Remote sensing (RS) and Geographic Information Systems (GISs) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in situ validation methods. This article reviews key image analysis tools in natural resource management, highlighting their unique strengths across diverse applications such as agriculture, forestry, water resources, soil management, and natural hazard monitoring. Google Earth Engine (GEE), a cloud-based platform introduced in 2010, stands out for its vast geospatial data catalog and scalability, making it ideal for global-scale analysis and algorithm development. ENVI, known for advanced multi- and hyperspectral image processing, excels in vegetation monitoring, environmental analysis, and feature extraction. ERDAS IMAGINE specializes in radar data analysis and LiDAR processing, offering robust classification and terrain analysis capabilities. Global Mapper is recognized for its versatility, supporting over 300 data formats and excelling in 3D visualization and point cloud processing, especially in UAV applications. eCognition leverages object-based image analysis (OBIA) to enhance classification accuracy by grouping pixels into meaningful objects, making it effective in environmental monitoring and urban planning. Lastly, QGIS integrates these remote sensing tools with powerful spatial analysis functions, supporting decision-making in sustainable resource management. Together, these tools when paired with in situ data provide comprehensive solutions for managing and analyzing natural resources across scales.

Remote sensing (RS) and Geographic Information Systems (GIS) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in-situ validation methods. This article reviews a range of image analysis tools applicable to natural resource management, including agriculture, water, forests, soil, and natural hazards, and compares their functionalities. Our study highlights that Google Earth Engine (GEE) is favored for wide-area analysis due to its extensive coverage and free access. Global Mapper excels in 3D and light detection and ranging (LIDAR) data, environment for visualizing images (ENVI) specializes in multi- and hyperspectral image processing, ERDAS IMAGINE is optimal for radar data, and eCognition is used for object-based image analysis. The article emphasizes the importance of in-situ validation data, which provides essential ground truth information to calibrate and validate RS models, thereby enhancing accuracy. Understanding these tools and integrating them with in-situ validation techniques enables natural resource managers to improve their monitoring and decision-making processes, facilitating effective collaboration with RS researchers.

Accurate scaling of outer-xylem sap flux observations to whole-tree transpiration is highly dependent on the proper characterization of sap flow with radial depth. While radial variability of sap flow has been identified as a substantial source of error in estimating whole-tree transpiration, there has yet to be a universal adoption of a single scaling method. This study evaluated the temporal dependency of the radial profile of sap flux on seasonality and meteorological drivers and assessed the accuracy of six radial profile estimation methods of varying complexity in comparison with a robust multi-point estimation of sap flow. Of the six scaling approaches examined in this study, the use of a linear regression to compare outer flow rates with whole tree flow generated the least error when compared with the multi-point estimation (0.6% error). A cross validation demonstrated that statistical models remain accurate when applied to individuals not included in the training of the statistical models (mean adjusted R2 = 0.961). These findings suggest that future sap flux studies seeking to scale to whole-tree or stand level could robustly instrument a subset of trees with multiple radial depth measurement points to train one of the statistical models described in this study. These models can then be applied to additional trees instrumented with a single measurement point in the outer xylem, reducing the cost of deployment and potential sources of error.