Measuring the efficiency and driving factors of urban land use based on the DEA method and the PLS-SEM model—A case study of 35 large and medium-sized cities in China

Sustainable Cities and Society, Journal Year: 2019, Volume and Issue: 50, P. 101646 - 101646

Published: June 1, 2019

Language: Английский

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A Synthesis of Land Use/Land Cover Studies: Definitions, Classification Systems, Meta-Studies, Challenges and Knowledge Gaps on a Global Landscape

Land, Journal Year: 2021, Volume and Issue: 10(9), P. 994 - 994

Published: Sept. 21, 2021

Land is a natural resource that humans have utilized for life and various activities. use/land cover change (LULCC) has been of great concern to many countries over the years. Some main reasons behind LULCC are rapid population growth, migration, conversion rural urban areas. LULC considerable impact on land-atmosphere/climate interactions. Over past two decades, numerous studies conducted in investigated areas field LULC. However, assemblage information missing some aspects. Therefore, provide coherent guidance, literature review scrutinize evaluate particular topical employed. This research study collected approximately four hundred articles five (5) interest, including (1) definitions; (2) classification systems used classify globally; (3) direct indirect changes meta-studies associated with LULC; (4) challenges knowledge gaps. The synthesis revealed definitions carried vital terms, at national, regional, global scales. Most were categories land changes. Additionally, analysis showed significant data consistency quality. gaps highlighted fall ecosystem services, forestry, data/image modeling Core findings exhibit common patterns, discrepancies, relationships from multiple studies. While as tool similarities among studies, our results recommend researchers endeavor perform further promote overall understanding, since investigations will continue

Language: Английский

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Land use land cover change modeling by integrating artificial neural network with cellular Automata-Markov chain model in Gidabo river basin, main Ethiopian rift

Environmental Challenges, Journal Year: 2021, Volume and Issue: 6, P. 100419 - 100419

Published: Dec. 4, 2021

Modeling land use cover (LULC) change is crucial to understand its spatiotemporal trends protect the resources sustainably. The appraisal of this study was model LULC from 1985 2050 owing business-as-usual scenario (BAU) in Gidabo River Basin (GRB) located Main Ethiopian Rift Valley. Different dependent and independent spatial datasets were used viz, 1985, 2003 2021 Landsat imagery; topography features, proximity variables, population density evidence likelihood. Since future projection requires historical as a baseline, detected using hybrid image classification procedure ERDAS Imagine nine major classes identified. Multi-Layer Perceptron Neural Network Cellular Automata-Markov Chain built-in TerrSet software implemented project 2035 LULC. depicts, GRB experienced significant dynamics will also be extended for coming several years. Agriculture land, settlement water body showed gains at expense forest, shrub grasslands loss. Land changes beyond land's capability played role triggering degradation. To minimize these adverse consequences change, environmentally-friendly management measures must implemented. outcome helpful providing opportunity develop adequate resource conservation strategy plan future.

Language: Английский

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U-Net-LSTM: Time Series-Enhanced Lake Boundary Prediction Model

Land, Journal Year: 2023, Volume and Issue: 12(10), P. 1859 - 1859

Published: Sept. 29, 2023

Change detection of natural lake boundaries is one the important tasks in remote sensing image interpretation. In an ordinary fully connected network, or CNN, signal neurons each layer can only be propagated to upper layer, and processing samples independent at moment. However, for time-series data with transferability, learned change information needs recorded utilized. To solve above problems, we propose a boundary prediction model combining U-Net LSTM. The ensemble LSTMs helps improve overall accuracy robustness by capturing spatial temporal nuances data, resulting more precise predictions. This study selected Lake Urmia as research area used annual panoramic images from 1996 2014 (Lat: 37°00′ N 38°15′ N, Lon: 46°10′ E 44°50′ E) obtained Google Earth Professional Edition 7.3 software set. uses network extract multi-level features analyze trend boundaries. LSTM module introduced after optimize predictive using historical storage forgetting well current input data. method enables automatically fit time series mine deep changes. Through experimental verification, model’s changes training reach 89.43%. Comparative experiments existing U-Net-STN show that U-Net-LSTM this has higher lower mean square error.

Language: Английский

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Land-use and land-cover change in Lake Ziway watershed of the Ethiopian Central Rift Valley Region and its environmental impacts

Land Use Policy, Journal Year: 2020, Volume and Issue: 96, P. 104682 - 104682

Published: May 10, 2020

Language: Английский

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Spatiotemporal change detection of land use land cover (LULC) in Fashiakhali wildlife sanctuary (FKWS) impact area, Bangladesh, employing multispectral images and GIS

Modeling Earth Systems and Environment, Journal Year: 2023, Volume and Issue: 9(3), P. 3151 - 3173

Published: Jan. 7, 2023

Abstract Land cover change has posed significant concerns to biodiversity and climate in Bangladesh globally. Despite the country’s designation of forest regions as protected areas conserve their valuable resources, deforestation conversion remained unabated. Fashiakhali Wildlife Sanctuary (FKWS), a area Chittagong Hill Tracts, its surrounding forested impact have experienced considerable changes over years, yet are deficient extensive assessment. This study evaluated land use (LULC) FKWS almost 3 decades (1994–2021) using multispectral remotely sensed data. The Landsat images 1994, 2001, 2010, 2021 were classified maximum likelihood algorithm analyzed for detection. comparative potential vegetation indices, including Normalized Difference Vegetation Index (NDVI) Soil Adjusted (SAVI), assessment, relationship between Surface Temperature (LST) NDVI was also assessed. A loss around 1117.17 ha (16%) recorded 1994 2021, with hugest proportion 867.78 (12.24%) deforested first period (1994–2001). Agricultural declined by 593.73 (8.37%) within entire period, despite initial increase 392.04 (5.53%) 2001 being primary driver earlier deforestation. However, recent decade (2010–2021), settlement expansion 963.90 (13.59%) due massive human migration contributed most remarkable overall 1731.51 (24.42%). Furthermore, provided better more accurate assessment than SAVI recommended aid quick evaluation monitoring future impacts agriculture, settlement, other sorts on cover. In tandem widely acknowledged issue increased temperature change, an absolute negative correlation found LST, confirming area.

Language: Английский

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Quantitative assessment of Land use/land cover changes in a developing region using machine learning algorithms: A case study in the Kurdistan Region, Iraq

Heliyon, Journal Year: 2023, Volume and Issue: 9(11), P. e21253 - e21253

Published: Oct. 24, 2023

The identification of land use/land cover (LULC) changes is important for monitoring, evaluating, and preserving natural resources. In the Kurdistan region, utilization remotely sensed data to assess effectiveness machine learning algorithms (MLAs) LULC classification change detection analysis has been limited. This study monitors analyzes in area from 1991 2021 using a quantitative approach with multi-temporal Landsat imagery. Five MLAs were applied: Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost). results showed that RF algorithm produced most accurate maps three-decade period, accompanied by high kappa coefficient (0.93-0.97) compared SVM (0.91-0.95), ANN (0.91-0.96), KNN (0.92-0.96), XGBoost (0.92-0.95) algorithms. Consequently, classifier was implemented categorize all obtainable satellite images. Socioeconomic throughout these transition periods revealed results. Rangeland barren areas decreased 11.33 % (-402.03 km2) 6.68 (-236.8 km2), respectively. transmission increases 13.54 (480.18 3.43 (151.74 0.71 (25.22 occurred agricultural land, forest, built-up areas, outcomes this contribute significantly monitoring developing regions, guiding stakeholders identify vulnerable better use planning sustainable environmental protection.

Language: Английский

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Determine the Land-Use Land-Cover Changes, Urban Expansion and Their Driving Factors for Sustainable Development in Gazipur Bangladesh

Atmosphere, Journal Year: 2021, Volume and Issue: 12(10), P. 1353 - 1353

Published: Oct. 16, 2021

At present, urbanization is a very common phenomenon around the world, especially in developing countries, and has significant impact on land-use/land-cover of specific areas, producing some unwanted effects. Bangladesh tightly inhabited country whose urban population increasing every day due to expansion infrastructure industry. This study explores change detection dynamics Gazipur district, Bangladesh, newly developed industrial hub city corporation, by using satellite imagery covering 10-year interval over period from 1990 2020. Supervised classification with maximum likelihood classifier was used gather spatial temporal information Landsat 5 (TM), 7 (ETM+) 8 (OLI/TIRS) images. The Geographical Information System (GIS) methodology also employed detect changes time. kappa coefficient ranged between 0.75 0.90. agricultural land observed be shrinking rapidly, an area 716 km2 Urbanization increased rapidly this area, grew more than 500% during period. urbanized expanded along major roads such as Dhaka–Mymensingh Highway Dhaka bypass road. was, moreover, concentrated near boundary line Dhaka, capital Bangladesh. Urban found influenced demographic-, economic-, location- accessibility-related factors. Therefore, similarly many concrete development policies should formulated preserve environment and, thereby, achieve sustainable goal (SDG) 11 (sustainable cities communities).

Language: Английский

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Monitoring Land Use Changes and Their Future Prospects Using GIS and ANN-CA for Perak River Basin, Malaysia

Water, Journal Year: 2021, Volume and Issue: 13(16), P. 2286 - 2286

Published: Aug. 21, 2021

Natural landscapes have changed significantly through anthropogenic activities, particularly in areas that are severely impacted by climate change and population expansion, such as countries Southeast Asia. It is essential for sustainable development, efficient water management practices, to know about the impact of land use cover (LULC) changes. Geographic information systems (GIS) remote sensing were used monitoring changes, whereas artificial neural network cellular automata (ANN-CA) modeling using quantum geographic (QGIS) was performed prediction LULC This study investigated changes Perak River basin years 2000, 2010, 2020. The also provides predictions future 2030, 2040, 2050. Landsat satellite images utilized monitor For classification images, maximum-likelihood supervised implemented. broad defines four main classes area, including (i) waterbodies, (ii) agricultural lands, (iii) barren urban (iv) dense forests. outcomes revealed a considerable reduction forests from year 2000 2020, substantial increase lands (up 547.39 km2) had occurred while has seen rapid rise. kappa coefficient assess validity classified with an overall 0.86, 0.88, 0.91 respectively. In addition, ANN-CA simulation results predicted will expand at expense other However, decrease occur area simulated years. successfully presents highlighting significant pattern basin. could be helpful administration planning region.

Language: Английский

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Land Use and Land Cover Change Assessment and Future Predictions in the Matenchose Watershed, Rift Valley Basin, Using CA-Markov Simulation

Land, Journal Year: 2022, Volume and Issue: 11(10), P. 1632 - 1632

Published: Sept. 22, 2022

Land use and land cover change (LULC) is known worldwide as a key factor of environmental modification that significantly affects natural resources. The aim this study was to evaluate the dynamics in Matenchose watershed from years 1991, 2003, 2020, future prediction changes for 2050. Landsat TM ETM+ Landsat-8 OLI were used LULC classification 2020. A supervised image sorting method exhausting maximum likelihood system used, with application using ERDAS Imagine software. Depending on classified LULC, 2050 predicted CA-Markov Change Models by considering different drivers dynamics. 1991 data showed predominantly covered grassland (35%), 2003 2020 cultivated (36% 52%, respectively). results settlement increased 6.36% 6.53%, respectively, while forestland decreased 63.76% 22.325, Conversion other categories most detrimental increase soil erosion, forest paramount reducing loss. concept population expansion relocation have led an agricultural forested areas further reinforced findings informant interviews. This result might help appropriate decision making improve policies management options.

Language: Английский

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