Forest applications with hyperspectral imaging

The world's forests generate oxygen and store carbon, mitigating global climate change. Monitoring the health of these forests is an international priority, which benefits from the use of remote sensing. With hyperspectral sensors capable of discerning forest species and foliar chemistry, many forest information products can be generated including maps of forest species, canopy chemistry, biomass, and carbon. Methods and challenges related to forest monitoring and mapping are discussed, with reference to our past and current work in the field of hyperspectral imaging, and with an emphasis on biomass and carbon mapping. The processes explained in this paper have been tested primarily on a study site on Vancouver Island, Canada.

[1]  Randolph H. Wynne,et al.  Fusion of Small-Footprint Lidar and Multispectral Data to Estimate Plot- Level Volume and Biomass in Deciduous and Pine Forests in Virginia, USA , 2004, Forest Science.

[2]  D. Lu.  The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[3]  Tian Han,et al.  Investigation of Nonlinearity in Hyperspectral Imagery Using Surrogate Data Methods , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[4]  R. Dubayah,et al.  Integrating waveform lidar with hyperspectral imagery for inventory of a northern temperate forest , 2008 .

[5]  K. Olaf Niemann,et al.  Mapping spatio-temporal variation in Douglas-fir (Pseudotsuga menziesii) foliar biochemistry , 2009, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing.

[6]  M. Herold,et al.  Mapping biomass with remote sensing: a comparison of methods for the case study of Uganda , 2011, Carbon balance and management.

[7]  Hao Chen,et al.  Comparison of Aviris and AISA Airborne Hyperspectral Sensing for Above-Ground Forest Carbon Mapping , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[8]  David G. Goodenough,et al.  Calibration of forest chemistry for hyperspectral analysis , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[9]  Stefano Pignatti,et al.  Experimental Approach to the Selection of the Components in the Minimum Noise Fraction , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Hans Pretzsch,et al.  Biomass estimation as a function of vertical forest structure and forest height - Potential and limitations for Radar Remote Sensing , 2010 .

[11]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[12]  Qian Du,et al.  Change Analysis for Hyperspectral Imagery , 2007, 2007 International Workshop on the Analysis of Multi-temporal Remote Sensing Images.

[13]  Hailemariam Temesgen,et al.  Imputing tree-lists from aerial attributes for complex stands of south-eastern British Columbia , 2003 .

[14]  Hao Chen,et al.  A framework for efficiently parallelizing nonlinear noise reduction algorithm , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[15]  K. Olaf Niemann,et al.  Lidar integrated airborne imaging spectroscopy for root disease detection and measurement of foliar chemistry , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[16]  Hao Chen,et al.  Comparison of AVIRIS and AISA for chemistry mapping , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[17]  S. Popescu,et al.  Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass , 2003 .

[18]  David G. Goodenough,et al.  Comparing and validating spectral unmixing algorithms with AVIRIS imagery , 2008 .

[19]  Hao Chen,et al.  Processing Hyperion and ALI for forest classification , 2003, IEEE Trans. Geosci. Remote. Sens..

[20]  Arief Wijaya,et al.  Improved strategy for estimating stem volume and forest biomass using moderate resolution remote sensing data and GIS , 2010, Journal of Forestry Research.

[21]  Mark G. Tjoelker,et al.  Crown structure and biomass allocation patterns modulate aboveground productivity in young loblolly pine and slash pine , 2007 .

[22]  Hao Chen,et al.  Forest information products from hyperspectral data — Victoria and Hoquiam test sites , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[23]  John E. Major,et al.  Impact of three silvicultural treatments on growth, light-energy processing, and related needle-level adaptive traits of Pinus strobus from two regions , 2009 .