Spatially explicit information on forest management at a global scale is critical for understanding the status of forests, for planning sustainable forest management and restoration, and conservation activities. Here, we produce the first reference data set and a prototype of a globally consistent forest management map with high spatial detail on the most prevalent forest management classes such as intact forests, managed forests with natural regeneration, planted forests, plantation forest (rotation up to 15 years), oil palm plantations, and agroforestry. We developed the reference dataset of 226 K unique locations through a series of expert and crowdsourcing campaigns using Geo-Wiki (https://www.geo-wiki.org/). We then combined the reference samples with time series from PROBA-V satellite imagery to create a global wall-to-wall map of forest management at a 100 m resolution for the year 2015, with forest management class accuracies ranging from 58% to 80%. The reference data set and the map present the status of forest ecosystems and can be used for investigating the value of forests for species, ecosystems and their services.

Unplanned urban growth, particularly in developing countries has led to changes in land use/land cover (LULC). Numerous Indian cities face problems of unplanned LULC change due to nominal or non-existent planning efforts compounded by rapid urban population growth. The Guwahati Metropolitan Area (GMA) is one such urban centre. The present study assesses the trajectories of LULC change using Landsat imageries acquired in 1976, 1989, 2002 and 2015. Natural and semi natural vegetated area and artificial and natural water bodies decreased while built-up areas, cultivated and managed areas, and natural and semi natural non-vegetated areas increased. The built-up area increased from 23.9 in 1976 to 115.1 km² in 2015 becoming the dominant land cover class accounting for 41.8% of the total geographical area. During this period, natural and semi natural vegetated land were reduced by 88.9 km² at an annual rate of 2.2 km². Over the years there was an increasing trend of built-up land and cultivated and managed areas in the peripheral areas of the city while natural and semi natural vegetated land diminished. Consequently, as in many other developing countries, there is an urgent need for the governmental authorities and other stakeholders to implement effective urban planning policies.

Floods represent a significant threat to human life, property, and agriculture, especially in low-lying floodplains. This study assesses flood susceptibility in the Brahmaputra River basin, which spans China, India, Bhutan, and Bangladesh—an area notorious for frequent flooding due to the saturation of river water intake capacity. We developed and evaluated several innovative models for predicting flood susceptibility by employing Multi-Criteria Decision Making (MCDM) and Machine Learning (ML) techniques. The models showed robust performance, evidenced by Area Under the Receiver Operating Characteristic Curve (AUC-ROC) scores exceeding 70% and Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) scores below 30%. Our findings indicate that approximately one-third of the studied region is categorized as moderately to highly flood-prone, while over 40% is classified as low to very low flood-risk areas. Specific regions with high to very high flood susceptibility include Dhemaji, Dibrugarh, Lakhimpur, Majuli, Darrang, Nalbari, Barpeta, Bongaigaon, and Dhubri districts in Assam; Coochbihar and Jalpaiguri districts in West Bengal; and Kurigram, Gaibandha, Bogra, Sirajganj, Pabna, Jamalpur, and Manikganj districts in Bangladesh. Owing to their strong performance and the suitability of the training datasets, we recommend the application of the developed MCDM techniques and ML algorithms in geographically similar areas. This study holds significant implications for policymakers, regional administrators, environmentalists, and engineers by informing flood management and prevention strategies, serving as a climate change adaptive response within the Brahmaputra River basin.

Bank erosion hazard is a frequent occurrence that poses threats to floodplain ecosystems. This analysis examined changes to the Jia Bharali River channel in India using the GIS-based Digital Shoreline Analysis System [DSAS]. The Jia Bharali’s future channel was predicted so as to identify the most erosion-susceptible zones. The rate of bankline movement was calculated using remotely sensed data collected over a period of 45 years (1976–2021). The results show that the river’s erosion and deposition rates were higher in the early years than towards the later part of the period under analysis. On the right and left banks of the river, the average shift rate was −9.22 and 5.8 m/y, respectively, which is comparatively high. The chosen portion of the river was evenly divided into three zones, A, B, and C. The most positively affected zone was zone A. The left bank of zone B exhibited a higher rate of erosion than the right bank, indicating that the river was moving to the left [eastward] in this zone. At the same time, the right bank was being eroded faster than the left, indicating a westward thrust at zone C. The predicted result demonstrates that the left bank of zone B and the right bank of zone C would have a higher average migration rate. Therefore, these banks were identified as being the most susceptible to bank erosion. The study evaluates the spatio-temporal change of the river in sensitive regions where neighboring settlements and infrastructure were at risk of changing channel dynamics. Using the actual and forecasted bankline, the degree of accuracy was confirmed. The results of the automated prediction approach could be useful for river hazard management in the Jia Bharali and in similar environmental settings with tropical high precipitation zones.