Taking six typical forest communities in Taizhou Green Heart (ⅰ: Liquidambar formosana + Ulmus pumila + Celtis sinensis; ⅱ: Celtis sinensis + Pterocarya stenoptera + Pinus massoniana; ⅲ: Sapindus mukorossi + Sapium sebiferum + Cupressus funebris; ⅳ: Liquidambar formosana + Acer buergerianum + Cupressus funebris); ⅴ: Celtis sinensis + Ligustrum compactum + Pinus massoniana; ⅵ: Machilus ichangensis + Sapindus mukorossi + Acer buergerianum) as the research objects, 5 indicators: Shannon-Wiener (H), Patrick richness (R1), Margalef species richness (R2), Pielou evenness (J) and ecological dominance (D) were used to analyze species diversity in forest communities. The results showed that: (1) the community was rich in plant resources, with a total of 50 species belonging to 40 genus and 31 families, including 19 species in tree layer, 22 species in shrub layer and only 9 species in herb layer, few plant species; (2) the species richness and diversity index of tree layer and shrub layer were significantly higher than that of herb layer, but there were differences among different communities in the same layer, and no significant difference was reached; (3) the species richness and community diversity of the six communities showed as follows: community VI > community I > community II > community IV > community V > community III.

The use of geotechnologies combined with remote sensing has become increasingly essential and important for efficiently and economically understanding land use and land cover in specific regions. The objective of this study was to observe changes in agricultural activities, particularly agriculture/livestock farming, in the North Forest Zone of Pernambuco (Mata Norte), a political-administrative region where sugarcane cultivation has historically been the backbone of the local economy. The region’s sugarcane biomass also contributes to land use and land cover observations through remote sensing techniques applied to digital satellite images, such as those from Landsat-8, which was used in this study. This study was conducted through digital image processing, allowing the calculation of the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), and the Leaf Area Index (LAI) to assess vegetation cover dynamics. The results revealed that sugarcane cultivation is the predominant agricultural and vegetation activity in Mata Norte. Livestock farming areas experienced a significant reduction over the observed decade, which, in turn, led to an increase in agricultural and forested areas. The most dynamic spatiotemporal behavior was observed in the expansion and reduction of livestock areas, a more significant change compared to sugarcane areas. Therefore, land use and land cover in this region are more closely tied to sugarcane cultivation than any other agricultural activity.

The structure and diversity of tree species in a temperate forest in northwestern Mexico was characterized. Nine sampling sites of 50 × 50 m (2,500 m²) were established, and a census of all tree species was carried out. Each individual was measured for total height and diameter at breast height. The importance value index (IVI) was obtained, calculated from the variable abundance, dominance and frequency. The diversity and richness indices were also calculated. A total of 12 species, four genera and four families were recorded. The forest has a density of 575.11 individuals and a basal area of 23.54/m². The species of Pinus cooperi had the highest IVI (79.05%), and the Shannon index of 1.74.

Fire, a phenomenon occurs in most parts of the world and causes severe financial losses, even, irreparable damages. Many parameters are involved in the occurrence of a fire; some of which are constant over time (at least in a fire cycle), but the others are dynamic and vary over time. Unlike the earthquake, the disturbance of fire depends on a set of physical, chemical, and biological relations. Monitoring the changes to predict the occurrence of fire is efficient in forest management. Method: In this research, the Persian and English databases were structurally searched using the keywords of fire risk modeling, fire risk, fire risk prediction, remote sensing and the reviewed papers that predicted the fire risk in the field of remote sensing and geographic information system were retrieved. Then, the modeling and zoning data of fire risk prediction were extracted and analyzed in a descriptive manner. Accordingly, the study was conducted in 1995-2017. Findings: Fuzzy analytic hierarchy process (AHP) zoning method was more practical among the applied methods and the plant moisture stress measurement was the most efficient among the remote sensing indices. Discussion and Conclusion: The findings indicate that RS and GIS are effective tools in the study of fire risk prediction.

This research is based on the condition of the ever-rampant events of illegal logging perpetrated by companies in various areas in Indonesia and Malaysia. The issue of corporate illegal logging happened due to a concerning level of conflict of interest between companies, the government, and local societies due to economic motives. this paper aims to analyze the law enforcement on corporate illegal logging in Indonesia and Malaysia as well as the law enforcement on corporate illegal logging that is based on sustainable forestry. this research used the normative legal approach that was supported by secondary data in the forms of documents and cases of illegal logging that happened in Indonesia and Malaysia. this paper employed the qualitative analysis. Results showed that Indonesia had greater commitment and legal action than Malaysia because Indonesia processed more illegal logging cases compared to Malaysia. But mere commitment is not enough as the illegal logging ratio in Indonesia compared to timber production is 60%. meanwhile, in Malaysia, it is 35%. This shows that the ratio of law enforcement in Malaysia is more effective when comparing the rate of illegal logging and timber production. The phenomenon of forest destruction in Indonesia happened due to a disharmonic situation or an improper social relationship between society, the regional government, the forestry sector, business owners, and the law-enforcing apparatus. The sustainable forest-based law enforcement concept against corporate illegal logging is carried out through the integration approach that involves various parties in both countries.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.