Spatiotemporal patterns of annual clear-cutting distribution in tropical and subtropical regions of China with time series Landsat and CCDC

Geo-spatial Information Science, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 18

Published: Jan. 17, 2025

Language: Английский

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Mapping built infrastructure in semi-arid systems using data integration and open-source approaches for image classification

Remote Sensing Applications Society and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 101472 - 101472

Published: Jan. 1, 2025

Language: Английский

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Crop Residue Burning in North‐Western India: Emission Estimation and Uncertainty Quantification

Journal of Geophysical Research Atmospheres, Journal Year: 2025, Volume and Issue: 130(4)

Published: Feb. 12, 2025

Abstract Air quality in India faces significant risk from agricultural residue burning, especially Punjab and Haryana, which are pivotal to the world's second‐largest agrarian economy. This study quantifies emissions post‐monsoon biomass burning (10 October–30 November 2022) these states using VIIRS fire detection data Sentinel‐2‐derived burnt areas. Ground validation via district‐level surveys aligns with findings of our study. Results show 51% total crop area was burned (14,700 km 2 Punjab; 8,300 Haryana), leading substantial PM 2.5 (54.28 Gg; 7.94 Gg), CH 4 (25.63 3.75 CO (1,100.3 195.7 NH 3 (0.83 0.15 SO (0.68 0.12 (62.1 11.04 Gg). Emissions about 6.5 times higher than Haryana attributable greater (∼14,700 ), yield, elevated residue‐to‐crop ratios. Compared VIIRS, Sentinel‐2 provides approximately 3.6 emission estimates, reflecting improved detection. District‐level variations underscore influence diverse farming practices, weather, management. An uncertainty analysis, derived multiple estimates methodologies, highlights regional disparities: exhibits highest both CO, respectively, showing least. Understanding uncertainties is vital for forecasting air pollution downwind cities such as New Delhi formulating targeted mitigation strategies.

Language: Английский

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Automatic Mapping of 10 m Tropical Evergreen Forest Cover in Central African Republic with Sentinel-2 Dynamic World Dataset

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 722 - 722

Published: Feb. 19, 2025

Tropical evergreen forests represent the richest biodiversity in terrestrial ecosystems, and fine spatial-temporal resolution mapping of these is essential for study conservation this vital natural resource. The current methods tropical frequently exhibit coarse spatial lengthy production cycles. This can be attributed to inherent challenges associated with monitoring diverse surface changes persistence cloudy, rainy conditions tropics. We propose a novel approach automatically map annual 10 m forest covers from 2017 2023 Sentinel-2 Dynamic World dataset biodiversity-rich conservation-sensitive Central African Republic (CAR). Copernicus Global Land Cover Layers (CGLC) Forest Change (GFC) products were used first track stable samples. Then, initial cover maps generated by determining threshold each yearly median probability maps. From 2023, modified finally produced NEFI (Non-Evergreen Index) images estimated thresholds. results proposed method achieved an overall accuracy >94.10% Cohen’s Kappa >87.63% across all years (F1-Score > 94.05%), which represents significant improvement over performance previous methods, including CGLC based on World. Our findings demonstrate that provides detailed characteristics time-series change Republic, substantial consistency years.

Language: Английский

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Using Landsat 8 and 9 operational land imager (OLI) data to characterize geometric distortion and improve geometric correction of Landsat Multispectral Scanner (MSS) imagery

Remote Sensing of Environment, Journal Year: 2025, Volume and Issue: 321, P. 114679 - 114679

Published: Feb. 26, 2025

Language: Английский

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Time series of Landsat-based bimonthly and annual spectral indices for continental Europe for 2000–2022

Earth system science data, Journal Year: 2025, Volume and Issue: 17(2), P. 741 - 772

Published: Feb. 26, 2025

Abstract. The production and evaluation of the analysis-ready cloud-optimized (ARCO) data cube for continental Europe (including Ukraine, UK, Türkiye), derived from Landsat dataset version 2 (ARD V2) produced by Global Land Analysis Discovery (GLAD) team covering period 2000 to 2022, is described. consists 17 TB at a 30 m resolution includes bimonthly, annual, long-term spectral indices on various thematic topics, including surface reflectance bands, normalized difference vegetation index (NDVI), soil adjusted (SAVI), fraction absorbed photosynthetically active radiation (FAPAR), snow (NDSI), water (NDWI), tillage (NDTI), minimum (minNDTI), bare (BSF), number seasons (NOS), crop duration ratio (CDR). was developed with intention provide comprehensive feature space environmental modeling mapping. quality time series assessed (1) assessing accuracy gap-filled bimonthly artificially created gaps; (2) visual examination artifacts inconsistencies; (3) plausibility checks ground survey data; (4) predictive tests, examples organic carbon (SOC) land cover (LC) classification. reconstruction demonstrates high accuracy, root mean squared error (RMSE) smaller than 0.05, R2 higher 0.6, across all bands. indicates that product complete consistent, except winter periods in northern latitudes high-altitude areas, where cloud density introduce significant gaps hence many remain. check further shows logically statistically capture processes. BSF showed strong negative correlation (−0.73) coverage data, while minNDTI had moderate positive (0.57) Eurostat practice data. detailed temporal characteristics provided different tiers predictors this proved be important both regression LC classification experiments based 60 723 LUCAS observations: (tier 4) were particularly valuable mapping SOC LC, coming out top variable importance assessment. Crop-specific (NOS CDR) limited value tested applications, possibly due noise or insufficient quantification methods. made available https://doi.org/10.5281/zenodo.10776891 (Tian et al., 2024) under CC-BY license will continuously updated.

Language: Английский

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Landsat Program

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

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Spatiotemporal evolution of vegetation phenology and its response to environmental factors in the upper and middle reaches of the Yellow River Basin

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 380, P. 124970 - 124970

Published: March 15, 2025

Language: Английский

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Satellite Data in Agricultural and Environmental Economics: Theory and Practice

Agricultural Economics, Journal Year: 2025, Volume and Issue: unknown

Published: March 16, 2025

ABSTRACT Agricultural and environmental economists are in the fortunate position that a lot of what is happening on ground observable from space. Most agricultural production happens open one can see space when where innovations adopted, crop yields change, or forests converted to pastures, name just few examples. However, converting remotely sensed images into measurements particular variable not trivial, as there more pitfalls nuances than “meet eye”. Overall, however, research benefits tremendously advances available satellite data well complementary tools, such cloud‐based platforms, machine learning algorithms, econometric approaches. Our goal here provide with an accessible introduction working data, show‐case applications, discuss solutions, emphasize best practices. This supported by extensive supporting information, we describe how create different variables, common workflows, discussion required resources skills. Last but least, example reproducible codes made online.

Language: Английский

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Corporate Biodiversity and Water Impact and Risk: Seven Key Principles for Leveraging Insights From Satellite Remote Sensing

Earth s Future, Journal Year: 2025, Volume and Issue: 13(3)

Published: March 1, 2025

Abstract Amid unprecedented biodiversity loss and water scarcity, calls for corporate responsibility are becoming louder have led to emerging non‐financial disclosure frameworks with demanding data needs. While the role of satellite remote sensing (RS) is highly anticipated address needs boost transparency, critical thought on what feasible how strategically integrate its insights ambitious lagging behind. To this, we propose applying a systems perspective represent complex, multi‐scale interactions between biodiversity, systems, operations, guide RS contributions analyze full spectrum impacts risks—ranging from direct concurrent cascading, cumulative, emergent. We highlight seven guiding (non‐exhaustive) principles leveraging assess risks. This process requires an effective system boundary (1) set spatially, temporally, process‐wise. Within which, water's multi‐dimensionality (2) be addressed monitor spatio‐temporal dynamics (3) that characterize ecosystem responses. attribute risk impact detected changes, need defined by causality (4) directionality (5), ultimately consider compound (6) across commodities, supply chains portfolios, as well cross‐system (7), example, climate change, biodiversity. review each these related challenges individually, providing theory definition, relevant capabilities, research directions. Addressing will crucial harness RS's potential comprehensive strong accountability.

Language: Английский

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