Mapping rangeland health indicators in eastern Africa from 2000 to 2022 DOI Creative Commons
Gerardo E. Soto,

Steven W. Wilcox,

Patrick E. Clark

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(11), P. 5375 - 5404

Published: Nov. 26, 2024

Abstract. Tracking environmental change is important to ensure efficient and sustainable natural resources management. Eastern Africa dominated by arid semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most people. Despite several mapping efforts, eastern lacks accurate reliable high-resolution maps health necessary many management, policy, research purposes. Earth observation data offer opportunity assess spatiotemporal dynamics in conditions at much higher spatial temporal coverage than conventional approaches, which rely on situ methods, while also complementing their accuracy. Using machine learning classification linear unmixing, we produced indicators – Landsat-based time series from 2000 2022 30 m resolution land cover classes (LCCs) vegetation fractional (VFC; including photosynthetic vegetation, non-photosynthetic bare ground) two assets deriving metrics Africa. Due scarcity measurements large, remote, highly heterogeneous landscape, an algorithm was developed combine WorldView-2 WorldView-3 satellite imagery < 2 resolutions with a limited set ground observations generate reference labels across study region using visual photo-interpretation. The LCC yielded overall accuracy 0.856 when comparing predictions our validation dataset comprised mixture photo-interpretation imagery, kappa 0.832; VFC returned R2=0.795, p 2.2×10-16, normalized root mean squared error (nRMSE) = 0.123 predicted bare-ground fractions photo-interpreted imagery. Our products represent first multi-decadal Landsat-resolution specifically designed monitoring rangelands Kenya, Ethiopia, Somalia, covering total area 745 840 km2. These can be valuable wide range development, humanitarian, ecological conservation efforts are available https://doi.org/10.5281/zenodo.7106166 (Soto et al., 2023) Google Engine (GEE; details “Data availability” section).

Language: Английский

Dust Intensity Across Vegetation Types in Mongolia: Drivers and Trends DOI Creative Commons
Chunling Bao, Yonghui Yang, Hasi Bagan

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 410 - 410

Published: Jan. 25, 2025

Dust storms, characterized by their rapid movement and high intensity, present significant challenges across atmospheric, human health, ecological domains. This study investigates the spatiotemporal variations in dust intensity (DI) its driving factors Mongolia from 2001 to 2022, using data ground observations, reanalysis, remote sensing satellites, statistical analyses. Our findings show an increasing DI trend at approximately two-thirds of monitoring stations, with rising average rate 0.8 per year during period. Anthropogenic dominate as primary drivers regions such Forest, Meadow Steppe, Typical Desert Gobi Desert. For example, GDP significantly impacts Forest Steppe areas, contributing 25.89% 14.11% influencing DI, respectively. Population emerges key driver Grasslands (20.77%), (26.65%), (37.66%). Conversely, climate change is dominant factor Alpine southern–central Hangay Uul, temperature (20.69%) relative humidity (20.67%) playing critical roles. These insights are vital for Mongolian authorities: promoting green economic initiatives could mitigate economically active regions, while adaptation strategies essential climate-sensitive Meadows. The also provide valuable guidance addressing environmental issues other arid semi-arid worldwide.

Language: Английский

Citations

1
Drought and bush encroachment threaten dry rangeland sustainability in Northeastern Ethiopia DOI Creative Commons
Minyahel Tilahun,

Zenghui Liu,

Ayana Angassa

et al.

Global Ecology and Conservation, Journal Year: 2025, Volume and Issue: unknown, P. e03425 - e03425

Published: Jan. 1, 2025

Language: Английский

Citations

0
Heterogeneous land surface phenology challenges the comparison among PlanetScope, HLS, and VIIRS detections in semi-arid rangelands DOI
Yuxia Liu, Xiaoyang Zhang, Khuong H. Tran

et al.

Agricultural and Forest Meteorology, Journal Year: 2025, Volume and Issue: 366, P. 110497 - 110497

Published: March 11, 2025

Language: Английский

Citations

0
Resilience through Relationships: Evaluating rangeland governance structures in semi-arid Tafresh county DOI

Leila Shariatyniya,

M Ghorbani, Hossein Azarnivand

et al.

Journal of Arid Environments, Journal Year: 2025, Volume and Issue: 229, P. 105397 - 105397

Published: May 3, 2025

Language: Английский

Citations

0
Mapping rangeland health indicators in eastern Africa from 2000 to 2022 DOI Creative Commons
Gerardo E. Soto,

Steven W. Wilcox,

Patrick E. Clark

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(11), P. 5375 - 5404

Published: Nov. 26, 2024

Abstract. Tracking environmental change is important to ensure efficient and sustainable natural resources management. Eastern Africa dominated by arid semi-arid rangeland systems, where extensive grazing of livestock represents the primary livelihood for most people. Despite several mapping efforts, eastern lacks accurate reliable high-resolution maps health necessary many management, policy, research purposes. Earth observation data offer opportunity assess spatiotemporal dynamics in conditions at much higher spatial temporal coverage than conventional approaches, which rely on situ methods, while also complementing their accuracy. Using machine learning classification linear unmixing, we produced indicators – Landsat-based time series from 2000 2022 30 m resolution land cover classes (LCCs) vegetation fractional (VFC; including photosynthetic vegetation, non-photosynthetic bare ground) two assets deriving metrics Africa. Due scarcity measurements large, remote, highly heterogeneous landscape, an algorithm was developed combine WorldView-2 WorldView-3 satellite imagery < 2 resolutions with a limited set ground observations generate reference labels across study region using visual photo-interpretation. The LCC yielded overall accuracy 0.856 when comparing predictions our validation dataset comprised mixture photo-interpretation imagery, kappa 0.832; VFC returned R2=0.795, p 2.2×10-16, normalized root mean squared error (nRMSE) = 0.123 predicted bare-ground fractions photo-interpreted imagery. Our products represent first multi-decadal Landsat-resolution specifically designed monitoring rangelands Kenya, Ethiopia, Somalia, covering total area 745 840 km2. These can be valuable wide range development, humanitarian, ecological conservation efforts are available https://doi.org/10.5281/zenodo.7106166 (Soto et al., 2023) Google Engine (GEE; details “Data availability” section).

Language: Английский

Citations

1