Amin Naboureh

The fraction of absorbed photosynthetically active radiation (FPAR) is a key physiological variable for characterizing vegetation structure and associated matter and energy exchange processes. Accurate and effective monitoring of FPAR is essential for understanding ecosystem functioning. However, systematic biases among existing ground-based observation techniques hinder the effective integration of FPAR data, limiting its potential for spatial scaling. This study selected five ground-based observation techniques, FPARnet, LAI-NOS, LAINet, LAI-2200, and digital hemispherical photography (DHP), based on the existing FPAR and LAI observation techniques at Wanglang Station, to develop a PAI_e-LAI-FPAR conversion model using the Beer–Lambert law. The correlation and consistency of FPAR derived from different observation techniques were comparatively analyzed. On this basis, a normalization-calibration model based on regression was developed for FPAR_LAI-NOS, FPAR_LAI-2200, and FPAR_DHP, using FPAR_FPARnet as the reference. Comparative analysis results show that FPAR_LAI-NOS and FPAR_FPARnet, as well as FPAR_LAI-2200 and FPAR_DHP with FPAR_LAI-NOS, exhibit good correlation and consistency (R ≥ 0.9, RMSE_obs ≤ 0.08). However, FPAR_LAINet shows a relatively weak correlation with FPAR_FPARnet (R = 0.12). After normalization-calibration, the consistency among multi-source FPAR observations was significantly improved (R remains unchanged, and the average RMSE_obs decreases by approximately 7.8%. The sample points are more closely aligned along the y = x line after calibration). This study provides a practical reference for the normalization-calibration of FPAR observations in mountainous forests based on multi-source ground-based observation techniques.

Urbanization has significantly increased over the past decades, making monitoring of urban growth and urban texture essential for urban planning and sustainable development. In this context, the classification of different urban textures has gained importance, leveraging advancemnts insatellite image processing and methods such as machine learning and object-based image analysis (OBIA), as well as their integration. The present study aims to apply and evaluate different object-based methods to map urban texture in different part of Tabriz city in Iran. To this end, five area with distinct urban texture patterns were selected and analyzed using OBIA’s spectral and spatial features. A semiautomated OBIA approach was developed and applied to map urban textures, and its robustness and efficiency was examinedOur analysis indicated that combining the average, shape, and gray level co-occurrence matrix methods enhances the ability to identify objects in urban environments. The results highlight the high potential of OBIA algorithms and features in detecting and classifying urban areas. This study provides valuable insights for urban planners, offering a useful tool for informed decision-making in future urban development.

Mangrove ecosystems are vital blue carbon habitats, and their accurate mapping and ongoing monitoring are essential for conserving their unique ecological and environmental benefits. This study employed a weakly supervised deep learning framework for semantic segmentation of the ecosystem by integrating time-series Sentinel-1 Synthetic Aperture Radar (SAR) imagery with an existing ecosystem map as weak labels. This approach incorporates a U-Net architecture with an EfficientNet-B7 backbone for robust feature extraction, employs Test-Time Augmentation (TTA) with an ensemble strategy to enhance prediction stability, and validates the method's temporal transferability across multiple years. The model achieved high accuracy in the baseline year (2020), with 95.56% overall accuracy according to independent reference samples, and successfully generated noise-free maps despite imperfect training data. The TTA approach improved the final map both visually and statistically compared to a simple prediction approach. Visual comparisons with high-resolution satellite imagery, an existing ecosystem map, and a recent global mangrove extent dataset confirmed the superior performance of our proposed workflow in mangrove mapping. The temporal transferability of the model, examined in 2017 and 2023 using the baseline model with no modification, confirmed its applicability by generating the ecosystem maps meticulously, though the predictive uncertainty value increased. The generated multi-temporal maps indicated a mangrove extent expansion of about 3.43% between 2017 and 2023. This straightforward workflow facilitates automatic mangrove ecosystem mapping and updating while effectively reducing the need for frequent reference sample collection, especially in cloud-prone areas. The temporal capability of the approach offers practical solutions for large-scale mangrove ecosystem conservation initiatives.

Droughts have a significant impact on surface water resources, especially in arid and semi-arid regions. Computational and data handling limitations have constrained previous time-series analyses. However, advances in cloud computing services and remote sensing technology allow for a more detailed examination. This study integrates multi-source satellite-derived data with a cloud computing platform to assess the impact of long-term drought on surface water and water balance in Iran from 2000 to 2021. Given the varying effects of drought on highlands and lowlands, the analysis was conducted at three levels: the entire country, the highlands, and the lowlands. The results of this study reveal imbalances between water balance from 2000 to 2021, with notable disparities observed during 2000–2007, 2009–2014, and 2016–2019. The results also show varying drought trends (e.g., −1.22 in 2000 and −0.73 in 2021), with severe conditions captured in 2008 (SPI: −1.92). Additionally, our analysis illustrated that lowlands were more impacted by droughts compared to highlands. Long-term drought and permanent surface water had correlation values of 0.33 across the country, 0.33 in the highlands, and 0.31 in the lowlands. For seasonal surface water, coefficients were 0.18 for the entire country, 0.16 for the highlands, and 0.18 for the lowlands. Overall, long-term drought had minimal effect on reducing surface water. These findings show that drought is only part of the explanation for the decrease in surface water resources.