Hyperspectral remote sensing and sustainable forest management

Hyperspectral Forestry Management

Sustainable forest management increases the focus on economic value, with a primary focus on The maintenance or improvement of species diversity, structure, and current and future ecosystem functions and biological productivity.The shift from old-fashioned, traditional forest management methods that emphasize timber value to sustainable forest management is profound—a challenging time for considering the future direction of forests and forestry.

The application of remote sensing in sustainable forest management is generally divided into four categories, including Forest cover type classification, estimation of forest structure and resource inventory variables (i.e., tree species, height, age, and canopy closure), forest change detection, and forest modeling.From this perspective, other variables provided by remote sensing (which may not currently be in high demand in forest management) will also be considered due to their potential role in future management scenarios. To better understand their function and response to environmental change, it is necessary to extend existing knowledge at the stand level to the scale of entire forests or small regions. Therefore, the distinction between inventory variables and indirect input/output variables in forest productivity models becomes less important, as the demand for ecological applications, modeling, and integrated forest structure information in forest management will continue to grow.

In recent years, forest ecosystems have faced immense pressure from environmental changes such as global warming and population growth. In this context, developing adaptive management strategies is crucial, and as this task progresses, along with the increasing need to understand the role of forests in the global carbon budget (e.g., the Kyoto Protocol), quantitative analysis of forest ecosystems is essential to better understand their key processes. The most important processes in forests include carbon exchange (photosynthesis and respiration), evapotranspiration, and nutrient cycling. At the local level, these processes are difficult to measure; from a regional to a global scale, they are simply impossible to measure. Therefore, mass and energy flux simulation models have been developed. These simulation models are needed to accurately estimate forest structure and chemical properties as input (for initialization) or for validation purposes.

Related studies have demonstrated how to use leaf and canopy variables obtained from remote sensing to constrain ecosystem models. These methods require a spatially continuous input of ecosystem state at the start of the simulation and assimilate relevant biophysical and biochemical state variables obtained from remote sensing. Hyperspectral imaging systems have been emphasized as being able to retrieve relevant vegetation characteristics in greater detail and accuracy, among which the most important vegetation parameters are leaf chlorophyll and nitrogen content, photosynthetically active radiation absorption ratio, canopy water content, annual maximum leaf weight per unit area, and annual maximum leaf area.

Forest inventory data used for management typically exists as quantitative attributes derived from field surveys or as forest cover maps interpreted from aerial imagery. Survey attributes include tree height, stem density, volume, tree species, and age. Traditionally, forest inventories are repeated every 10 years or longer, usually targeting nationally or municipally owned forests rather than privately owned ones. Local and state forestry agencies are particularly interested in such inventory data because privately owned forests can have significant economic and ecological importance. Therefore, remote sensing methods capable of mapping important forest attributes such as species type, age class, canopy volume, and biomass would be of great value.

Therefore, hyperspectral remote sensing will focus on forestry applications where hyperspectral imaging systems are expected to make significant contributions, rather than technical aspects such as image acquisition, image calibration, and processing. Starting from the goals identified in sustainable forest management and ecosystem-based management approaches, the focus will be on forest classification (stand species composition, density, canopy closure, height, and age), forest structure estimation (continuous variable estimation), and forest change detection (natural disturbances, such as forest destruction, defoliation, and inactivation due to disasters, fires, fragmentation, etc.).

Forest species classification