Globally, land degradation is becoming a grave concern. Over the years, conditions such as drought, extreme weather events, pollution, changes in land use land cover, and desertification have intensified and led to land degradation, affecting both ecological and economic processes. Equally, during the last two centuries, population and urbanization have amplified manifold and increased the demand for additional food and shelter, resulting in alteration in land use land cover, over-grazing, and over-cultivation, loss of nutrient-rich surface soil, greater runoff from the more impermeable subsoil, and reduced water availability. Geographically, Goa is a highly diversified state. It is sandwiched between the West Coast and the Western Ghats. The state is blessed with beaches, mangroves, backwaters, wetlands, wildlife sanctuaries, evergreen forests, barren lands, and other vital ecosystems. The State of Goa, on average, receives more than 3000 millimeters of rainfall annually with high surface runoff. Using both primary and secondary data, this study sought to investigate and quantify the state’s land degradation. Secondary data came from satellites and other sources, while primary data came from field observation and ground truthing. Land degradation factors related to soil loss and the spatial pattern of soil erosion are predicted and evaluated using the Revised Universal Soil Loss Equation (RUSLE) method. Landsat-8 OLI-TIRS images were utilized to decide land use and cover (C factor), while DEM information was utilized to assess (LS factor). A soil map and rainfall data were collected to acquire a better understanding of soil erodibility (K factor) and rainfall erosivity (R factor). The kriging interpolation technique was used to gain a deeper comprehension of land degradation.The purpose of this paper is to comprehend the concept of integrated land degradation and how it affects the environment of Goa. Using remote sensing data and geostatistical methods, the study creates a comprehensive map of land degradation in the region by identifying and analyzing the various forms of land degradation in Goa. The paper also looks at how rainfall and the amount of land cover affect the rate of soil erosion in Goa. According to the findings, intense rainfall makes the eastern part of Goa particularly susceptible to soil erosion, and bare soil has a greater potential for erosion than vegetated land. The paper concludes that comprehensive land degradation mapping can be a useful tool for developing efficient land management strategies to preserve soil and encourage sustainable development in the region.

The study was carried out on the territory of the Kemerovo region-Kuzbass (Western Siberia, Russia). The purpose of the study was to obtain information on the species diversity and population of big-game animals. The monitoring was carried out on the forest territories of the region’s administrative districts. In the course of remote sensing using an unmanned aerial vehicle, the presence of all types of animals under consideration, except for the bear, was recorded. The deviation of the population number determined using the traditional method of digital technologies varied up to 50%. It was established that environmental measures organized and carried out by the regional administration and hunting farms improved the situation and stabilized the population of the main group of game animals. It was found that when using a sufficiently high sensitivity of the thermal imager (the used thermal imager had a very high sensitivity class ≤ 60 mK at 300 K), long-haired animals, which are characterized by a lower intensity of thermal radiation (for example, wolves) are identified and recognized in the images. The larger the animal and the worse the thermal insulation layer (wool or feathers), the easier it is to identify it in infrared images and the lower the sensitivity requirements of thermal imagers. The ability to recognize and record smaller animals and birds requires additional research on existing technologies. Our research has confirmed the validity of digital remote monitoring methods for managing the wildlife of hunting farms and nature conservation areas of the Siberian Taiga territories.