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Five countries in the Lancang-Mekong region, including Myanmar, Laos, Thailand, Cambodia, and Vietnam, are facing the threat of deforestation, despite having a high level of forest coverage. Quantitatively assessing the forest ecosystem status and its variations based on remote sensing products for vegetation parameters is a crucial prerequisite for the ongoing phase of our future project. In this study, we analyzed forest health in the year 2020 using four vegetation indicators: forest coverage index (FCI), leaf area index (LAI), fraction of green vegetation cover (FVC), and gross primary productivity (GPP). Additionally, we introduced an ecosystem quality index (EQI) to assess the quality of forest health. To understand the long-term trends in the vegetation indicators and EQI, we also performed a linear regression analysis from 2010 to 2020. The results revealed that Laos ranked as the top-performing country for forest ecosystem status in the Lancang-Mekong region in 2020. However, the long-term trend analysis results showed that Cambodia experienced the most significant decline across all indicators, while Vietnam and Thailand demonstrated varying degrees of improvement. This study provides a quality assessment of forest health and its variations in the Lancang-Mekong region, which is crucial for implementing effective conservation strategies.

The article focuses on the impact of the interannual variation in leaf expansion and teak defoliator at a teak plantation to the leaf area index (LAI) in northern Thailand. It evaluates several factors affecting tree growth in the area including radiative transmittance and volumetric soil moisture. The study suggests that leaf expansion occurred after increases in soil moisture and notes that increases in shallow soil moisture were insufficient to trigger leaf expansion.

Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change. [ABSTRACT FROM AUTHOR]