Application of Remote Sensing and GIS in Monitoring Forest Cover Changes in Vietnam Based on Natural Zoning DOI Creative Commons
An Nguyen, V.F. Kovyazin,

Cong Pham

и другие.

Land, Год журнала: 2025, Номер 14(5), С. 1037 - 1037

Опубликована: Май 9, 2025

Forest cover changes monitoring in Vietnam has been conducted using remote sensing (RS) and geographic information systems (GIS). Given Vietnam’s diverse climate, this study focused on the Thanh Hoa, Kon Tum, Dong Nai provinces due to their distinct natural conditions forest structures. Land was classified into five categories: broadleaf forests, mixed shrubland/grassland/agricultural land, non-forested areas, water bodies. RS data processing performed Google Earth Engine (GEE), with land classification via Random algorithm. The findings revealed significant between 2010 2020. In forests expanded by 51.15% (91,159 ha), while declined 19.68% (105,445 ha). Tum experienced reductions both (20.05%, 26,685 ha) (4.06%, 20,501 Meanwhile, recorded increases (29.15%, 23,263 (12.17%, 20,632 study’s reliability confirmed a Kappa coefficient of 0.81–0.89. To predict changes, two methods—the CA-Markov model MOLUSCE module—were compared. Results demonstrated that module achieved higher accuracy, deviations from actual 1.61, 1.14, 1.80 for Nai, respectively, whereas yielded larger (8.79, 6.29, 5.03). Future projections 2030, generated MOLUSCE, suggest impacts agricultural expansion, deforestation, restoration efforts area. This highlights advantages GIS complex sustainable management Vietnam.

Язык: Английский

Multi‐Scenario Simulation of Land Use Changes and Their Ecological Risk in the Global Largest Inland Arid Urban Agglomeration DOI Open Access

Xiaojuan Zhi,

Xiaojun Song, Jing Ma

и другие.

Land Degradation and Development, Год журнала: 2025, Номер unknown

Опубликована: Март 12, 2025

ABSTRACT Rapid global urbanization had significantly altered land use (LU), threatening the ecology and sustainability of arid regions. Systematic forward‐looking analyses changes (LUCs) ecological risks in Asia's zones, particularly urban agglomeration on northern slope Tianshan Mountains (UANSTM), remained limited. Herein, LUCs UANSTM under four scenarios, including ecology‐economy balanced development scenario (EES), protection (EPS), economic (EDS), natural (NDS) 2030, was predicted by employing PLUS model multi‐objective programming (MOP) model. Then, an evaluation system developed from dimensions expansion, risk, food demand, degradation to assess corresponding risk each case. The results showed that: (1) Under scenario, desert bare grassland were found be main LU modes UANSTM, with a significant increase cultivated negligible change water forest; (2) area decreased NDS while areas grassland, forest land, construction increased other especially unused grassland; (3) LU‐induced these scenarios similarities, overall high risks. Among them, 52.04% at relatively high‐risk levels, only 2.97% low‐risk levels. This study reveals diversified different thereby facilitating individualized planning environmental restoration UANSTM.

Язык: Английский

Процитировано

0
A seasonal assessment of urban thermal behavior and its links to land use patterns in Harare, Zimbabwe DOI Creative Commons
DMSLB Dissanayake, Takehiro Morimoto, Manjula Ranagalage

и другие.

Scientific African, Год журнала: 2025, Номер unknown, С. e02677 - e02677

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Multi-Scenario Simulation and Assessment of Ecological Security Patterns: A Case Study of Poyang Lake Eco-Economic Zone DOI Open Access

Yuke Song,

Mangen Li,

Linghua Duo

и другие.

Sustainability, Год журнала: 2025, Номер 17(9), С. 4017 - 4017

Опубликована: Апрель 29, 2025

Ecological security is integral to national strategies, making the construction of ecological patterns essential for mitigating risks. However, predictive research on (ESPs) remains limited. This study integrates Patch-generating Land Use Simulation (PLUS) model with pattern analysis provide scientific insights into spatial governance and optimization in Poyang Lake Economic Zone (PLEEZ). First, PLUS simulated land use changes 2030 under three scenarios: natural development (ND), economic (ED), protection (EP). Based these projections, were constructed using Integrated Valuation Ecosystem Services Tradeoffs (InVEST) model, Morphological Spatial Pattern Analysis (MSPA) method, Conefor 2.6, Minimum Cumulative Resistance (MCR) resistance theory. The results indicate: (1) 19, 18, 21 source areas identified different scenarios, covering 6093.16 km2, 5973.21 6702.56 respectively, 9, 8, 10 important sites, primarily north. (2) 37, 35, 43 corridors delineated, exhibiting a spiderweb-like distribution. (3) 94, 62, 107 pinch points 116, 121, 104 barrier detected. Node Aggregation Area was as critical zone targeted restoration. Finally, zoning management strategy “Four Cores, Two Zones, One Belt” proposed. offers valuable sustainable planning risk mitigation.

Язык: Английский

Процитировано

0
Application of Remote Sensing and GIS in Monitoring Forest Cover Changes in Vietnam Based on Natural Zoning DOI Creative Commons
An Nguyen, V.F. Kovyazin,

Cong Pham

и другие.

Land, Год журнала: 2025, Номер 14(5), С. 1037 - 1037

Опубликована: Май 9, 2025

Forest cover changes monitoring in Vietnam has been conducted using remote sensing (RS) and geographic information systems (GIS). Given Vietnam’s diverse climate, this study focused on the Thanh Hoa, Kon Tum, Dong Nai provinces due to their distinct natural conditions forest structures. Land was classified into five categories: broadleaf forests, mixed shrubland/grassland/agricultural land, non-forested areas, water bodies. RS data processing performed Google Earth Engine (GEE), with land classification via Random algorithm. The findings revealed significant between 2010 2020. In forests expanded by 51.15% (91,159 ha), while declined 19.68% (105,445 ha). Tum experienced reductions both (20.05%, 26,685 ha) (4.06%, 20,501 Meanwhile, recorded increases (29.15%, 23,263 (12.17%, 20,632 study’s reliability confirmed a Kappa coefficient of 0.81–0.89. To predict changes, two methods—the CA-Markov model MOLUSCE module—were compared. Results demonstrated that module achieved higher accuracy, deviations from actual 1.61, 1.14, 1.80 for Nai, respectively, whereas yielded larger (8.79, 6.29, 5.03). Future projections 2030, generated MOLUSCE, suggest impacts agricultural expansion, deforestation, restoration efforts area. This highlights advantages GIS complex sustainable management Vietnam.

Язык: Английский

Процитировано

0