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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.

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.