Scholarlink

<p>Remote sensing technologies are valuable for effectively monitoring patterns of tree mortality; however, their integration with environmental assessments presents challenges when identifying specific stressors in forested areas. This study aimed to analyze the spatiotemporal distribution of tree dieback using UltraCam and UAV aerial imagery. Additionally, it evaluated the environmental factors influencing dieback hotspots in Daland Forest Park, located in Golestan Province, northern Iran. Images from UltraCam (2016) and UAV (2021, 2023) were analyzed using object-based classification with the Bayes algorithm to detect dead and dying trees. The generated maps were validated, and spatial patterns were assessed using Moran’s Index. The intensity of clustering was measured with the Getis-Ord General G statistic, while the Getis-Ord Gi statistic identified hot and cold spots across the study periods. A Spearman’s correlation test was applied to explore relationships between these zones and environmental factors. The results showed that UltraCam and UAV imagery achieved high accuracies (83.33%-91.2%) and Kappa coefficients (0.77–0.88). Spatial analysis using Moran’s Index revealed a clustered distribution of dieback trees. Clustering intensity analyses based on the General Getis-Ord statistic indicated low clustering in 2016 and 2021, which transitioned to high clustering by 2023. Furthermore, the investigation into hot and cold spots showed negative correlations between the prevalence of dieback and proximity to forest roads and water wells. This study demonstrated that UltraCam and UAV imagery are effective data sources for monitoring tree mortality and its spatial patterns. The findings reveal that tree mortality is predominantly concentrated near forest roads and water wells, providing a foundation for developing strategies to enhance sustainable forest management practices in these areas.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatiotemporal analysis of the forest tree dieback patterns using aerial remote sensing data and clustering pattern indices

  • 期刊论文

  • 发表日期：

    2026年1月5日

查看全文
  • Mohammad Hassan Naseri,
  • Shaban Shataee Jouibary,
  • Hashem Habashi

摘要

Remote sensing technologies are valuable for effectively monitoring patterns of tree mortality; however, their integration with environmental assessments presents challenges when identifying specific stressors in forested areas. This study aimed to analyze the spatiotemporal distribution of tree dieback using UltraCam and UAV aerial imagery. Additionally, it evaluated the environmental factors influencing dieback hotspots in Daland Forest Park, located in Golestan Province, northern Iran. Images from UltraCam (2016) and UAV (2021, 2023) were analyzed using object-based classification with the Bayes algorithm to detect dead and dying trees. The generated maps were validated, and spatial patterns were assessed using Moran’s Index. The intensity of clustering was measured with the Getis-Ord General G statistic, while the Getis-Ord Gi statistic identified hot and cold spots across the study periods. A Spearman’s correlation test was applied to explore relationships between these zones and environmental factors. The results showed that UltraCam and UAV imagery achieved high accuracies (83.33%-91.2%) and Kappa coefficients (0.77–0.88). Spatial analysis using Moran’s Index revealed a clustered distribution of dieback trees. Clustering intensity analyses based on the General Getis-Ord statistic indicated low clustering in 2016 and 2021, which transitioned to high clustering by 2023. Furthermore, the investigation into hot and cold spots showed negative correlations between the prevalence of dieback and proximity to forest roads and water wells. This study demonstrated that UltraCam and UAV imagery are effective data sources for monitoring tree mortality and its spatial patterns. The findings reveal that tree mortality is predominantly concentrated near forest roads and water wells, providing a foundation for developing strategies to enhance sustainable forest management practices in these areas.