Tracking U.S. Land Cover Changes: A Dataset of Sentinel-2 Imagery and Dynamic World Labels (2016–2024)

Data, Journal Year: 2025, Volume and Issue: 10(5), P. 67 - 67

Published: May 4, 2025

Monitoring land cover changes is crucial for understanding how natural processes and human activities such as deforestation, urbanization, agriculture reshape the environment. We introduce a publicly available dataset covering entire United States from 2016 to 2024, integrating six spectral bands (Red, Green, Blue, NIR, SWIR1, SWIR2) Sentinel-2 imagery with pixel-level annotations Dynamic World dataset. This combined resource provides consistent, high-resolution view of nation’s landscapes, enabling detailed analysis both short- long-term changes. To ease complexities remote sensing data handling, we supply comprehensive code loading, basic analysis, visualization. also demonstrate an example application—semantic segmentation state-of-the-art models—to evaluate quality reveal challenges associated minority classes. The accompanying tools facilitate research in environmental monitoring, urban planning, climate adaptation, offering valuable asset evolving dynamics over time.

Language: Английский

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How Sentinel-1 timeseries can improve the implementation of conservation programs in Brazil

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 35, P. 101241 - 101241

Published: May 11, 2024

Cumulative Sum (CuSum) change detection was applied on a Sentinel-1 backscatter time series at spatial scale of 10 m as part conservation program implemented in Acre, Brazil, requiring the monitoring deforestation activities by participants program. This study evaluated results CuSum and compared them to those obtained from conventional products, demonstrating how this method can improve implementation such programs. We aimed map events with minimum resolution 0.1 ha maximise event while minimising false positives, which could lead unfair penalties for participants. The remarkable precision (ranging 87.3 % 96.1 %) short delay algorithm make it suitable implementing program, illustrated study. Moreover, has potential accurately assess extent future deforestation. contributes development effective strategies within framework programmes facilitate improved farming practices climate mitigation. code is available https://github.com/Pfefer/cusum.

Language: Английский

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Adapting CuSUM Algorithm for Site-Specific Forest Conditions to Detect Tropical Deforestation

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(20), P. 3871 - 3871

Published: Oct. 18, 2024

Forest degradation is a major issue in ecosystem monitoring, and to take reformative measures, it important detect, map, quantify the losses of forests. Synthetic Aperture Radar (SAR) time-series data have potential detect forest loss. However, its sensitivity influenced by ecoregion, type, site conditions. In this work, we assessed accuracy open-source C-band from Sentinel-1 SAR for detecting deforestation across forests Africa, South Asia, Southeast Asia. The statistical Cumulative Sums Change (CuSUM) algorithm was applied determine point change data. algorithm’s robustness different conditions, polarizations, resolutions, under varying moisture We observed that detection affected site- forest-management activities, also precipitation. type eco-region performance, which varied co- cross-pol backscattering components. channel showed better deforested region delineation with less spurious detection. results Kalimantan at 100 m spatial resolution, 25.1% increase average Kappa coefficient VH polarization comparison 25 resolution. To avoid false due high impact soil case Haldwani, seasonal analysis carried out based on dry wet seasons. For analysis, good accuracy, an 0.85 This work support upcoming NISAR mission. datasets were repackaged NISAR-like HDF5 format processing methods similar ATBDs.

Language: Английский

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How Sentinel-1 Timeseries Can Help Improving the Implementation of Conservation Programs in Brazil

Published: Jan. 1, 2024

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Language: Английский

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CuSum-Nrt as a Crop Monitoring System: A Sentinel-1 Application to Sunflower and Sorghum in Southwestern France

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium, Journal Year: 2024, Volume and Issue: unknown, P. 10117 - 10120

Published: July 7, 2024

Language: Английский

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