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.

Ice storms are a type of extreme winter weather event common to north temperate and boreal forests worldwide. Recent climate modelling studies suggest that these storms may become more frequent and severe under a changing climate. Compared to other types of storm events, relatively little is known about the direct and indirect impacts of these storms on forests, as naturally occurring ice storms are inherently difficult to study. Here we describe a novel experimental approach used to create a suite of ice storms in a mature hardwood forest in New Hampshire, USA. The experiment included five ice storm intensities (0, 6.4, 12.7 and 19.1 mm radial ice accretion) applied in a single year, and one ice storm intensity (12.7 mm) applied in two consecutive years. Results demonstrate the feasibility of this approach for creating experimental ice storms, quantify the increase in fine and coarse woody debris mass and nutrients transferred from the forest canopy to the soil under the different icing conditions, and show an increase in the damage to the forest canopy with increasing icing that evolves over time. In this forest, little damage occurred below 6.4 mm radial ice accretion, moderate damage occurred with up to 12.7 mm of accretion, and significant branch breakage and canopy damage occurred with 19.1 mm of ice. The icing in consecutive years demonstrated an interactive effect of ice storm frequency and severity such that some branches damaged in the first year of icing appeared to remain in the canopy and then fall to the ground in the second year of icing. These results have implications for National Weather Service ice storm warning levels, as they provide a quantitative assessment of ice-load related inputs of forest debris that will be useful to municipalities creating response plans for current and future ice storms.

We evaluated shoot nonstructural carbohydrate (NSC) concentrations, stem wound closure, and radial growth of sugar maple ( Acer saccharum Marsh.) and red maple ( Acer rubrum L.) trees in a novel ice storm experiment in which five storm treatments (0, 6.4, 12.7, and 19.1 mm of radial ice accretion in 1 year and 12.7 mm of ice in two consecutive years) were applied within a mature northern hardwood forest. We tested for changes in physiology at two levels: (1) associated with plot-level ice treatments and (2) with crown damage classes of individual trees. Few differences in NSC or wound closure associated with treatment were found. Growth decreased for red maple in the medium and high treatments and sugar maple in the high treatment but no other treatments. Changes in physiology were more evident when assessed using crown damage classes. Two NSC components were elevated in sugar and red maples with high (≥50%) crown damage. Wound closure was less for red maples with high damage, and separation among damage classes was even greater for sugar maple. Red maples with moderate (<50%) and high crown damage showed gradually declining growth, whereas sugar maples with high damage showed ∼80% reduction in growth the first year after injury.

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.

Ice storms might sound fascinating, but for forests, they are not as cool as they seem! Ice storms happen when freezing rain coats trees, branches, and the forest floor in a shiny, frozen glaze. These storms can be a big deal because they can affect our forests and the things we care about, like, wood, wildlife, and places for recreation. Despite this, we do not know very much about how ice storms affect forests. Scientists at the Hubbard Brook Experimental Forest, New Hampshire (USA), decided to learn more. They used fire-fighting pumps and hoses to create experimental ice storms in the forest during the winters of 2016 and 2017. They found that these experimental storms caused a lot of short- and long-term damage to trees, including breaking of twigs and branches. In a nutshell, ice storms are not just about frozen beauty—they have real consequences for our forests and species that are not used to ice!