Spatial Scaling Analysis in Gross Primary Production Estimation

Li, Shihua; Xiao, Junjie; Hu, Zongchen; Li, Zhaoyu; Zhao, Lusheng

doi:10.1007/978-3-642-27957-7_32

Cited by 2 publications

(1 citation statement)

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“…In part, the accuracy of the retrieval of vegetation properties using remote sensing depends upon sensor spatial resolution [ 136 ]. Using remote sensing data, especially low spatial resolution data, such as the 1 km spatial resolution Advanced Very High Resolution Radiometer (AVHRR), for ecosystem health assessment may introduce uncertainties resulting from land surface heterogeneity and mixed pixels containing more land cover types [ 137 ]. GPP calculated from the Region Production Efficiency Model (REG-PEM, [ 138 ]) with all model inputs obtained from AVHRR 1 km remote sensing data is significantly different from the GPP calculated using Landsat TM 30 m data [ 137 ].…”

Section: Challenges To Developing a Remote Sensing Based Eha Systemmentioning

confidence: 99%

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

Guo

2014

Sensors

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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.

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