Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2021, Volume and Issue: 103, P. 102475 - 102475

Published: Aug. 8, 2021

Rapid urbanization at the expense of environment led to a reduction in vegetation cover, and consequently aggravated land degradation, urban water logging, heat island effect other effects. Revealing driving mechanism behind use change facilitates deeper insight into human biophysical effects process thereby supports sustainable development. This work proposed margin-based measure random forest for core factor identification change, which mainly included constructed land, bodies, etc., using multitemporal global cover products point-of-interest (POI) data. Taking Wuhan from 2010 2020 as case study, method was employed sort forces 24 factors. The results suggested that more reliable sensitive than traditional importance when detecting change. Meanwhile, both values ranking orders factors measured by were stable regardless similarity chosen applied. findings also showed topographic conditions persistently affected while transportation factors, instead business services, gradually became most important last 10 years.

Language: Английский

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Land Use and Land Cover Mapping Using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A Comparison of Two Composition Methods

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(9), P. 1977 - 1977

Published: April 20, 2022

Accurate and real-time land use/land cover (LULC) maps are important to provide precise information for dynamic monitoring, planning, management of the Earth. With advent cloud computing platforms, time series feature extraction techniques, machine learning classifiers, new opportunities arising in more accurate large-scale LULC mapping. In this study, we aimed at finding out how two composition methods spectral–temporal metrics extracted from satellite can affect ability a classifier produce maps. We used Google Earth Engine (GEE) platform create cloud-free Sentinel-2 (S-2) Landsat-8 (L-8) over Tehran Province (Iran) as 2020. Two methods, namely, seasonal composites percentiles metrics, were define four datasets based on series, vegetation indices, topographic layers. The random forest was classification identifying most variables. Accuracy assessment results showed that S-2 outperformed L-8 overall class level. Moreover, comparison indicated percentile both series. At level, improved performance related their better about phenological variation different classes. Finally, conclude methodology GEE an fast way be

Language: Английский

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Quantitative spatial analysis of vegetation dynamics and potential driving factors in a typical alpine region on the northeastern Tibetan Plateau using the Google Earth Engine

CATENA, Journal Year: 2021, Volume and Issue: 206, P. 105500 - 105500

Published: June 8, 2021

Language: Английский

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Spatiotemporal evolution of land cover changes and landscape ecological risk assessment in the Yellow River Basin, 2015–2020

Journal of Environmental Management, Journal Year: 2023, Volume and Issue: 332, P. 117149 - 117149

Published: Feb. 17, 2023

Language: Английский

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Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India

Urban Climate, Journal Year: 2022, Volume and Issue: 42, P. 101116 - 101116

Published: Feb. 9, 2022

Language: Английский

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Remote sensing based forest cover classification using machine learning

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 2, 2024

Abstract Pakistan falls significantly below the recommended forest coverage level of 20 to 30 percent total area, with less than 6 its land under cover. This deficiency is primarily attributed illicit deforestation for wood and charcoal, coupled a failure embrace advanced techniques estimation, monitoring, supervision. Remote sensing leveraging Sentinel-2 satellite images were employed. Both single-layer stacked temporal layer from various dates utilized classification. The application an artificial neural network (ANN) supervised classification algorithm yielded notable results. Using image Sentinel-2, impressive 91.37% training overall accuracy 0.865 kappa coefficient achieved, along 93.77% testing 0.902 coefficient. Furthermore, approach demonstrated even better method 98.07% accuracy, 97.75% coefficients 0.970 0.965, respectively. random (RF) algorithm, when applied, achieved 99.12% 92.90% 0.986 0.882. Notably, satellite, RF reached exceptional performance 99.79% 96.98% validation 0.996 0.954. In terms cover ANN identified 31.07% in District Abbottabad region. comparison, recorded slightly higher 31.17% forested area. research highlights potential remote machine learning algorithms improving assessment monitoring strategies.

Language: Английский

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Quantitative analysis of fractional vegetation cover in southern Sichuan urban agglomeration using optimal parameter geographic detector model, China

Ecological Indicators, Journal Year: 2024, Volume and Issue: 158, P. 111529 - 111529

Published: Jan. 1, 2024

Vegetation is a main part of ecosystems and an essential indicator for monitoring changes in terrestrial ecosystems. It crucial us to discover the temporal spatial features potential drivers vegetation change promote regional ecological environment protection management. However, it can be difficult pinpoint causes when considering both human activity climate change. We used trend stability method study patterns evolution South Sichuan Urban Agglomeration (SSUA) from 2001 2021 with Google Earth Engine (GEE) platform. An optimal parameter-based geographical detector (OPGD) model was applied optimize scale zoning effect geographic data, effectively solving problem data heterogeneity. compensates inadequacies conventional approaches that neglect modifiable areal units (MAUP), improving science accuracy quantitative analysis identification drivers. studied demonstrate (1) During last 21 years, Fractional Cover (FVC) has generally been good condition, multi-year average FVC greater than 0.4 71.74 %, significantly characterized by low fluctuation 78.16 %. there significant degradation, accounting 8.89 mainly urban areas Neijiang Zizhong County, Lu County Luzhou City, Gao Yibin other rapid urbanization. In general, built-up towns along transportation roads, while mountainous agricultural have high level cover. (2) The OPGD detection showed cover this region 2 km. Optimal discrete parameter combinations slope, elevation, temperature GDP are quantile breaks 9 intervals, which contribute improved scientific precision studies its (3) explanatory power urbanization rate, land use type, GDP, population density, annual precipitation were all above 20 % Moreover, any two factors interacted nonlinear enhancement bi-variable enhancement, increasing impact on variation. When slope 26.9°∼87.4°, elevation 967 m ∼ 4207 m, 0.18 °C 13.6 °C, 328 mm 439 mm, 4.07 5.23 million yuan km−2, density 12.7 21.1 people/km2, rate 33.4 %∼37.7 land-use type forest land, value highest suitable growth. using detect effects variables solves shortcomings previous methods variable methods, may more precisely explore driving mechanisms, offers references environmental conservation long-term economic growth region.

Language: Английский

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Evaluate Water Yield and Soil Conservation and Their Environmental Gradient Effects in Fujian Province in South China Based on InVEST and Geodetector Models

Water, Journal Year: 2025, Volume and Issue: 17(2), P. 230 - 230

Published: Jan. 16, 2025

Fujian Province is an important soil and water conservation region in hilly South China. However, there has been limited attention paid to the assessment of production at provincial level, distribution patterns ecosystem services under different environmental gradients regions have not revealed. This study evaluated spatiotemporal characteristics yield based on InVEST model 2000, 2010, 2020, explored their differences six gradients: elevation, slope, terrain position index, geomorphy, LULC, NDVI. The results statistics showed significant spatial differentiation temporal change yield; changes both exhibited obvious clustering cold hot spots (low high values); cities were higher than those conservation. index Geodetector that retention gradients; generally lower degree more sensitive response factors (slope, TPI, DEM). high-value 1000 2160 m for DEM, 25° 70.2° 0.81 1.42 medium mountain forest land 0.9 0.92 NDVI, which indicates mountainous with altitude, steep slopes, changes, vegetation coverage. exhibit distributions across gradients, should be adapting local conditions ecological environment development.

Language: Английский

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Integrating satellite remote sensing data and small-scale farmers’ perceptions to determine land use/ land cover changes and their driving factors in Gedaref state, Sudan

Environment Development and Sustainability, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 20, 2025

Language: Английский

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Modeling and Predicting Land Use Land Cover Spatiotemporal Changes: A Case Study in Chalus Watershed, Iran

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2022, Volume and Issue: 15, P. 5496 - 5513

Published: Jan. 1, 2022

Land use and land cover change (LULCC) is a main driver of global environmental has destructive effects on the structure function ecosystem. This study attempts to detect temporal spatial changes in LULC patterns Chalus watershed during last two decades using multi-temporal Landsat images predict future for year 2040. A hybrid method between segment-based pixel-based classification was applied each image 2001, 2014 2021 produce maps watershed. In this study, transition potential probability matrices types were provided by Support Vector Machine (SVM) algorithm Markov Chain model, respectively, project 2040 maps. The achieved K-index values that compared simulated map with actual resulted Kstandard = 0.9160, Kno 0.9379, Klocation 0.9318 KlocationStrata 0.9320, showing good agreement map. Analysis historical depicted 2001-2021, significant increase Agricultural (14317 ha) Barren area (9063 ha), sharp decline Grassland (26215 Forest (5989 major model predicted will continue decrease from 29.46% (50720.2667 25.67% (44207.78694 2040, as well as, unceasing expansion area, Built-up be expected Therefore, understanding spatiotemporal dynamics extremely important implement essential measures minimize consequences these changes.

Language: Английский

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