The relative productivity index: Mapping human impacts on rangeland vegetation productivity with quantile regression forests

Ecological Indicators, Journal Year: 2025, Volume and Issue: 171, P. 113208 - 113208

Published: Feb. 1, 2025

Language: Английский

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VPM v3.0 model: improved estimates of terrestrial gross primary production from individual eddy flux tower sites to the globe

Journal of Remote Sensing, Journal Year: 2025, Volume and Issue: 5

Published: Jan. 1, 2025

Accurate estimation of gross primary production (GPP) terrestrial vegetation is crucial for comprehending the carbon dynamics. To date, there still no consensus on magnitude and seasonality global GPP among major products, underscoring necessity to improve models higher accuracy estimates. Here, we introduce an improved Vegetation Photosynthesis Model (VPM v3.0), which incorporates site-specific apparent optimum temperature photosynthesis, leaf-trait-based light absorption (flat leaf vs. needle leaf), water stress estimation. The VPM simulation driven by Moderate Resolution Imaging Spectroradiometer images ERA5-Land climate dataset. We evaluate v3.0 using from 205 eddy flux tower sites across 11 land cover types (1,658 site-years) (GPP EC ), as well TROPOspheric monitoring instrument (TROPOMI) solar-induced fluorescence (SIF) product 2018 2021. slope, R 2 , root mean square error between VPM-v3 ) are 0.97, 0.78, 1.46 gC m −2 day −1 respectively. shows high temporal consistency with TROPOMI SIF. provides estimates at most evaluated than v2.0. Comparisons other products reveal both spatial–temporal discrepancies. These findings clearly indicate in estimating GPP, making it suitable generating datasets.

Language: Английский

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Spatiotemporal Changes in Vegetation Cover during the Growing Season and Its Implications for Chinese Grain for Green Program in the Luo River Basin

Forests, Journal Year: 2024, Volume and Issue: 15(9), P. 1649 - 1649

Published: Sept. 19, 2024

The Grain for Green Program (GFGP) plays a critical role in enhancing watershed vegetation cover. Analyzing changes cover provides significant practical value guiding ecological conservation and restoration vulnerable regions. This study utilizes MOD13Q1 NDVI data to construct the Kernel Normalized Difference Vegetation Index (kNDVI) analyzes spatiotemporal evolution future trends of from 2000 2020, covering key periods GFGP. innovatively combines optimal parameter geographic detector with constraint lines comprehensively reveal nonlinear constraints, intensities, thresholds imposed by various driving factors on kNDVI. results indicate that following: (1) Luo River Basin increased significantly between noticeable increase percentage high-quality vegetation. Spatially, followed pattern being “high southwest low northeast”, 73.69% region displaying improved conditions. Future degradation is predicted threaten 59.40% region, showing continuous or declining trend. (2) primary are evapotranspiration, elevation, population density, geomorphology type, temperatures GDP secondary factors. Dual-factor enhancement was observed interactions among factors, evapotranspiration density having largest interaction (q = 0.76). (3) effects exhibited patterns, existing hump-shaped concave-waved types. stability kNDVI 40.23% areas showed moderate high fluctuations, most fluctuations low-altitude high-temperature areas, as well those impacted dense human activities. (4) By overlaying classifications GFGP priority reforestation totaling 68.27 km2 were identified. findings can help decisionmakers optimize next phase effective regional management.

Language: Английский

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A 2001–2022 global gross primary productivity dataset using an ensemble model based on the random forest method

Biogeosciences, Journal Year: 2024, Volume and Issue: 21(19), P. 4285 - 4300

Published: Oct. 2, 2024

Abstract. Advancements in remote sensing technology have significantly contributed to the improvement of models for estimating terrestrial gross primary productivity (GPP). However, discrepancies spatial distribution and interannual variability within GPP datasets pose challenges a comprehensive understanding carbon cycle. In contrast previous that rely on environmental variables, we developed an ensemble model based random forest method (denoted ERF model). This used outputs from established models: Eddy Covariance Light Use Efficiency (EC-LUE), estimate Kernel Normalized Difference Vegetation Index (GPP-kNDVI), Near-Infrared Reflectance (GPP-NIRv), Revised-EC-LUE, Photosynthesis Model (VPM), Moderate Resolution Imaging Spectroradiometer (MODIS). These were as inputs GPP. The demonstrated superior performance, explaining 85.1 % monthly variations at 170 sites surpassing performance selected (67.7 %–77.5 %) independent using variables (81.5 %). Additionally, improved accuracy across each month with various subranges, mitigating issue “high-value underestimation low-value overestimation” estimates. Over period 2001 2022, global estimated by was 132.7 PgC yr−1, increasing trend 0.42 yr−2, which is comparable or slightly better than other mainstream terms validation results observations FLUXNET (i.e., ChinaFLUX). Importantly, growing number datasets, our study provides way integrate these may lead more reliable

Language: Английский

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The Relative Productivity Index: Mapping Human Impacts on Rangeland Vegetation Productivity with Quantile Regression Forests

Published: Jan. 1, 2024

Language: Английский

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