Remote Sensing of Ecosystem Health: Opportunities, Challenges, and Future Perspectives

Abstract

Maintaining a healthy ecosystem is essential for maximizing sustainable ecological services of the best quality to human beings. Ecological and conservation research has provided a strong scientific background on identifying ecological health indicators and correspondingly making effective conservation plans. At the same time, ecologists have asserted a strong need for spatially explicit and temporally effective ecosystem health assessments based on remote sensing data. Currently, remote sensing of ecosystem health is only based on one ecosystem attribute: vigor, organization, or resilience. However, an effective ecosystem health assessment should be a comprehensive and dynamic measurement of the three attributes. This paper reviews opportunities of remote sensing, including optical, radar, and LiDAR, for directly estimating indicators of the three ecosystem attributes, discusses the main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system, and provides some future perspectives. The main challenges to develop a remote sensing-based spatially-explicit comprehensive ecosystem health system are: (1) scale issue; (2) transportability issue; (3) data availability; and (4) uncertainties in health indicators estimated from remote sensing data. However, the Radarsat-2 constellation, upcoming new optical sensors on Worldview-3 and Sentinel-2 satellites, and improved technologies for the acquisition and processing of hyperspectral, multi-angle optical, radar, and LiDAR data and multi-sensoral data fusion may partly address the current challenges.

Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring

“…A combination of remotely sensed data along common time series from multiple source can be only achieved if their consistency is demonstrated, or a proper data integration strategy is adopted [22,23]. Many works in literature report experiences concerning data integration of S2 and L8 datasets for different issues like geological [24], forest, environmental applications [25,26], and urban sprawl monitoring [27]. In general, these works do not refer to the consistency of spectral and geometric features of datasets, giving it as a fact.…”

Remote Sensing of Woodland Structure and Composition in the Sudano-Sahelian zone : Application of WorldView-2 and Landsat 8

“…This research has obviously contributed key knowledge about many aspects of vegetation change in the SSZ. However, a more comprehensive understanding would be possible if additional vegetation attributes were integrated in the analysis, or if vegetation change was observed simultaneously at multiple spatial scales (Li, Xu, and Guo 2014). For example, AVHRR data could initially be used to detect areas where changes in vegetation productivity and/or phenology have occurred.…”

Biophysical Characterization of Protected Areas Globally through Optimized Image Segmentation and Classification

“…Strict validation with other sources of information is not always possible since results vary depending on the environmental variables and stratification method used. Rather than focusing on the spatial accuracy of the HFTs as if they were categorical units, here we focus on the overall biophysical gradients identified in terms of the degree of habitat dissimilarities found in PAs, which can be used as a proxy for habitats and species diversity [88,89]. Rather than comparing different maps of habitats or ecosystem types with the obtained HFTs, we think that finding correlations between species and HFT composition was a more meaningful way of testing our results, provided that: (1) species data available are spatially explicit; (2) were systematically sampled; and (3) the environmental variables used for the analysis are relevant for the selected species.…”