Multi-Source and Multitemporal Urban and Rural Settlement Mapping Under Spatial Constraint: Qinghai–Tibetan Plateau Case Study

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(3), P. 401 - 401

Published: Jan. 24, 2025

Accurately extracting long-term urban and rural settlement (URS) information is crucial for studying urbanization processes their impacts on the ecological environment. However, existing remote sensing extraction methods often rely independent classification strategies each period, leading to error accumulation increased uncertainty in sequence extraction. To address this, this study proposed a data/model-constrained dynamic method URS validated it using Qinghai–Tibetan Plateau at five-year intervals from 1985 2020. The area of extracted by had matching degree 97.79% with reference, an average overall accuracy 93.25% kappa 0.89 1985–2020 confusion matrix sample. boundary (URSB) were more accurate than Global Urban Boundary (GUB) dataset, particularly spatial completeness detail. results provide technical support uncovering development patterns environmental impacts.

Language: Английский

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Landsat series images for evaluating ecological restoration effect from multi-time scale based on an ideal reference

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: March 12, 2024

Multi-time scale assessment of ecological restoration effects based on objective and scientific approaches can provide crucial information for implementing environmental protection policies ensuring sustainable regional development. This study evaluated the effect a natural evolution as reference frame, using yearly Landsat time series. Southern Ningxia in China was selected area. The remote sensing index (RSEI) calculated. features were derived from series RSEI reserve areas (NRAs). LandTrendr employed to characterize disturbance–recovery processes. Furthermore, we adopted dynamic time-warping method entire period, along with relative variation ratio (during cycle) capture long-term short-term effects, respectively. following conclusions drawn: First, time-series used successfully monitor Second, majority disturbances (i.e., >60%) occurred between 2000 2005. It is characterized by fewer disturbance times obvious spatial heterogeneity duration. Notably, 2022, improved. Additionally, approximately 40% area portrayed strong similarity NRAs. We conclude that quantifying at multi-time scales practical operational approach policymakers protection. Our presents novel insights assessing quality, capturing processes

Language: Английский

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Ecological Resilience Assessment Based on the Temporal Satellite Remote Sensing Imagery: A Case Study in Qianshan Region of Northeast Forest Belt

Published: Jan. 1, 2024

Language: Английский

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Artificial intelligence and its application in grassland monitoring and restoration

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 445 - 478

Published: Dec. 4, 2024

Language: Английский

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