Forest destruction has been found to be the cause of natural disasters in the form of floods, landslides in the rainy season, droughts in the dry season, climate change, and global warming. The high rate of forest destruction is caused by various factors, including weak law enforcement efforts against forestry crimes, such as illegal logging events. However, in Indonesia, illegal logging is only discovered when the perpetrator has distributed the wood products. The lack of monitoring of the overall condition of the forest has an impact on the current high level of forest destruction. Through this research, the problems related to environmental damage due to illegal logging will be described through remote sensing technology, which is currently mainly developed on the basis of artificial intelligence and machine learning, namely Google Earth Engine (GEE). Monitoring of illegal logging events will be analysed using Sentinel 1 and 2 data. Obtaining satellite imagery with relatively small cloud cover for tropical regions, such as Indonesia, is remarkably difficult. This difficulty is due to the presence of a radar sensor on Sentinel 1 images that can penetrate clouds, allowing for observation of the forest condition even in the presence of clouds. Using the random forest classification algorithm of the GEE platform, data on forest conditions in 2021 were obtained, covering an area of 2,843,938.87 ha or 63% of the total area of Sulawesi Selatan Province. An analysis using a map of the function of forest areas revealed that of the current forest area, 38.46% was non-forest estates and 61.54% was forest areas. The continued identification of illegal logging events also found 1971 spots of forest change events in the vulnerable time of the first period (January–April) with the second period (April–August), and 1680 spots of forest change events in the vulnerable period of the second period (April–August) with the third period (September–December), revealing a total incident area of 7599.28 ha.

The study aims to develop and apply land use change (LUC) performance on landslide susceptibility map using frequency ratio (FR), and Logistic regression (LR) method in a geographic information system. In the study area, Upper Ujung-loe Watersheds area of Indonesia, landslides were detected using field survey and air photography from time series data image of Google Earth Pro from 2012 to 2016 and LUC from 2004 to 2011. Landslide susceptibility map (LSM) was constructed using FR and LR with nine causative factors. The result indicated that LUC affect the production of LSM. Validation of landslide susceptibility was carried out in this study at both with and without LUC causative factors. First, performances of each landslide model were tested using AUC curve for success and predictive rate. The highest value of predictive rate at with LUC in both FR and LR method were 83.4 % and 85.2 %, respectively. In the second stage, the ratio of landslides falling on high to a very high class of susceptibility was obtained, which indicates the level of accuracy of the method.LR method with LUC had the highest accuracy of 80.24 %. Taken together, the results suggested that changing the vegetation to another landscape causes slopes unstable and increases probability to landslide occurrence.

In Indonesia, the number of landslide disasters case increase each year, especially in watershed. The main factor of landslide is a gravity in slope. Another factor like the high intensity of rainfall, the wrong use of sloping land and geology structure. Focusing the many cases of landslide, this project is aim to Analysis of the Landslides Vulnerability Level Using Frequency Ratio Method in Tangka Watershed. This research was started from July-September 2019. We used Tangka Watershed Map, Imagery of Landsat 7, DEM National Imagery resolution 8-meter, geology map scale 1: 50.000, rainfall intensity from BMKG 10 years ago and imagery time series from Google Earth Pro to analyze the landslide factor, like slope, river distance, fracture distance, lithology, land cover, curvature, and aspect. Frequency Ratio Method used to analyze and get the landslide vulnerability level. There are 191 polygons in landslide inventory among 2004-2014. The frequency ratio in land cover is 5.99. It means there are higher potency of landslide in land cover area than another area.

Landslides that often occur in the Subs watershed of Mamasa increase the sedimentation rate so that the Bakaru hydropower plant becomes less than optimal. The contributing factors to lanslide susceptibility are land closure, lithology, curve, slope direction aspect, slope, precipitation, fault distance, and river distance. The research aims to determine the most influential erosion causative factor in Mamasa Sub-watershed by building a landslide susceptibility map using the frequency ratio method. The most significant factor is land closure, with a value of 2.03, indicating a high probability of lanslide events. The model’s success rate and prediction rate’s success rate were expressed fairly well with 0.754 and 0.752. Based on the insanity map, the Region is very high and high at 23.74% and 12.52%; insanity is moderate, low, and very low consecutively at 27.44 %, 23.77, and 12.33%.

This study aims to build a landslide susceptibility map (LSM) by using certainty factor (CF) models for mitigation of landslide hazards and mitigation for people who live near to the forest. In the study area, the mountainous area of the Ujung-loe watersheds of South Sulawesi, Indonesia, information on landslides were derived from aerial photography using time series data images from Google Earth Pro© from 2012 to 2016 and field surveys. The LSM was built by using a CF model with eleven causative factors. The results indicated that the causative factor with the highest impact on the probability of landslide occurrence is the class of change from dense vegetation to sparse vegetation (4-1), with CF value 0.95. The CF method proved to be an excellent method for producing a landslide susceptibility map for mitigation with an area under curve (AUC) success rate of 0.831, and AUC predictive rate 0.830 and 85.28% of landslides validation fell into the high to very high class. In conclusion, correlations between landslide occurrence with causative factors shows an overall highest LUC causative factor related to the class of change from dense vegetation to sparse vegetation, resulting in the highest probability of landslide occurrence. Thus, forest areas uses at these locations should prioritize maintaining dense vegetation and involving the community in protection measures to reduce the occurrence of landslide risk. LSM models that apply certainty factors can serve as guidelines for mitigation of people living in this area to pay attention to landslide hazards with high and very high landslide vulnerability and to be careful to avoid productive activities at those locations.