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Artisanal small-scale mining (ASM) is an environmentally damaging activity in many developing countries, particularly in the wet tropics, yet serves as a crucial economic resource for millions of people. The lack of effective mapping methods hinders quantifying the spatial extent of ASM and management efforts. This study presents a novel approach to integrate multi-spectral and imaging radar datasets within the Google Earth Engine (GEE) platform to map ASGM in a tropical rainforest. We used a case study of gold mining in central Kalimantan and diverse training and validation data sources. The methodology involved pre-processing multispectral and radar imagery, generating and standardizing covariates, applying feature-level data fusion for the Random Forest algorithm in GEE, and training and classifying data with optimized parameters through iterative loops. This approach achieved a classification accuracy of 81% in detecting ASM activities, surpassing the accuracy of a map constructed solely from Sentinel-2 multispectral data by 14%. Through the inclusion of evaluation metrics such as the f(β) score and Matthews Correlation Coefficient (MCC), our approach demonstrates its robustness in accurately identifying target instances, while reducing false positives and addressing imbalanced class sizes by 6.25% and 60%, respectively. Our model’s efficacy underscores its potential to accurately map ASM at larger regional scales (104 – 10⁶ km2) in wet-tropical forests, while being scalable and resource-efficient. Opportunities to further improve this approach by mitigating false-positive errors involve integrating texture filtering with optical and radar data sets. Despite some inherent limitations, our approach overcomes some current challenges of mapping small-scale, but extensive, environmental changes in the wet tropics and thus advances improvements in the continual surveillance, management, and regulation of ASM and other activities that involve selective clearing.

Understanding land-use dynamics and patterns and how they respond to management scenarios helps to develop sustainable land use policies. This study simulated future land uses in Brantas River Basin (BRB), East Java, Indonesia using Land Change Modeler (LCM) under policy scenarios. Following identification of twelve important biophysical and socio-economic spatial drivers, the LCM accurately simulated land-use changes over the period 1995–2015, with validation against the 1995 land use map giving an overall accuracy of 85–88 %. Under a business-as-usual scenario, the LCM predicted that during the period 2015 to 2035 continuing deforestation may leave forest cover accounting for only 4 % of the total BRB area, and cause declines in dryland from 45 % to 40 % and in rice-field farming from 24 % to 22 %. The results of that scenario also indicate the prospect of massive urban development, increasing from 18 % of the BRB area in 2015 to 30 % in 2035, with increasing threats to food security and water resources. Both percentage changes and a mean-weighted fractal dimension index suggest increased risks of forest fragmentation and urban aggregation. A spatial planning-influenced scenario, which aims to reduce forest loss and support watershed protection, is predicted to lead to 5.5 % forest cover, 37 % dryland, 29 % rice-field farming and 24 % urban area in 2035. Sensitivity analysis of the LCM showed that the model results were more sensitive to drivers, spatial resolution, and policy scenarios than to uncertainty in model parameters. It is concluded that despite some limitations, the LCM successfully provided'' insights into policy impacts on land-uses in BRB, and the roles of forest and rice-field protection in spatial planning are essential in controlling urban development for watershed sustainability.

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R2 = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R2 = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km2 due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R2 = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R2 = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km2–100 km2). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R2 = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R2 = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.