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<p>Remote sensing time series enable timely detection of forest degradation, which is critical in the semi-arid Zagros oak forests. We analyze a 1995–2024 Landsat NDVI time series with BFAST Spatial to map the location, timing, and magnitude of canopy disturbance across two representative sites. The workflow includes preprocessing (cloud/shadow masking), temporal segmentation, and thresholded change-magnitude mapping validated against very high-resolution imagery. BFAST Spatial detected 8,699.6&#xa0;ha of disturbed oak forest in total: 5,058.4&#xa0;ha (4.9%) in Site A and 3,641.2&#xa0;ha (4.1%) in Site B, relative to 1995 forest baselines (102,765.4&#xa0;ha and 88,450.1&#xa0;ha, respectively). Independent interpretation of validation points yielded overall accuracy (OA) of 88.6% (Site A) and 77.7% (Site B), with class-specific user’s and producer’s accuracies reported per site. Spatial patterns indicate disturbance concentration near reservoir shorelines, road/pipeline corridors, and agriculture–forest edges; natural stressors (fire, drought, pests) further contribute to loss. Implications. The results demonstrate that long Landsat records combined with temporal segmentation provide robust, scalable indicators of degradation in mountainous oak ecosystems. To enhance small-patch sensitivity in recent years, Sentinel-2 (10&#xa0;m) and optical–SAR fusion are recommended as complementary inputs to BFAST for post-2015 monitoring.</p>

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Deforestation Monitoring in Zagros Oak Forests with Landsat time Series and the BFAST Spatial Algorithm

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Mahmood Alizadeh,
  • Khalil Gholamnia,
  • Golzar Einali,
  • Alijafar Mousivand,
  • Somayeh Sima,
  • Omid Ghorbanzadeh

摘要

Remote sensing time series enable timely detection of forest degradation, which is critical in the semi-arid Zagros oak forests. We analyze a 1995–2024 Landsat NDVI time series with BFAST Spatial to map the location, timing, and magnitude of canopy disturbance across two representative sites. The workflow includes preprocessing (cloud/shadow masking), temporal segmentation, and thresholded change-magnitude mapping validated against very high-resolution imagery. BFAST Spatial detected 8,699.6 ha of disturbed oak forest in total: 5,058.4 ha (4.9%) in Site A and 3,641.2 ha (4.1%) in Site B, relative to 1995 forest baselines (102,765.4 ha and 88,450.1 ha, respectively). Independent interpretation of validation points yielded overall accuracy (OA) of 88.6% (Site A) and 77.7% (Site B), with class-specific user’s and producer’s accuracies reported per site. Spatial patterns indicate disturbance concentration near reservoir shorelines, road/pipeline corridors, and agriculture–forest edges; natural stressors (fire, drought, pests) further contribute to loss. Implications. The results demonstrate that long Landsat records combined with temporal segmentation provide robust, scalable indicators of degradation in mountainous oak ecosystems. To enhance small-patch sensitivity in recent years, Sentinel-2 (10 m) and optical–SAR fusion are recommended as complementary inputs to BFAST for post-2015 monitoring.