Search

The discrepancies in data across different phases and the unexplored optimal spatial resolution present challenges when using multitemporal canopy height models to accurately discern actual forest growth. In this study, we evaluated the reliability of bi-temporal CHMs to characterize growth changes in a boreal natural forest over a fouryear period. A maximum mosaic method was introduced to construct a CHM from various flight strips, aimed at minimizing data alignment errors. Subsequently, the canopy height and height changed derived from six different height percentile metrics and five spatial resolutions were evaluated. The results showed that higher resolution (e.g., < 2 m) and lower height metrics (e.g., 85th height percentile) consistently underestimated. Tree growth correlation with individual segmentation surpassed field-measurements at all resolutions and height metrics. for the optimal resolution and height metrics, the results suggest that using a 95th height percentile the 10 m scale effectively represents both canopy height (R²2012 = 0.95, RMSE2012 = 0.88 m, rRMSE2012 = 5.83%; R²2016 = 0.96, RMSE2016 = 1.11 m, rRMSE2016 = 6.91%) and height changes (R² = 0.59, RMSE = 0.86 m, rRMSE = 18.38%). This study demonstrates the necessity of carefully evaluating data characteristics and resolutions when employing multi-temporal CHM for forest dynamics monitoring. [ABSTRACT FROM AUTHOR]

Frequent water quality anomalies severely affect the ecological environment, hindering the protection, restoration, and sustainable use of inland freshwater ecosystems in Sustainable Development Goals (SDG) 15.1. Thus, there is an urgent need to conduct real-time monitoring and early warnings of various water quality anomalies. However, current remote sensing indices of water quality anomalies are aimed at only one specific water anomaly event in water regions, which makes it difficult to achieve high-precision and on-orbit detection of sudden water quality anomalies in complex geographical environments with the limited storage and computing resources of satellites. Therefore, this study proposed a remote sensing index for the detection of multi-type water quality anomalies in complex geographical environments by analyzing the spectral differences between different earth's surfaces. The validation results indicated that the proposed index achieved satisfactory performance for the synchronous detection of most water quality anomalies and could be used directly in complex geographical environments to detect water anomalies. Overall, this study contributes to the realization of real-time water quality anomaly detection methods in complex geographical environments, which increases the possibility of on-orbit water quality anomaly detection. [ABSTRACT FROM AUTHOR]