Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(19), P. 3139 - 3139

Published: Sept. 24, 2020

As an important production base for livestock and a unique ecological zone in China, the northeast Tibetan Plateau has experienced dramatic land use/land cover (LULC) changes with increasing human activities continuous climate change. However, extensive cloud limits ability of optical remote sensing satellites to monitor accurately LULC this area. To overcome problem mapping Ganan Prefecture, 2000–2018, we used dense time stacking multi-temporal Landsat images random forest algorithm based on Google Earth Engine (GEE) platform. The dynamic trends were analyzed, geographical detectors quantitatively evaluated key driving factors these changes. results showed that (1) overall classification accuracy varied between 89.14% 91.41%, kappa values greater than 86.55%, indicating reliably accurate. (2) major types study area grassland forest, their accounted 50% 25%, respectively. During period, decreased, while construction increased varying degrees. land-use intensity presents multi-level intensity, it was higher southwest. (3) Elevation population density changes, economic development also significantly affected LULC. These findings revealed main Gannan Prefecture provided reference assisting sustainable management protection policy decisions.

Language: Английский

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Response and multi-scenario prediction of carbon storage to land use/cover change in the main urban area of Chongqing, China

Ecological Indicators, Journal Year: 2022, Volume and Issue: 142, P. 109205 - 109205

Published: July 26, 2022

Carbon storage services play an important role in maintaining ecosystem stability. Land use/cover change (LUCC) is the dominant factor generating changes carbon storage. Demonstrating impact of LUCC on regional and predicting future under different land use scenarios great significance for promoting peak neutrality goals. Taking main urban area Chongqing as example, this study analyzes from 2000 to 2020 response LUCC. The Markov-FLUS model employed predict pattern 2035 four scenarios, InVEST used assess scenarios. results can be summarized follows: 1) In past 20 years, cultivated areas decreased by 743.29 km2, construction increased 773.48 km2. About 18.8 % was transferred, conversion being most type transfer. 2) 2000, 2005, 2010, 2015, 2020, 59.85, 59.29, 57.90, 56.95, 54.07 Tg, respectively, showing annually decreasing trend with a cumulative decrease 5.78 Tg. occupation leading rapid spatial distribution differs significantly, exhibiting low middle high surrounding areas. 3) 2035, shows degrees decline Natural Trend Scenario (NTS), Food Security (FSS) High Urbanization (HUS), 3.37, 0.59, 5.25 respectively. only increase 1.51 Tg found Ecological (ESS). Therefore, background "Dual Carbon" targets positioning ecological barrier Yangtze River Basin, ESS considered development planning Chongqing, which both sink ensure security.

Language: Английский

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Integrating Remote Sensing and a Markov-FLUS Model to Simulate Future Land Use Changes in Hokkaido, Japan

Remote Sensing, Journal Year: 2021, Volume and Issue: 13(13), P. 2621 - 2621

Published: July 3, 2021

As the second largest island in Japan, Hokkaido provides precious land resources for Japanese people. Meanwhile, as food base of gradual decrease agricultural population and more intensive practices on have led its arable use to change year by year, which has also caused changes whole pattern entire Hokkaido. To realize sustainable Hokkaido, past future patterns must be investigated, target-based planning suggestions should given this basis. This study uses remote sensing GIS technology analyze temporal spatial during two decades. The types include cultivated land, forest, waterbody, construction, grassland, others, using satellite images Landsat 2000, 2010, 2019 achieve goal make classification. In addition, used coupled Markov-FLUS model simulate three different scenarios next 20 years. Scenario-based situational analysis shows that will drop about 25% 2040 under natural development scenario (ND), while area remain basically unchanged protection (CP). forest (FP), increase 1580.8 km2. It is believed findings reveal been well protected However, future, government enterprises pay attention land.

Language: Английский

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Analysis of the Current and Future Prediction of Land Use/Land Cover Change Using Remote Sensing and the CA-Markov Model in Majang Forest Biosphere Reserves of Gambella, Southwestern Ethiopia

The Scientific World JOURNAL, Journal Year: 2021, Volume and Issue: 2021, P. 1 - 18

Published: Feb. 23, 2021

This study aimed to evaluate land use/land cover changes (1987–2017), prediction (2032–2047), and identify the drivers of Majang Forest Biosphere Reserves. Landsat image (TM, ETM+, OLI-TIRS) socioeconomy data were used for LU/LC analysis its change. The supervised classification was also employed classify LU/LC. CA-Markov model predict future change using IDRISI software. Data collected from 240 households eight kebeles in two districts drivers. Five classes identified: forestland, farmland, grassland, settlement, waterbody. Farmland settlement increased by 17.4% 3.4%, respectively; while, forestland grassland reduced 77.8% 1.4%, respectively, 1987 2017. predicted results indicated that farmland 26.3% 6.4%, while decreased 66.5% 0.8%, 2032 2047. Eventually, agricultural expansion, population growth, shifting cultivation, fuel wood extraction, fire risk identified as main Generally, substantial observed will continue future. Hence, use plan should be proposed sustain resource Reserves, local communities’ livelihood improvement strategies are required halt conversion.

Language: Английский

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Assessing the effects of ecological engineering on spatiotemporal dynamics of carbon storage from 2000 to 2016 in the Loess Plateau area using the InVEST model: A case study in Huining County, China

Environmental Development, Journal Year: 2021, Volume and Issue: 39, P. 100641 - 100641

Published: April 16, 2021

Language: Английский

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Land Use/Land Cover Change and Their Driving Factors in the Yellow River Basin of Shandong Province Based on Google Earth Engine from 2000 to 2020

ISPRS International Journal of Geo-Information, Journal Year: 2022, Volume and Issue: 11(3), P. 163 - 163

Published: Feb. 23, 2022

As the convenient outlet to Bo Sea and major region of economic development in Yellow River Basin, Shandong Province China has undergone large changes land use/land cover (LULC) past two decades with rapid urbanization population growth. The analysis LULC change patterns its driving factors section Basin can provide a scientific basis for rational planning ecological protection resources Basin. In this manuscript, we analyzed spatial pattern temporal 2000, 2010, 2020 by using random forest classification algorithm Google Earth Engine platform multi-temporal Landsat TM/OLI data. were also quantified factor detector interaction geodetector. Results show that decades, types study area are mainly farmland construction land, among which proportion decreased increased from 19.4% 29.7%. Based on results detector, it be concluded elevation, slope, soil type key affecting area. between elevation slope type, temperature precipitation strong explanatory power variation research data support environmental protection, sustainable, high-quality help local governments take corresponding measures achieve coordinated sustainable socioeconomic development.

Language: Английский

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Simulating Future LUCC by Coupling Climate Change and Human Effects Based on Multi-Phase Remote Sensing Data

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(7), P. 1698 - 1698

Published: March 31, 2022

Future land use and cover change (LUCC) simulations play an important role in providing fundamental data to reveal the carbon cycle response of forest ecosystems LUCC. Subtropical forests have great potential for sequestration, yet their future dynamics under natural human influences are unclear. Zhejiang Province China is distribution area subtropical forests. For management, it significance explore dynamic changes Zhejiang. As a popular LUCC spatial simulation model, cellular automata (CA) model coupled with machine learning quantitative demand models such as system (SD) can achieve effective simulation. Therefore, we first integrated back propagation neural network (BPNN), CA, SD BPNN_CA_SD (BCS) then designed slow development scenario (SD_Scenario), harmonious (HD_Scenario), baseline (BD_Scenario), fast (FD_Scenario), combining climate disturbance. Thirdly, obtained land-use patterns from 2014 2084 multiple scenarios, finally, analyzed temporal discussed future. The results showed following: (1) overall accuracy was approximately 0.8, kappa coefficient 0.75, figure merit (FOM) value over 28% when using BCS predict LUCC, indicating that could consistent accurately. (2) evolution different scenarios varied, growth bamboo decline coniferous FD_Scenario being prominent among changes. Compared 2014, will increase by 37%, while decrease 25%. (3) Comparing forests, SD_Scenario found be beneficial ecology. These provide decision-making reference planning sustainable Province.

Language: Английский

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Land Use Dynamic Changes in an Arid Inland River Basin Based on Multi-Scenario Simulation

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(12), P. 2797 - 2797

Published: June 10, 2022

The Tarim River Basin is the largest inland river basin in China. It located an extremely arid region, where agriculture and animal husbandry are main development industries. recent rapid rise population land demand has intensified competition for urban use, making water body ecosystem increasingly fragile. In light of these issues, it important to comprehensively grasp regional structure changes, improve degree reasonably allocate resources achieve sustainable both social economy ecological environment. This study uses CA-Markov model, PLUS model gray prediction simulate validate use/cover change (LUCC) Basin, based on remote sensing data. aim this research discern dynamic LUCC patterns predict evolution future spatial temporal use. results show that grassland barren currently types Basin. Furthermore, significant expansion cropland area reduction characteristics changes during period (1992–2020), when about 1.60% 1.36% converted cropland. Over next 10 years, we anticipate land-use will be dominated by land, with increasing trend other than land. Grassland add 31,241.96 km2, mainly Dina lower parts Weigan-Kuqu, Kashgar, Kriya, Qarqan rivers, while decline 2.77%, decreases middle reaches findings provide a solid scientific basis resource planning.

Language: Английский

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Construction of the green infrastructure network for adaption to the sustainable future urban sprawl: A case study of Lanzhou City, Gansu Province, China

Ecological Indicators, Journal Year: 2022, Volume and Issue: 145, P. 109715 - 109715

Published: Nov. 24, 2022

Nature-based green infrastructure (GI) is considered as a sound solution to mitigate the urban issues resulting from rapid urbanization. It exceedingly essential construct GI network for adaption dynamics of natural and social conditions. However, it has not been well studied until now. In this study, taking Lanzhou City, Gansu Province, China case, we proposed an improved framework construction sustainable sprawl. First, predicted land use cover (LULC) in City 2030 by using FLUS (future simulation) model. Then, based on LULC, extracted ecological sources corridors integrating MSPA (Morphological spatial pattern analysis) MCR (minimum cumulative resistance) Finally, optimized future sprawl was established, which included 10 sources, 56 19 nodes. case proven be reliable, thus can promoted other regions different scenarios. This study would provide some new insights planning both cities regions, then contribute protection regional security.

Language: Английский

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Dynamics and future prediction of LULC on Pare River basin of Arunachal Pradesh using machine learning techniques

Environmental Monitoring and Assessment, Journal Year: 2023, Volume and Issue: 195(6)

Published: May 22, 2023

Language: Английский

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