Analysing the Spatial and Temporal Characteristics of Ecological Land Encroachment by Cropland Expansion and Its Drivers in Cambodia DOI Creative Commons

Danni Su,

Kun Yang, Zongqi Peng

et al.

Land, Journal Year: 2024, Volume and Issue: 13(12), P. 2195 - 2195

Published: Dec. 16, 2024

The rapid expansion of cropland in Cambodia, the world’s seventh-largest rice exporter, has created an imbalance land use structure. However, there is a lack quantitative investigation loss ecological as result and its drivers. In this research, spatial autocorrelation, landscape pattern index transfer matrix methods were used based on data from 2000 to 2023. Then, eXtreme Gradient Boosting-SHapley Additive exPlanations (XGBoost-SHAP) Geographic Detector explore drivers expansion. findings indicate that expanse agricultural Cambodia significantly increased by 13.47%. proportion area (37.87%) close forest (40.19%). Cultivated dominated fields, supplemented drylands. Spatial clustering obvious both drylands fields. Drylands are mainly concentrated eastern western mountainous areas northern border, while fields central plains. encroached total 30,579.27km2 land, which 62.88% was dry 37.12% Forests shrubs main source cropland. addition, soil type (0.18), elevation (0.17) GDP (0.17), population (0.52) their interactions strongly drove dryland should conduct scientific research assess demand for growth economic progress. It realize orderly cultivated reduce damage promote coordinated development society, environment economy.

Language: Английский

Interpretable flash flood susceptibility mapping in Yarlung Tsangpo River Basin using H2O Auto-ML DOI Creative Commons

Fei He,

Suxia Liu, Xingguo Mo

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Jan. 11, 2025

Flash flood susceptibility mapping is essential for identifying areas prone to flooding events and aiding decision-makers in formulating effective prevention measures. This study aims evaluate the flash Yarlung Tsangpo River Basin (YTRB) using multiple machine learning (ML) models facilitated by H2O automated ML platform. The best-performing model was used generate a map, its interpretability analyzed Shapley Additive Explanations (SHAP) tree interpretation method. results revealed that top four models, including both single ensemble demonstrated high accuracy tests. map generated eXtreme Randomized Trees (XRT) showed 8.92%, 12.95%, 15.42%, 31.34%, 31.37% of area exhibited very high, moderate, low, low susceptibility, respectively, with approximately 74.9% historical floods occurring classified as moderate susceptibility. SHAP plot identified topographic factors primary drivers floods, importance analysis ranking most influential such descending order DEM, wetness index, position normalized difference vegetation average multi-year precipitation. demonstrates benefits interpretable learning, which can provide guidance mitigation.

Language: Английский

Citations

1
SpatioTemporal Random Forest and SpatioTemporal Stacking Tree: A novel spatially explicit ensemble learning approach to modeling non-linearity in spatiotemporal non-stationarity DOI Creative Commons
Yun Luo, Shiliang Su

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 136, P. 104315 - 104315

Published: Dec. 12, 2024

Language: Английский

Citations

4
Configuration of public transportation stations in Hong Kong based on population density prediction by machine learning DOI Creative Commons
Yinghua Ji, Hao Zheng

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 136, P. 104339 - 104339

Published: Jan. 13, 2025

Language: Английский

Citations

0
Land Use and Land Cover Change Dynamics in the Niger Delta Region of Nigeria from 1986 to 2024 DOI Creative Commons

Obroma O. Agumagu,

Rob Marchant, Lindsay C. Stringer

et al.

Land, Journal Year: 2025, Volume and Issue: 14(4), P. 765 - 765

Published: April 3, 2025

Land Use and Cover Change (LULCCs) shapes catchment dynamics is a key driver of hydrological risks, affecting responses as vegetated land replaced with urban developments cultivated land. The resultant risks are likely to become more critical in the future climate changes becomes increasingly variable. Understanding effects LULCC vital for developing management strategies reducing adverse on cycle environment. This study examines Niger Delta Region (NDR) Nigeria from 1986 2024. A supervised maximum likelihood classification was applied Landsat 5 TM 8 OLI images 1986, 2015, Five use classes were classified: Water bodies, Rainforest, Built-up, Agriculture, Mangrove. overall accuracy Kappa coefficients 93% 0.90, 91% 0.87, 84% 0.79 2024, respectively. Between built-up agriculture areas substantially increased by about 8229 6727 km2 (561% 79%), respectively, concomitant decrease mangrove vegetation 14,350 10,844 (−54% −42%), spatial distribution across NDR states varied, Delta, Bayelsa, Cross River, Rivers States experiencing highest rainforest, losses 64%, 55, 44%, 44% (5711 km2, 3554 2250 1297 km2), NDR’s mangroves evidently under serious threat. has important implications, particularly given role played forests regulating hazards. dramatic rainforest could exacerbate climate-related impacts. provides quantitative information that be used support planning practices well sustainable development.

Language: Английский

Citations

0
AI Analytics for Carbon-Neutral City Planning: A Systematic Review of Applications DOI Creative Commons
Cong Cong, Jessica Page, Yoonshin Kwak

et al.

Urban Science, Journal Year: 2024, Volume and Issue: 8(3), P. 104 - 104

Published: Aug. 1, 2024

Artificial intelligence (AI) has become a transformative force across various disciplines, including urban planning. It unprecedented potential to address complex challenges. An essential task is facilitate informed decision making regarding the integration of constantly evolving AI analytics into planning research and practice. This paper presents review how methods are applied in studies, focusing particularly on carbon neutrality We highlight already being used generate new scientific knowledge interactions between human activities nature. consider conditions which advantages AI-enabled studies can positively influence decision-making outcomes. also importance interdisciplinary collaboration, responsible governance, community engagement guiding data-driven suggest contribute supporting carbon-neutrality goals.

Language: Английский

Citations

3
Spatiotemporal Effects and Optimization Strategies of Land-Use Carbon Emissions at the County Scale: A Case Study of Shaanxi Province, China DOI Open Access
Yahui Zhang, Jianfeng Li, Siqi Liu

et al.

Sustainability, Journal Year: 2024, Volume and Issue: 16(10), P. 4104 - 4104

Published: May 14, 2024

Land use, as one of the major sources carbon emissions, has profound implications for global climate change. County-level land-use systems play a critical role in national emission management and control. Consequently, it is essential to explore spatiotemporal effects optimization strategies emissions at county scale promote achievement regional dual targets. This study, focusing on Shaanxi Province, analyzed characteristics land use from 2000 2020. By establishing evaluation model, county-level were clarified. Utilizing Geodetector K-means clustering methods, driving mechanisms elucidated, explored. The results showed that during 2000–2020, Province underwent significant changes, with constructed increasing by 97.62%, while cultivated grassland substantially reduced. overall exhibited pattern North > Central South. total within province increased nearly fourfold over 20 years, reaching 1.00 × 108 tons. Constructed was primary source forest contributed significantly sink study area. Interactions among factors had impacts spatial differentiation emissions. For counties different types differentiated recommended. Low-carbon should intensify ecological protection rational utilization, medium-carbon need strike balance between economic development environmental protection, high-carbon prioritize reduction structural transformation.

Language: Английский

Citations

2
Comprehensive evaluation of land-use carbon emissions integrating social network analysis and a zone-based machine learning approach DOI
Houbao Fan, Xinmin Zhang, Xiao Zhou

et al.

Environmental Impact Assessment Review, Journal Year: 2024, Volume and Issue: 112, P. 107775 - 107775

Published: Dec. 21, 2024

Language: Английский

Citations

2
Predicting CO 2 emissions through partitioned land use change simulations considering urban hierarchy DOI Creative Commons
Jing Yang, Haotian Lu, Zaiyang Ma

et al.

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1)

Published: Sept. 22, 2024

Language: Английский

Citations

1
Spatiotemporal Evolution and Driving Factors of Land Use Carbon Emissions in Jiangxi Province, China DOI Open Access
Fei Dai, Mingjin Zhan, Xinxin Chen

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(10), P. 1825 - 1825

Published: Oct. 19, 2024

Analyzing the spatiotemporal changes and influencing factors of carbon emissions generated by land use is great importance for improving structure promoting regional low-carbon economic development. This study, based on remote sensing statistical yearbook data from 1995 to 2020, calculated in Jiangxi Province, China. Multiple spatial analysis methods logarithmic mean Divisia index were used elucidate evolution driving emissions, findings revealed following: (1) The Province during 1995–2020 substantial as forest accounted 65% entire area, while construction increased 98.1%. Cultivated decreased most, followed land. (2) There was a fourfold rise driven primarily land, northern areas produced higher compared with central southern regions. Forest main sink. (3) Economic development (257.36%) impact proportion (211.31%) primary contributing increase use, other had inhibitory effects. study transformed macroscale strategy cities into targeted local policies, research theories adopted could provide scientific reference regions urgent need reduction worldwide.

Language: Английский

Citations

1
Net Forest Carbon Loss Induced by Forest Cover Change and Compound Drought and Heat Events in Two Regions of China DOI Open Access

Chenfeng Gu,

Tongyu Wang, Wenjuan Shen

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 2048 - 2048

Published: Nov. 20, 2024

Compound drought and heat events (CDHEs) forest cover change influence regional carbon dynamics. Changes in vegetation biomass soil storage induced by often exhibit considerable uncertainty, previous research on the impacts of CDHEs dynamics is limited. To accurately quantify specific effects different regions, we employed a combined algorithm Carnegie–Ames–Stanford Approach (CASA) bookkeeping empirical models to examine impact changes during 2000–2022 Nanjing Shaoguan, Southern China. Using Geographical Detector model, then analyzed Next, used photosynthesis equation optimal response time forests (heat) calculate sequestration caused both regions 2000–2022. The results indicated that afforestation deforestation led +0.269 TgC +1.509 0.491 2.802 emissions respectively. overall were manifested as net loss. In Nanjing, loss (0.186 TgC) was lower than due (0.222 TgC). (3.219 much more significant (1.293 This study demonstrated are dominated factors which provides scientific basis for local governments formulate targeted management policies.

Language: Английский

Citations

1