#arcgis — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #arcgis, aggregated by home.social.
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What's New in #ArcGIS Maps SDKs for Native Apps 300.1
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What's New in #ArcGIS Maps SDKs for Native Apps 300.1
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What's New in #ArcGIS Maps SDKs for Native Apps 300.1
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What's New in #ArcGIS Maps SDKs for Native Apps 300.1
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What's New in #ArcGIS Maps SDKs for Native Apps 300.1
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Meshes vs. Gaussian Splats in #ArcGIS Reality Studio https://tinyurl.com/mryjat89
#3D #dataviz #modeling #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esriaec @esritraining @urisa
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Meshes vs. Gaussian Splats in #ArcGIS Reality Studio https://tinyurl.com/mryjat89
#3D #dataviz #modeling #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esriaec @esritraining @urisa
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Meshes vs. Gaussian Splats in #ArcGIS Reality Studio https://tinyurl.com/mryjat89
#3D #dataviz #modeling #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esriaec @esritraining @urisa
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Meshes vs. Gaussian Splats in #ArcGIS Reality Studio https://tinyurl.com/mryjat89
#3D #dataviz #modeling #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esriaec @esritraining @urisa
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Meshes vs. Gaussian Splats in #ArcGIS Reality Studio https://tinyurl.com/mryjat89
#3D #dataviz #modeling #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esriaec @esritraining @urisa
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What new #ArcGISEnterprise Administrators need to see first https://tinyurl.com/ms4pasfu via #Onneer
#ArcGISMonitor #observability #GIS #esri #arcgis #GISchat #geospatial @esri @esrifederalgovt @esrislgov @arcgisxprise @esritraining @urisa
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What new #ArcGISEnterprise Administrators need to see first https://tinyurl.com/ms4pasfu via #Onneer
#ArcGISMonitor #observability #GIS #esri #arcgis #GISchat #geospatial @esri @esrifederalgovt @esrislgov @arcgisxprise @esritraining @urisa
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What new #ArcGISEnterprise Administrators need to see first https://tinyurl.com/ms4pasfu via #Onneer
#ArcGISMonitor #observability #GIS #esri #arcgis #GISchat #geospatial @esri @esrifederalgovt @esrislgov @arcgisxprise @esritraining @urisa
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What new #ArcGISEnterprise Administrators need to see first https://tinyurl.com/ms4pasfu via #Onneer
#ArcGISMonitor #observability #GIS #esri #arcgis #GISchat #geospatial @esri @esrifederalgovt @esrislgov @arcgisxprise @esritraining @urisa
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What new #ArcGISEnterprise Administrators need to see first https://tinyurl.com/ms4pasfu via #Onneer
#ArcGISMonitor #observability #GIS #esri #arcgis #GISchat #geospatial @esri @esrifederalgovt @esrislgov @arcgisxprise @esritraining @urisa
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PSA to anyone using the #ArcGIS #ParcelFabric :
I know I've posted something like this before, but it still boggles my mind how misleading the parcel fabric was.
We started with misaligned data, used the topology tools to, we thought, clean things up.
If not for yourself, for your downstream data consumers. Every now and then, take a look at the *raw data*.
Each vertex on this curve is supposed to be a single node, but it's really just tight clusters of multiples.
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PSA to anyone using the #ArcGIS #ParcelFabric :
I know I've posted something like this before, but it still boggles my mind how misleading the parcel fabric was.
We started with misaligned data, used the topology tools to, we thought, clean things up.
If not for yourself, for your downstream data consumers. Every now and then, take a look at the *raw data*.
Each vertex on this curve is supposed to be a single node, but it's really just tight clusters of multiples.
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PSA to anyone using the #ArcGIS #ParcelFabric :
I know I've posted something like this before, but it still boggles my mind how misleading the parcel fabric was.
We started with misaligned data, used the topology tools to, we thought, clean things up.
If not for yourself, for your downstream data consumers. Every now and then, take a look at the *raw data*.
Each vertex on this curve is supposed to be a single node, but it's really just tight clusters of multiples.
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PSA to anyone using the #ArcGIS #ParcelFabric :
I know I've posted something like this before, but it still boggles my mind how misleading the parcel fabric was.
We started with misaligned data, used the topology tools to, we thought, clean things up.
If not for yourself, for your downstream data consumers. Every now and then, take a look at the *raw data*.
Each vertex on this curve is supposed to be a single node, but it's really just tight clusters of multiples.
-
PSA to anyone using the #ArcGIS #ParcelFabric :
I know I've posted something like this before, but it still boggles my mind how misleading the parcel fabric was.
We started with misaligned data, used the topology tools to, we thought, clean things up.
If not for yourself, for your downstream data consumers. Every now and then, take a look at the *raw data*.
Each vertex on this curve is supposed to be a single node, but it's really just tight clusters of multiples.
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Resources for Migrating from #ArcGIS #WebAppBuilder to #Experience Builder https://tinyurl.com/3e25mbeh
#apps #GIS #esri #mapping #maps #ArcGISApps #GISchat #geospatial @esri @esrifederalgovt @esrislgov @arcgisapps @esritraining @urisa
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Resources for Migrating from #ArcGIS #WebAppBuilder to #Experience Builder https://tinyurl.com/3e25mbeh
#apps #GIS #esri #mapping #maps #ArcGISApps #GISchat #geospatial @esri @esrifederalgovt @esrislgov @arcgisapps @esritraining @urisa
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Resources for Migrating from #ArcGIS #WebAppBuilder to #Experience Builder https://tinyurl.com/3e25mbeh
#apps #GIS #esri #mapping #maps #ArcGISApps #GISchat #geospatial @esri @esrifederalgovt @esrislgov @arcgisapps @esritraining @urisa
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Resources for Migrating from #ArcGIS #WebAppBuilder to #Experience Builder https://tinyurl.com/3e25mbeh
#apps #GIS #esri #mapping #maps #ArcGISApps #GISchat #geospatial @esri @esrifederalgovt @esrislgov @arcgisapps @esritraining @urisa
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Resources for Migrating from #ArcGIS #WebAppBuilder to #Experience Builder https://tinyurl.com/3e25mbeh
#apps #GIS #esri #mapping #maps #ArcGISApps #GISchat #geospatial @esri @esrifederalgovt @esrislgov @arcgisapps @esritraining @urisa
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Introducing #ArcGIS Linear Referencing https://tinyurl.com/2jcczd5x
#location #spatial #intelligence #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esriblog @esriaec @esritraining @arcgispro @urisa
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Introducing #ArcGIS Linear Referencing https://tinyurl.com/2jcczd5x
#location #spatial #intelligence #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esriblog @esriaec @esritraining @arcgispro @urisa
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Introducing #ArcGIS Linear Referencing https://tinyurl.com/2jcczd5x
#location #spatial #intelligence #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esriblog @esriaec @esritraining @arcgispro @urisa
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Introducing #ArcGIS Linear Referencing https://tinyurl.com/2jcczd5x
#location #spatial #intelligence #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esriblog @esriaec @esritraining @arcgispro @urisa
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Introducing #ArcGIS Linear Referencing https://tinyurl.com/2jcczd5x
#location #spatial #intelligence #GIS #esri #mapping #GISchat #geospatial @esri @esrifederalgovt @esrislgov @esriblog @esriaec @esritraining @arcgispro @urisa
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Batch Geocoding Service In #ArcGIS Pro
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Batch Geocoding Service In #ArcGIS Pro
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Batch Geocoding Service In #ArcGIS Pro
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Batch Geocoding Service In #ArcGIS Pro
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Batch Geocoding Service In #ArcGIS Pro
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How #ArcGISPro multiscale surface tools reveal the hidden #seafloor structures that affect #ocean circulation https://tinyurl.com/msxnmvzn
#water #marine #oceanography #imagery #RemoteSensing #GIS #esri #arcgis #mapping @esri @gisandscience @esritraining @urisa
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How #ArcGISPro multiscale surface tools reveal the hidden #seafloor structures that affect #ocean circulation https://tinyurl.com/msxnmvzn
#water #marine #oceanography #imagery #RemoteSensing #GIS #esri #arcgis #mapping @esri @gisandscience @esritraining @urisa
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How #ArcGISPro multiscale surface tools reveal the hidden #seafloor structures that affect #ocean circulation https://tinyurl.com/msxnmvzn
#water #marine #oceanography #imagery #RemoteSensing #GIS #esri #arcgis #mapping @esri @gisandscience @esritraining @urisa
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How #ArcGISPro multiscale surface tools reveal the hidden #seafloor structures that affect #ocean circulation https://tinyurl.com/msxnmvzn
#water #marine #oceanography #imagery #RemoteSensing #GIS #esri #arcgis #mapping @esri @gisandscience @esritraining @urisa
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How #ArcGISPro multiscale surface tools reveal the hidden #seafloor structures that affect #ocean circulation https://tinyurl.com/msxnmvzn
#water #marine #oceanography #imagery #RemoteSensing #GIS #esri #arcgis #mapping @esri @gisandscience @esritraining @urisa
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Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
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https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
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https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
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H/T@ Gaurav Parajulim
“[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
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“Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
#GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal -
Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
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https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
--
https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
--
H/T@ Gaurav Parajulim
“[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
--
“Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
#GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal -
Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
--
https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
--
https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
--
H/T@ Gaurav Parajulim
“[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
--
“Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
#GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal -
Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
--
https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
--
https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
--
H/T@ Gaurav Parajulim
“[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
--
“Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
#GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal -
Impact Of Urbanization Driven Land Use And Land Cover Change On Ecological Environmental Quality In Rupandehi Nepal Assessed Using The Remote Sensing Ecological Index
--
https://doi.org/10.1007/s44288-026-00650-y <-- shared paper
--
https://kathmandupost.com/money/2026/02/18/rupandehi-s-continued-urban-sprawl-comes-at-a-cost-for-agriculture-in-the-periphery <-- shared media article
--
H/T@ Gaurav Parajulim
“[The authors] studied how the ecological quality of Nepal's Rupandehi District has changed over three decades (1993–2023), using satellite imagery and the Remote Sensing Ecological Index (RSEI) to track the health of the landscape year by year and to understand how urbanization-driven land use change has reshaped it.
What [they] found tells a nuanced story: as Butwal and Bhairahawa grew and built-up land expanded, ecological quality shifted in ways that a single number can't capture, some areas recovered, others declined, and the patterns rarely moved in a straight line…”
--
“Rapid urbanization and population growth are major drivers of land use and land cover (LULC) change and can substantially alter ecological environmental quality (EEQ). This study assessed the spatiotemporal dynamics of LULC and their effect on EEQ in Rupandehi District, Nepal, over a 30-year period (1993–2023). Four ecological indicators representing greenness, wetness, dryness, and heat were derived from Landsat imagery in Google Earth Engine (GEE), and LULC was classified using a Support Vector Machine (SVM). The Remote Sensing Ecological Index (RSEI) was then constructed from these indicators using Principal Component Analysis (PCA) in ArcGIS Pro, and its spatial structure was examined using global and local spatial autocorrelation. The mean RSEI followed a non-linear trajectory, rising from 0.59 in 1993 to 0.635 in 2004, declining to 0.55 in 2013, and recovering to 0.67 in 2023, indicating an overall improvement in EEQ with a temporary mid-period decline. Over the same period, built-up areas expanded substantially and agricultural land declined, whereas forest cover fluctuated but showed a slight net increase by 2023, and barren land decreased markedly. Higher EEQ was concentrated in the forested northern hills, while lower values occurred in the urban centers of Butwal and Bhairahawa, closely matching the spatial pattern of LULC change. The results indicate that ecological quality reflects the combined influence of all land cover classes rather than any single class. This study provides a transferable and reproducible workflow for long-term ecological assessment based on openly available Landsat data, with the analysis code shared in a public repository, offering practical guidance for sustainable land management and environmentally responsible urban development...”
#GIS #spatial #mapping #RemoteSensing #GIS #RSEI #EnvironmentalMonitoring #Nepal #Research #GoogleEarthEngine #ArcGIS #EcologicalQuality #spatialautocorrelation #ecology #environment #earthobservation #RemoteSensingEcologicalIndex #landscape #urbanisation #urban #development #landuse #change #spatialanalysis #spatiotemporal