, 2014,230:1-8
[25]Wang J Z, Ding J L,Abulimiti A,et al. Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared(VISNIR)spectroscopy, Ebinur Lake Wetland, Northwest China. PeerJ,2018,6:e4703
[26]Nawar S,Buddenbaum H, Hill J. Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy:A case study from Egypt. Arabian Journal of Geosciences, 2015, 8(7):5127-5140
[27]Scudiero E,Skaggs T H,Corwin D L. Regional-scale soil salinity assessment using Landsat ETM+canopy reflectance. Remote Sensing of Environment, 2015,169:335-343
[28]胡江玲,武胜利,金海龙,等.艾比湖流域近48年来降水变化特征分析.干旱区资源与环境,2010, 24(9):94-99Hu J L, Wu S L,Jin H L,et al. Precipitation it Aibi Lake Basin of Xinjiang from 1961 to 2008(In Chinese). Journal of Arid Land Resource and Environment, 2010, 24(9):94-99
[29]Arnous M O,Green D R. Monitoring and assessing waterlogged and salt-affected areas in the Eastern Nile Delta region,Egypt, using remotely sensed multi-temporal data and GIS. Journal of Coastal Conservation, 2015, 19(3):369-391
[30]朱海强,李艳红,李发东.艾比湖湿地典型植物群落土壤酶活性季节变化特征.应用生态学报,2017, 28(4):1145-1154Zhu H Q,Li Y H, Li F D. Seasonal variations of soil enzyme activity in typical plant communities in the Ebinur Lake wetland(In Chinese). Chinese Journal of Applied Ecology,2017,28(4):1145-1154
[2]Gorji T, Sertel E, Tanik A. Monitoring soil salinity via remote sensing technology under data scarce conditions:A case study from Turkey. Ecological Indicators, 2017, 74:384-391
[3]李晓明,杨劲松,杨奇勇.基于电磁感应的南疆典型田块土壤盐分空间异质性研究.水土保持学报,2011, 25(1):167-170Li X M, Yang J S, Yang Q Y. Spatial heterogeneity of soil salinity in a typical field in south Xinjiang based on electromagnetic induction(In Chinese). Journal of Soil and Water Conservation, 2011, 25(1):167-170
[4]Ma L G, Ma F L, Li J D, et al. Characterizing and modeling regional-scale variations in soil salinity in the arid oasis of Tarim Basin, China. Geoderma,2017, 305:1-11
[5]Scudiero F, Skaggs T H, Corwin D L. Regional scale soil salinity evaluation using Landsat 7, western San Joaquin Valley, California, USA. Geoderma Regional, 2014, 2:82-90
[6]Ding J L, Yu D L. Monitoring and evaluating spatial variability of soil salinity in dry and wet seasons in the Werigan-Kuqa Oasis, China, using remote sensing and electromagnetic induction instruments.Geoderma, 2014, 235:316-322
[7]丁建丽,姚远,王飞.干旱区土壤盐渍化特征空间建模.生态学报,2014,34(16):4620-4631Ding J L, Yao Y, Wang F. Detecting soil salinization in arid regions using spectral feature space derived from remote sensing data(In Chinese). Acta Ecologica Sinica, 2014, 34(16):4620-4631
[8]李新国,李和平,任云霞,等.开都河流域下游绿洲土壤盐渍化特征及其光谱分析.土壤通报,2012, 43(1):166-170Li X G, Li H P, Ren Y X, et al. Analysis on the characteristics of the Oasis soil salinization and soil spectrum in the lower reaches of Kaidu River Basin,Xinjiang(In Chinese). Chinese Journal of Soil Science, 2012, 43(1):166-170
[9]Nawar S, Buddenbaum H, Hill J, et al. Modeling and mapping of soil salinity with reflectance spectroscopy and Landsat data using two quantitative methods(PLSR and MARS). Remote Sensing,2014, 6(11):10813-10834
[10]Meng L, Zhou S, Zhang H, et al. Estimating soil salinity in different landscapes of the Yellow River Delta through Landsat OLI/TIRS and ETM+Data.Journal of Coastal Conservation, 2016, 20(4):271-279
[11]Wang X P, Zhang F, Ding J L, et al. Estimation of soil salt content(SSC)in the Ebinur Lake Wetland National Nature Reserve(ELWNNR), Northwest China, based on a Bootstrap-BP neural network model and optimal spectral indices. Science of the Total Environment, 2018, 615:918-930
[12]厉彦玲,赵庚星,常春艳,等.OLI与HSI影像融合的土壤盐分反演模型.农业工程学报,2017, 33(21):173-180Li Y L, Zhao G X, Chang C Y, et al. Soil salinity retrieval model based on OLI and HSI image fusion(In Chinese). Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(21):173-180
[13]王爽,丁建丽,王璐,等.基于地表光谱建模的区域土壤盐渍化遥感监测研究.干旱区地理,2016, 39(1):190-198Wang S,Ding J L, Wang L,et al. Remote sensing monitoring of soil salinization based on surface spectral modeling(In Chinese). Arid Land Geography,2016, 39(1):190-198
[14]王明宽,莫宏伟,陈红艳.基于多光谱影像反演土壤盐分的建模方法研究.土壤通报,2016, 47(5):1036-1041Wang M K, Mo H W, Chen H Y. Study on model method of inversion of soil salt based on multispectral image(In Chinese). Chinese Journal of Soil Science,2016,47(5):1036-1041
[15]Wang J,Zhang F, Kung H, et al. Linkage analysis of land use/cover patterns and hydro-chemical characteristics in different seasons in Ebinur Lake Watershed, China. Water, 2017, 9(11):888
[16]任岩,张飞,王娟,等.新疆艾比湖流域地表水丰水期和枯水期水质分异特征及污染源解析.湖泊科学,2017, 29(5):1143-1157Ren Y, Zhang F, Wang J, et al. Spatio-temporal characteristics and source identification of surface water pollutants in Lake Ebinur Watershed, Xinjiang(In Chinese). Journal of Lake Sciences, 2017, 29(5):1143-1157
[17]杨雅楠,阿里木江·卡斯木.“一带一路”背景下新疆城镇交通优势度与区域经济发展水平的关系分析.干旱区地理,2017,40(3):680-691Yang Y N,Alimujiang-Kasimu. Relationship between regional transportation advantage and development of regional economy in Xinjiang under the background of the national strategy of the Belt and Road(In Chinese). Arid Land Geography, 2017, 40(3):680-691
[18]杨爱霞,丁建丽.新疆艾比湖湿地土壤有机碳含量的光谱测定方法对比.农业工程学报,2015,31(18):162-168Yang A X,Ding J L. Comparative assessment of two methods for estimation of soil organic carbon content by Vis-NIR spectra in Xinjiang Ebinur Lake Wetland(In Chinese). Transactions of the Chinese Society of Agricultural Engineering, 2015,31(18):162-168
[19]鄢雪英,丁建丽,李鑫,等.艾比湖湿地退化对盐尘暴发生及运移路径的影响.生态学报,2015, 35(17):5856-5865Yan X Y, Ding J L,Li X,et al. Effect of salt dust storm migration pathways on degradation of Ebinur Lake Wetland(In Chinese). Acta Ecologica Sinica,2015, 35(17):5856-5865
[20]秦璐,傅德平,杨军,等.艾比湖湿地自然保护区典型群落土壤理化性质分析.干旱区资源与环境,2011, 25(8):144-151Qin L,F u D P,Yang J, et al. The soil physiochemical properties of typical communities in Ebinur Lake Wetlands nature reserve(In Chinese).Journal of Arid Land Resources and Environment,2011, 25(8):144-151
[21]徐涵秋,唐菲.新一代Landsat系列卫星:Landsat 8遥感影像新增特征及其生态环境意义.生态学报,2013, 33(11):3249-3257Xu H Q, Tang F. Analysis of new characteristics of the first Landsat 8 image and their eco-environmental significance(In Chinese). Acta Ecologica Sinica,2013, 33(11):3249-3257
[22]Fourati H T,Bouaziz M,Benzina M, et al. Modeling of soil salinity within a semi-arid region using spectral analysis. Arabian Journal of Geosciences, 2015,8(12):11175-11182
[23]曹雷,丁建丽,玉米提·哈力克,等.基于国产高分一号卫星数据的区域土壤盐渍化信息提取与建模.土壤学报,2016,53(6):1399-1409Cao L,Ding J L, Umut Halik,et al. Extraction and modeling of regional soil salinization based on data from GF-1 satellite(In Chinese). Acta Pedologica Sinica, 2016, 53(6):1399-1409
[24]Allbed A, Kumar L,Aldakheel Y Y. Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries:Applications in a date palm dominatedregion. Geoderma, 2014,230:1-8
[25]Wang J Z, Ding J L,Abulimiti A,et al. Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared(VISNIR)spectroscopy, Ebinur Lake Wetland, Northwest China. PeerJ,2018,6:e4703
[26]Nawar S,Buddenbaum H, Hill J. Estimation of soil salinity using three quantitative methods based on visible and near-infrared reflectance spectroscopy:A case study from Egypt. Arabian Journal of Geosciences, 2015, 8(7):5127-5140
[27]Scudiero E,Skaggs T H,Corwin D L. Regional-scale soil salinity assessment using Landsat ETM+canopy reflectance. Remote Sensing of Environment, 2015,169:335-343
[28]胡江玲,武胜利,金海龙,等.艾比湖流域近48年来降水变化特征分析.干旱区资源与环境,2010, 24(9):94-99Hu J L, Wu S L,Jin H L,et al. Precipitation it Aibi Lake Basin of Xinjiang from 1961 to 2008(In Chinese). Journal of Arid Land Resource and Environment, 2010, 24(9):94-99
[29]Arnous M O,Green D R. Monitoring and assessing waterlogged and salt-affected areas in the Eastern Nile Delta region,Egypt, using remotely sensed multi-temporal data and GIS. Journal of Coastal Conservation, 2015, 19(3):369-391
[30]朱海强,李艳红,李发东.艾比湖湿地典型植物群落土壤酶活性季节变化特征.应用生态学报,2017, 28(4):1145-1154Zhu H Q,Li Y H, Li F D. Seasonal variations of soil enzyme activity in typical plant communities in the Ebinur Lake wetland(In Chinese). Chinese Journal of Applied Ecology,2017,28(4):1145-1154
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