The study explores the intricate interplay between land use land cover(LULC),normalized difference vegetation index(NDVI),and land surface temperature(LST)within the Lower Son River Basin in India from 1991 to 2020.Th...The study explores the intricate interplay between land use land cover(LULC),normalized difference vegetation index(NDVI),and land surface temperature(LST)within the Lower Son River Basin in India from 1991 to 2020.The region’s ecological balance has been increasingly strained due to rapid urbanization and changing land use patterns.Through a combination of Landsat TM&OLI/TIRS satellite imageries and geospatial analysis techniques,this study unveils the intricate connection between land use and land cover changes,vegetation,and land surface temperature variations.The study area is classified into three altitudinal zones(Zone Ⅰ:39–300 m,Zone Ⅱ:301–600 m and Zone Ⅲ:601–1,247 m)to examine the changes in depth.The area has seen significant changes in LULC,vegetation and LST in all the three altitudinal zones.The findings hold significant implications for sustainable land management and environmental conservation strategies in the Lower Son River Basin.As per the result,103,438 ha of vegetation was converted into agriculture land and 82,572 ha of agricultural land was transformed into settlements from 1991 to 2020.This trend shows human pressure on the land resource in the study area.Minor increase in water body is seen which is attributed to commissioning of Bansagar dam.Zone Ⅰ has seen highest settlement growth while Zone Ⅲ experienced severe deforestation of around 15%.Zone Ⅱ and Ⅲ needs attention for holistic sustenance.Analysis of LST shows that it has increased by 0.82℃ from 1991 to 2020 which is a red flag.The study underscores the critical importance of balanced land use practices to preserve ecological integrity and mitigate the adverse effects of urbanization and climate change.展开更多
该文采用M OD IS N DV I时序数据对东北区土地覆盖分类进行研究,以验证M OD IS区域土地覆盖制图的可靠性。通过试验发现经过Sav izky-G o lay滤波处理能有效去除云、缺失数据及异常值的影响,使得N DV I时序曲线能更好的反映植被季相变...该文采用M OD IS N DV I时序数据对东北区土地覆盖分类进行研究,以验证M OD IS区域土地覆盖制图的可靠性。通过试验发现经过Sav izky-G o lay滤波处理能有效去除云、缺失数据及异常值的影响,使得N DV I时序曲线能更好的反映植被季相变化特征,分类结果表明N DV I时序数列能较好的区分植被与非植被、草本(一年生)与木本(多年生)覆盖类型。但研究区内一年一熟的农作物与高盖度草地、落叶针叶林与落叶阔叶林具有相似的物候特征,混分现象比较严重。该研究通过添加地表温度(land surface tem perature,LST)数据解决这一问题,利用所得温度/植被指数TV I对研究区进行土地覆盖分类。所得结果用363个野外调查样区进行验证,N DV I及TV I时序数据的分类精度分别为62.26%与71.63%。结果表明TV I比N DV I对土地覆盖类型中的植被类型识别更有效。展开更多
文摘The study explores the intricate interplay between land use land cover(LULC),normalized difference vegetation index(NDVI),and land surface temperature(LST)within the Lower Son River Basin in India from 1991 to 2020.The region’s ecological balance has been increasingly strained due to rapid urbanization and changing land use patterns.Through a combination of Landsat TM&OLI/TIRS satellite imageries and geospatial analysis techniques,this study unveils the intricate connection between land use and land cover changes,vegetation,and land surface temperature variations.The study area is classified into three altitudinal zones(Zone Ⅰ:39–300 m,Zone Ⅱ:301–600 m and Zone Ⅲ:601–1,247 m)to examine the changes in depth.The area has seen significant changes in LULC,vegetation and LST in all the three altitudinal zones.The findings hold significant implications for sustainable land management and environmental conservation strategies in the Lower Son River Basin.As per the result,103,438 ha of vegetation was converted into agriculture land and 82,572 ha of agricultural land was transformed into settlements from 1991 to 2020.This trend shows human pressure on the land resource in the study area.Minor increase in water body is seen which is attributed to commissioning of Bansagar dam.Zone Ⅰ has seen highest settlement growth while Zone Ⅲ experienced severe deforestation of around 15%.Zone Ⅱ and Ⅲ needs attention for holistic sustenance.Analysis of LST shows that it has increased by 0.82℃ from 1991 to 2020 which is a red flag.The study underscores the critical importance of balanced land use practices to preserve ecological integrity and mitigate the adverse effects of urbanization and climate change.
文摘该文采用M OD IS N DV I时序数据对东北区土地覆盖分类进行研究,以验证M OD IS区域土地覆盖制图的可靠性。通过试验发现经过Sav izky-G o lay滤波处理能有效去除云、缺失数据及异常值的影响,使得N DV I时序曲线能更好的反映植被季相变化特征,分类结果表明N DV I时序数列能较好的区分植被与非植被、草本(一年生)与木本(多年生)覆盖类型。但研究区内一年一熟的农作物与高盖度草地、落叶针叶林与落叶阔叶林具有相似的物候特征,混分现象比较严重。该研究通过添加地表温度(land surface tem perature,LST)数据解决这一问题,利用所得温度/植被指数TV I对研究区进行土地覆盖分类。所得结果用363个野外调查样区进行验证,N DV I及TV I时序数据的分类精度分别为62.26%与71.63%。结果表明TV I比N DV I对土地覆盖类型中的植被类型识别更有效。
