Monitoring changes in Annual Net Primary Productivity(ANPP)is required for reporting on UN Sustainable Development Goal(SDG)Indicator 15.3.1:the proportion of land that is degraded over the total land area.Calibrating...Monitoring changes in Annual Net Primary Productivity(ANPP)is required for reporting on UN Sustainable Development Goal(SDG)Indicator 15.3.1:the proportion of land that is degraded over the total land area.Calibrating time-series observations of ANPP to derive Water Use Efficiency(WUE;a measure of ANPP per unit of evapotranspiration)can minimize the influence of climate factors on ANPP observations and highlight the influence of non-climatic drivers of degradation such as land use changes.Comparing the ANPP and WUE time series may be useful for identifying the primary drivers of land degradation,which could be used to support the Land Degradation Neutrality objectives of the UN Convention to Combat Desertification(UNCCD).This paper presents an algorithm for the Google Earth Engine(freely and openly available upon request-http://doi.org/10.5281/zenodo.4429773)to calculate and compare ANPP and WUE time series for Santa Cruz,Bolivia,which has recently experienced an intensification in its land use.This code builds on the Good Practice Guidance document(ver-sion 1)for monitoring SDG Indicator 15.3.1.We use the MODIS 16-day average,250 m resolution to demonstrate that the Enhanced Vegetation Index(EVI)responds faster to changes in water avail-ability than the Normalized Difference Vegetation Index(NDVI).We also consider the relationships between ANPP and WUE.Significant and concordant trends may highlight good agricultural practices or increased resilience in ecosystem structure and productivity when they are positive or reducing resilience and functional integrity if negative.The sign and significance of the correlation between ANPP and WUE may also diverge over time.With further analysis,it may be possible to interpret this relationship in terms of the drivers of change in plant productivity and ecosystem resilience.展开更多
Land productivity is one of the sub-indicators for measuring SDG 15.3.1.Land Productivity Dynamics(LPD)is the most popular approach for reporting this indicator at the global scale.A major limitation of existing produ...Land productivity is one of the sub-indicators for measuring SDG 15.3.1.Land Productivity Dynamics(LPD)is the most popular approach for reporting this indicator at the global scale.A major limitation of existing products of LPD is the coarse spatial resolution caused by remote sensing data input,which cannot meet the requirement offine-scale land degradation assessment.To resolve this problem,this study developed a tool(HiLPD-GEE)to calculate 30 m LPD by fusing Landsat and MODIS data based on Google Earth Engine(GEE).The tool generates high-quality fused Normalized Difference Vegetation Index(NDVI)dataset for LPD calculation through gapfilling and Savitzky–Golayfiltering(GF-SG)and then uses the method recommended by the European Commission Joint Research Centre(JRC)to calculate LPD.The tool can calculate 30 m LPD in any spatial range within any time window after 2013,supporting global land degradation monitoring.To demonstrate the applicability of this tool,the LPD product was produced for African Great Green Wall(GGW)countries.The analysis proves that the 30 m LPD product generated by HiLPD-GEE could reflect the land productivity change effectively and reflect more spatial details.The results also provide an important insight for the GGW initiative.展开更多
Avoiding,reducing,and reversing land degradation and restoring degraded land is an urgent priority to protect the biodiversity and ecosystem services that are vital to life on Earth.To halt and reverse the current tre...Avoiding,reducing,and reversing land degradation and restoring degraded land is an urgent priority to protect the biodiversity and ecosystem services that are vital to life on Earth.To halt and reverse the current trends in land degradation,there is an immediate need to enhance national capacities to undertake quantitative assessments and mapping of their degraded lands,as required by the Sustainable Development Goals(SDGs),in particular,the SDG indicator 15.3.1(“proportion of land that is degraded over total land area”).Earth Observations(EO)can play an important role both for generating this indicator as well as complementing or enhancing national official data sources.Implementations like Trends.Earth to monitor land degradation in accordance with the SDG15.3.1 rely on default datasets of coarse spatial resolution provided by MODIS or AVHRR.Consequently,there is a need to develop methodologies to benefit from medium to high-resolution satellite EO data(e.g.Landsat or Sentinels).In response to this issue,this paper presents an initial overview of an innovative approach to monitor land degradation at the national scale in compliance with the SDG15.3.1 indicator using Landsat observations using a data cube but further work is required to improve the calculation of the three sub-indicators.展开更多
Changes in land productivity have been endorsed by the Inter Agency Expert Group on Sustainable Development Goals(IAEGSDGs)as key indicators for monitoring SDG 15.3.1.Multiple vegeta-tion parameters from optical remot...Changes in land productivity have been endorsed by the Inter Agency Expert Group on Sustainable Development Goals(IAEGSDGs)as key indicators for monitoring SDG 15.3.1.Multiple vegeta-tion parameters from optical remote sensing techniques have been widely utilized across different land productivity decline processes and scales.However,there is no consensus on indicator selection and their effectiveness at representing land productivity declining at different scales.This study proposes a fusion framework that incorporates the trends and consistencies within the four com-monly used remote sensing-based vegetation indicators.We ana-lyzed the differences among the four vegetation parameters in different land cover and climate zones,finally producing a new global land productivity dynamics(LPD)product with confidence level degrees.The LPD classes indicated by the four vegetation indicators(VIs)showed that all three levels(low,medium,and high confidence)of increasing area account for 23.99%of the global vegetated area and declining area account for 7.00%.The Increase high-confidence(HC)area accounted for 2.77%of the total area,and the Decline-HC accounted for 0.35%of the total area.This study demonstrates the accuracy of the high-confidence(HC)area for the evaluation of land productivity decline and increase.The“forest”landcover type and“humid”climate zone had the largest increasing and declining area but had the lowest high-confidence proportion.The data product provides an important and optional reference for the assessment of SDG 15.3.1 at global and regional scales according to the specific application target.展开更多
基金This study was partially funded by UNDP Grant:BOL/118208(“Laboratorios de Recuperación Temprana”),a study led by Fundación para Conservación del Bosque Chiquitano(www.fcbc.org.bo)to determine forest patches requiring post-fire assisted recovery in the aftermath of 2019 wildfires in Santa Cruz,Bolivia:“Plan Estratégico para la Restauración de lasÁreas Afectadas por los Incendios en el 2019”.
文摘Monitoring changes in Annual Net Primary Productivity(ANPP)is required for reporting on UN Sustainable Development Goal(SDG)Indicator 15.3.1:the proportion of land that is degraded over the total land area.Calibrating time-series observations of ANPP to derive Water Use Efficiency(WUE;a measure of ANPP per unit of evapotranspiration)can minimize the influence of climate factors on ANPP observations and highlight the influence of non-climatic drivers of degradation such as land use changes.Comparing the ANPP and WUE time series may be useful for identifying the primary drivers of land degradation,which could be used to support the Land Degradation Neutrality objectives of the UN Convention to Combat Desertification(UNCCD).This paper presents an algorithm for the Google Earth Engine(freely and openly available upon request-http://doi.org/10.5281/zenodo.4429773)to calculate and compare ANPP and WUE time series for Santa Cruz,Bolivia,which has recently experienced an intensification in its land use.This code builds on the Good Practice Guidance document(ver-sion 1)for monitoring SDG Indicator 15.3.1.We use the MODIS 16-day average,250 m resolution to demonstrate that the Enhanced Vegetation Index(EVI)responds faster to changes in water avail-ability than the Normalized Difference Vegetation Index(NDVI).We also consider the relationships between ANPP and WUE.Significant and concordant trends may highlight good agricultural practices or increased resilience in ecosystem structure and productivity when they are positive or reducing resilience and functional integrity if negative.The sign and significance of the correlation between ANPP and WUE may also diverge over time.With further analysis,it may be possible to interpret this relationship in terms of the drivers of change in plant productivity and ecosystem resilience.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences[grant numbers XDA19090124 and XDA19030104].
文摘Land productivity is one of the sub-indicators for measuring SDG 15.3.1.Land Productivity Dynamics(LPD)is the most popular approach for reporting this indicator at the global scale.A major limitation of existing products of LPD is the coarse spatial resolution caused by remote sensing data input,which cannot meet the requirement offine-scale land degradation assessment.To resolve this problem,this study developed a tool(HiLPD-GEE)to calculate 30 m LPD by fusing Landsat and MODIS data based on Google Earth Engine(GEE).The tool generates high-quality fused Normalized Difference Vegetation Index(NDVI)dataset for LPD calculation through gapfilling and Savitzky–Golayfiltering(GF-SG)and then uses the method recommended by the European Commission Joint Research Centre(JRC)to calculate LPD.The tool can calculate 30 m LPD in any spatial range within any time window after 2013,supporting global land degradation monitoring.To demonstrate the applicability of this tool,the LPD product was produced for African Great Green Wall(GGW)countries.The analysis proves that the 30 m LPD product generated by HiLPD-GEE could reflect the land productivity change effectively and reflect more spatial details.The results also provide an important insight for the GGW initiative.
基金This research was funded by the European Commission“Horizon 2020 Program”ERA-PLANET/GEOEssential project,grant number 689443.
文摘Avoiding,reducing,and reversing land degradation and restoring degraded land is an urgent priority to protect the biodiversity and ecosystem services that are vital to life on Earth.To halt and reverse the current trends in land degradation,there is an immediate need to enhance national capacities to undertake quantitative assessments and mapping of their degraded lands,as required by the Sustainable Development Goals(SDGs),in particular,the SDG indicator 15.3.1(“proportion of land that is degraded over total land area”).Earth Observations(EO)can play an important role both for generating this indicator as well as complementing or enhancing national official data sources.Implementations like Trends.Earth to monitor land degradation in accordance with the SDG15.3.1 rely on default datasets of coarse spatial resolution provided by MODIS or AVHRR.Consequently,there is a need to develop methodologies to benefit from medium to high-resolution satellite EO data(e.g.Landsat or Sentinels).In response to this issue,this paper presents an initial overview of an innovative approach to monitor land degradation at the national scale in compliance with the SDG15.3.1 indicator using Landsat observations using a data cube but further work is required to improve the calculation of the three sub-indicators.
基金was funded by the National Key Research and Development Program of China(No.2016YFC0500806)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Number XDA19090124).
文摘Changes in land productivity have been endorsed by the Inter Agency Expert Group on Sustainable Development Goals(IAEGSDGs)as key indicators for monitoring SDG 15.3.1.Multiple vegeta-tion parameters from optical remote sensing techniques have been widely utilized across different land productivity decline processes and scales.However,there is no consensus on indicator selection and their effectiveness at representing land productivity declining at different scales.This study proposes a fusion framework that incorporates the trends and consistencies within the four com-monly used remote sensing-based vegetation indicators.We ana-lyzed the differences among the four vegetation parameters in different land cover and climate zones,finally producing a new global land productivity dynamics(LPD)product with confidence level degrees.The LPD classes indicated by the four vegetation indicators(VIs)showed that all three levels(low,medium,and high confidence)of increasing area account for 23.99%of the global vegetated area and declining area account for 7.00%.The Increase high-confidence(HC)area accounted for 2.77%of the total area,and the Decline-HC accounted for 0.35%of the total area.This study demonstrates the accuracy of the high-confidence(HC)area for the evaluation of land productivity decline and increase.The“forest”landcover type and“humid”climate zone had the largest increasing and declining area but had the lowest high-confidence proportion.The data product provides an important and optional reference for the assessment of SDG 15.3.1 at global and regional scales according to the specific application target.
