In Situ/Remote Sensing Integration to Assess Forest Health—A Review

Abstract

For mapping, quantifying and monitoring regional and global forest health, satellite remote sensing provides fundamental data for the observation of spatial and temporal forest patterns and processes. While new remote-sensing technologies are able to detect forest data in high quality and large quantity, operational applications are still limited by deficits of in situ verification. In situ sampling data as input is required in order to add value to physical imaging remote sensing observations and possibilities to interlink the forest health assessment with biotic and abiotic factors. Numerous methods on how to link remote sensing and in situ data have been presented in the scientific literature using e.g. empirical and physical-based models. In situ data differs in type, quality and quantity between case studies. The irregular subsets of in situ data availability limit the exploitation of available satellite remote sensing data. To achieve a broad implementation of satellite remote sensing data in forest monitoring and management, a standardization of in situ data, workflows and products is essential and necessary for user acceptance. The key focus of the review is a discussion of concept and is designed to bridge gaps of understanding between forestry and remote sensing science community. Methodological approaches for in situ/remote-sensing implementation are organized and evaluated with respect to qualifying for forest monitoring. Research gaps and recommendations for standardization of remote-sensing based products are discussed. Concluding the importance of outstanding organizational work to provide a legally accepted framework for new information products in forestry are highlighted.

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

“…Various studies have shown that the implementation of multi-sensor RS approaches improves the discrimination of ST/STV over time and thus the accuracy of estimation of FH indicators [14,[134][135][136][137]190]. Depending on their sensor characteristics (spatial, radiometric, spectral, temporal or angular resolution), RS sensors can record specific ST/STV and thus discriminate between certain species, populations, communities, habitats and biomes of FES [16,136].…”

“…To provide but one example: in the 364-page report for 46 European countries, the term "remote sensing" occurs only once where it is stated that RS and geographic information systems are new technologies that will be used by some countries [2]. Pause et al [14] concluded that a major advantage of implementing remote-sensing techniques is the possibility of repeatedly acquiring standardized information over large areas at low costs with high frequency. However, RS techniques are only helpful to improve the objectivity of FHM when they can be reliably linked to FH indicators.…”

“…Estimation of ST/STV of FH [14,16,138,139] Implementation of multi-sensor RS in modelling approaches improves the discrimination, quantification and accuracy when estimating ST/STV. [197] Multiple platforms for a given sensor type, e.g., the forthcoming Radarsat Constellation of three platforms provides potential for more frequent data acquisition.…”

Linking canopy reflectance to crop structure and photosynthesis to capture and interpret spatiotemporal dimensions of per-field photosynthetic productivity

“…While plot data provide the most detailed information on rice carbon and water vapor gas exchange, applying this understanding to broader spatial and temporal domains requires scaling approaches. As mentioned before, the field niche between in situ plot and regional dimension is supposed to be a key chain of a spatially hierarchical remote-sensing network (Masek et al, 2015;Pause et al, 2016). Applications of the data fusion at the microsite/field scale that combine observations of in situ canopy structure and function with field crop information derived from the UAV system capture critical growth information of rice crop in space.…”

“…Applications of spatially coarse satellite products generate considerable spatiotemporal uncertainties in evaluating strength of daily carbon fluxes among microsites of the same plant function type at principle growth stages. Multi-pragmatic solutions are suggested to develop spatial/temporal data fusions that integrate spatially hierarchical remote-sensing networks and in situ ground surface observations Pause et al, 2016), aiming to better monitor canopy dynamics and environmental impacts on them.…”