The forest biome is vital to the health of the earth. Canada and the United States have a combined forest area of 4.68
Mkm2. The monitoring of these forest resources has become increasingly complex. Hyperspectral remote sensing can
provide a wealth of improved information products to land managers to make more informed decisions. Research in this
area has demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest
inventory (major forest species), forest health, foliar biochemistry, biomass, and aboveground carbon. Operationally
there is a requirement for a mix of airborne and satellite approaches. This paper surveys some methods and results in
hyperspectral sensing of forests and discusses the implications for space initiatives with hyperspectral sensing
In this paper we first highlight a new approach to the analysis of compact polarimetric imaging radar data, based on decomposition theory. We then use a time series of Radarsat-2 quadpol data acquisitions collected over the autumn and winter of 2011/12 for a calibrated forest test site near Hinton in Alberta, Canada, which contains a mixed forest, seminatural vegetation and mountainous terrain environment. This data is collected in the new wide-swath quadpol mode FQW of Radarsat-2, which matches the wider range swath capability of any future compact mode. This data is first used to simulate compact mode using circular polarization transmit and dual linear receive and the co-registered multitemporal stack then employed for a rule-based classifier to determine land-use types compared against a reference landuse map. We compare the information obtained from compact against a standard dualpol linear transmit and dual linear receive, as proposed for example in the ESA Sentinel missions, to confirm the utility of using circular polarization for enhanced land-use products at C-band.
Canada contains 10% of the world's forests covering an area of 418 million hectares. The sustainable management of
these forest resources has become increasingly complex. Hyperspectral remote sensing can provide a wealth of new and
improved information products to resource managers to make more informed decisions. Research in this area has
demonstrated that hyperspectral remote sensing can be used to create more accurate products for forest inventory, forest
health, foliar biochemistry, biomass, and aboveground carbon than are currently available. This paper surveys recent
methods and results in hyperspectral sensing of forests and describes space initiatives for hyperspectral sensing.
Our objective is to integrate transformational analogy, derivational analogy, and goal- regression to create solutions for an intelligent system called SEIDAM (System of Experts for Intelligent Data Management). SEIDAM answers queries about forests and the environment through the integration of remote sensing, geographic information, models, and field measurements. A query (problem) could require, for example, that a forest inventory stored in a geographical information system be updated to reflect past harvesting by overlaying current satellite imagery over forest cover maps. A case consists of a query, remote sensing data, and geographic information, and the analysis methods to answer the query. SEIDAM will consist of approximately 150 expert systems performing satellite and aircraft image analysis, integrated to multiple GIS and a relational database. Derivational analogy provides the means by which this search can be expanded knowledgeably; i.e., provide a knowledge-based approach justifying the expansion of the search. Transformational analogy eliminates the problems associated with searching by foregoing a search altogether. The advantage is that the intractability of exploring the search space is no longer a consideration.
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