Land-use changes surrounding Mahasarakham University in Thailand were investigated using multi-sensor images from 2002 and 2019. This study used aerial photographs and Landsat-7 satellite images captured in 2002, and aerial photographs from an unmanned aerial vehicle and Sentinel-2A data observed in 2019. Visual image interpretation (VII), object-based image analysis (OBIA), and random forest (RF) methods were applied to classify building areas from the multi-sensor images. Population was estimated using buildings and field-survey data, and population samples. The samples were obtained by point-, pixel-, and area-based methods. The different population estimation approaches were then compared with the actual population based on field surveys. VII yielded accuracies of 97% in 2002 and 97.5% in 2019. Built-up extraction using RF yielded accuracies of 86.55 and 90.76%, whereas OBIA was 76.47 and 82.35%, indicating a transformation in the land use from paddy fields to urban and residential areas. The area-based method were highly efficient in 2002 (r2 = 0.92) and 2019 (r2 = 0.93). The proposed area-based method provides more accurate population estimates than existing methods, with accuracies considered to be comparable to those of field data.

Abstract. Drought directly threatens food security and livelihoods, thereby increasing socioeconomic risks and remains a challenge for natural resource management, particularly in frequently affected regions. Earth observation (EO) satellites provide extensive spectral and temporal data for long-term drought monitoring. This study monitored droughts in Northeast Thailand from 2001 to 2019 using the MODIS normalised difference vegetation index (NDVI) image time series. The Savitzky-Golay (S-G) method was used to remove noise and fill gaps in the image datasets. Optimal indicators as the vegetation condition index (VCI) and the standard vegetation index (SVI) were used to monitor drought distribution patterns over the previous 19 years. S-G filtering effectively reduced the impact of undetected clouds and water vapour, while VCI had the highest accuracy coefficient of determination (R2) for rainfall data at 0.85. Long-term droughts occurred frequently in 2005, 2004, 2007, and 2001 with the northern and central regions most severely affected. Severe drought primarily impacted agricultural land, forest and miscellaneous areas. Inter-annual drought variability for one and three time steps was clearly demonstrated in May and April to June from 2001 to 2019. Overall, the VCI provided a high level of satisfaction for drought monitoring in this region and clearly displayed the spatial distribution of long-term drought regions. Our findings provide a valuable resource for drought mitigation planning and warning systems.

Abstract. The Royal Forest Department of Thailand has permitted people to use the resources in national parks since 2005. It leads to a decrease in forest areas. This study aims to monitor and predict forest land change in Phu Phan National Park using Landsat 5 TM images in 1998 and 2008, and Sentinel-2 MSI image in 2018. The atmosphere correction was conducted for satellite images. Land use changes were classified by object base image analysis (OBIA), include forest, agriculture, built-up, water and miscellaneous. The land use maps were measured, and then the CA-Markov model was applied to predict the forest change in a year of 2028. The results demonstrate that overall accuracy (OA) of land use maps is 85.6%, 88%, and 89.6% in 1998, 2008 and 2018, respectively. The land use map in 2018 is more accurate than others because the high-resolution image and current data input. Moreover, the use of reference data nowadays has high potential and reality for classification. During 1998 to 2008, forest and built-up extended 45.35% and 5.07%, respectively. Meanwhile, miscellaneous, agriculture, and water decreased by 41.38%, 21.92%, and 3.45%. During 2008 to 2018, agriculture, miscellaneous, and built-up slightly increased by 21.92%, 14.75%, and 12.26%, respectively while forest and water decreased by 48.82% and 2.24%, respectively. The predicted forest change in 2028 is a decrease by 10.49% due to land use change to miscellaneous, agriculture, built-up, and water area, as forest is likely to be trespassed for built-up and agriculture areas as a result of local population growth. The results of the study can be useful for planning and managing the national park in the future.