Spatial-temporal changes of land use/cover change and habitat quality in Sanjiang plain from 1985 to 2017

Jin, Shanhe; Liu, Xin; Yang, Jiajia; Lv, Junchao; Gu, Yuchao; Yan, Jishun; Yuan, Ruo; Shi, Yudi

doi:10.3389/fenvs.2022.1032584

Cited by 19 publications

(7 citation statements)

References 51 publications

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“…Such practices have resulted in low resource efficiency and wastage. To gain deeper insights, this driving mechanism should be further quantitatively analyzed in conjunction with habitat quality changes [50] and ecosystem service values [51], providing scientific guidance for ecological environment construction in the Bohai Rim.…”

Section: Spatial and Temporal Evolution Of The Bohai Sea Regionmentioning

confidence: 99%

Spatial-Temporal Pattern of Land Use and SDG15 Assessment in the Bohai Rim Region Based on GEE and RF Algorithms

Ke,

Liu,

Tan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The United Nations has proposed Sustainable Development Goal 15 (SDG15), which emphasizes the importance of sustainable land development. This study aims to use remote sensing data to build spectral index feature dataset based on Google Earth Engine platform, dig deep spectral features of ground objects, and use random forest algorithm to extract land use type distribution data in the Bohai Rim region from 2000 to 2020. Meanwhile, combined with land use transfer map and landscape pattern index, the spatio-temporal pattern of land use was quantitatively analyzed. Finally, the sustainable development level of land was evaluated quantitatively from three aspects: forest resource sustainability, wetland resource sustainability and land system sustainability. The results show that : (1) The overall accuracy and kappa coefficient of land use classification achieved by GEE and the RF algorithm were 0.94 and 0.92, respectively. (2) From 2000 to 2020, the main land use type in the study area is cropland, accounting for 33% of the total, and the impervious has significantly expanded, increasing by 9588.01 square kilometers, mainly from cropland, barren and water. (3) In relation to SDG15, forest resources exhibited poor stability, wetland resources demonstrated a steady recovery, SDG15.3.1 revealed that the problems of land degradation exist in various provinces and cities around the Bohai Sea, and the stability of land system is poor between 2000 and 2020. This paper can provide a valuable reference for land use management and ecological remediation in the Bohai Rim region.

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Section: Spatial and Temporal Evolution Of The Bohai Sea Regionmentioning

confidence: 99%

Spatial-Temporal Pattern of Land Use and SDG15 Assessment in the Bohai Rim Region Based on GEE and RF Algorithms

Ke,

Liu,

Tan

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In this paper, we transform the raster data of land use status in 2005 and 2020 into a land use transfer matrix using ArcGIS software. The mathematical model is as follows (Jin et al, 2022):…”

Section: Land Use Transfer Matrixmentioning

confidence: 99%

Spatiotemporal evolution patterns and driving factors of arable land in Huaihai Economic Zone, China

Wang,

Lin,

Wang

et al. 2023

Front. Environ. Sci.

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The Huaihai Economic Zone (HEZ) has excellent topographic and climatic conditions and is recognized as one of China’s major grain production areas. Identifying the spatiotemporal evolution patterns of arable land and the driving factors can offer valuable insights for protecting arable land, optimizing land use layout, and developing ecological economics in HEZ. In this paper, we analyze the spatiotemporal evolution patterns using spatial autocorrelation, land use transfer matrix, and kernel density calculations and investigate the driving factors of arable land evolution with Geodetector. The results show the following trends in the distribution of arable land within the HEZ: 1) The kernel density distribution of the arable land in HEZ remained relatively consistent from 2005 to 2020, but the density showed a downward trend over time. 2) The distribution of arable land in HEZ exhibited lower density in the Northeast and higher density in the Southwest, showing growth in the initial stage and subsequent declines in the intermediate and final phases. 3) A significant positive spatial correlation was observed in the distribution of arable land in HEZ. The main local cluster types were the “high-high” and “low-low” clusters, and their distribution characteristics were similar to the kernel density of arable land. 4) The change of arable land in HEZ between 2005 and 2020 was primarily driven by the conversion of land types, with a notable shift towards grassland and construction land 5) The main driving factors affecting the spatial distribution of arable land in HEZ included traffic accessibility, air temperature, precipitation, elevation, and slope. The secondary driving factors were land use and soil type. Over time, population and GDP have also evolved into significant driving factors. 6) The factors were bi-enhance after the interaction. In the future, all cities within HEZ should implement rigorous control measures to limit the expansion of arable land usage. Their primary focus should be on revitalizing existing construction land while strictly upholding the “compensation determines occupation” principle. They should also work to optimize the arrangement of arable land and give increased consideration to the interactive effects of traffic accessibility, population, and GDP to protect arable land in a more focused and effective manner.

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“…The ecosystem was almost undisturbed by human activities and may display the better ecosystem services in this region. Driven by the national food security strategy, large-scale cultivated land was carried out after the mid-1950s to meet the food demand for population growth [41]. Sanjiang Plain, with sufficient water sources, flat terrain, suitable accumulated temperature, and a unique land management system, has become a strategic grain reserve base in China, earning the reputation of "North China Granary" [42].…”

Section: Introductionmentioning

confidence: 99%

Differentiated Impacts of Land-Use Changes on Landscape and Ecosystem Services under Different Land Management System Regions in Sanjiang Plain of China from 1990 to 2020

Ning,

Pan,

Zhang

et al. 2024

Land

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Currently, impacts of rapid cropland reclamation and its intense structural changes in internal paddy-upland on ecosystem service are insufficient in Sanjiang Plain, China. Further, land management systems of Sanjiang Plain consist of state-owned and private farms; however, exploring the impact of different land management systems on “land use–landscape–ecosystem service” is still lacking. To reveal this issue, the integrated methodology of “land dynamic tracking–landscape index–improved ecosystem service assessment” was established. Results are displayed below: From 1990 to 2020, land use was featured by decreases in forest (−3308.62 km2), grassland (−6030.86 km2), waters (−475.81 km2), and unused land (−3037.27 km2), with a slight increase in constructed land (+403.25 km2) and a rapid increase in cropland (+12,447.56 km2). Although nearly equal increments of cropland on state-owned and private farms (i.e., 6156.70 km2 vs. 6290.86 km2) were monitored, different cropland structure changes were still revealed, namely a drastic expansion of paddy fields (13,788.32 km2) and an acute decrease in upland crops (−7631.62 km2) on state-owned farms, but both a slight increments in paddy fields (5920.08 km2) and upland crops (370.78 km2) on private farms. For landscape, private farms were more fragmented (SHDI = +0.63%), causing a decrease in aggregation (AI = −0.56%) and a more complex shape (LSI = +23.3%), by contrast, state-owned farms displayed an increased integrity (SHDI = −9.88%), along with an increase in aggregation (AI = +0.43%) and simplified shape (LSI = −13.30%). Evaluated ecosystem service value changed from 338.62 to 296.25 billion yuan from 1990 to 2020, a loss rate of 12.58% in Sanjiang Plain. Then, a new finding showed a higher loss rate on state-owned than private farms (i.e., 30.15% vs. only 6.18%). This study revealed differentiated processes of “land use–landscape–ecosystem service” in different land management system regions in China, providing new findings in the fields of land management system, ecological landscape, and environment.

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Spatial-temporal changes of land use/cover change and habitat quality in Sanjiang plain from 1985 to 2017

Cited by 19 publications

References 51 publications

Spatial-Temporal Pattern of Land Use and SDG15 Assessment in the Bohai Rim Region Based on GEE and RF Algorithms

Spatial-Temporal Pattern of Land Use and SDG15 Assessment in the Bohai Rim Region Based on GEE and RF Algorithms

Spatiotemporal evolution patterns and driving factors of arable land in Huaihai Economic Zone, China

Differentiated Impacts of Land-Use Changes on Landscape and Ecosystem Services under Different Land Management System Regions in Sanjiang Plain of China from 1990 to 2020

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