Verification of the potential of various remote sensing data sources for forest inventory

Hyyppä, Juha; Hyppa, H.; Inkinen, M.; Engdahl, Marcus

doi:10.1109/igarss.1998.703660

Cited by 3 publications

(6 citation statements)

References 4 publications

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“…For the SLICER and TM images, techniques used in previous work (i.e., Cohen and Spies 1992;Lefsky et al 1999b) were adopted without considerable modification. For the AVIRIS image, we used principal component analysis to reduce the dimensionality of the original images so they could be analyzed in a common statistical framework, as in Hyppa et al (1998). For the ADAR high spatial resolution images, our approach was to use absolute difference filters at a range of resolutions to capture the high-resolution texture of the images.…”

Section: Methodsmentioning

confidence: 99%

“…Images were then registered using the automated GCP identification routine. The spatial resolution of the georeferenced image was 25 m. To simplify analysis, principal component analysis was performed to reduce the large number of spectral bands to 20 principal components, following Hyppa et al (1998).…”

Section: Avirismentioning

confidence: 99%

“…These include hyperspectral and high spatial resolution (hyperspatial) optical sensors, as well as radar and lidar sensors. There have been only a few comparisons of the relative utility of these new and established devices (e.g., Hyppa et al 1998), and as a result, both remote sensing specialists and users have little information on which to make an informed selec-tion for their purposes. The goal of this study was to provide such a comparison, for the specific purpose of remotely measuring forest structural attributes at a regional scale, for the Douglas-fir -western hemlock (Pseudotsuga menziesii (Mirb.)…”

Section: Introductionmentioning

confidence: 99%

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An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon

Lefsky¹,

Cohen²,

Spies³

2001

Can. J. For. Res.

166

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This research evaluates the utility of several remote sensing data types for the purpose of mapping forest structure and related attributes at a regional scale. Several sensors were evaluated, including (i) single date Landsat Thematic Mapper (TM); (ii) multitemporal Landsat TM; (iii) Airborne Data Acquisition and Registration (ADAR), a sensor with high spatial resolution; (iv) Airborne Visible-Infrared Imaging Spectrometer (AVIRIS), a sensor with high spectral resolution; and (v) Scanning Lidar Imager Of Canopies By Echo Recovery (SLICER), a lidar sensor that directly measures the height and canopy structure of forest vegetation. To evaluate the ability of each of the sensors to predict stand structure attributes, we assembled a data set consisting of 92 field plots within the Willamette National Forest in the vicinity of the H.J. Andrews Experimental Forest. Stand structure attributes included age, basal area, aboveground biomass, mean diameter at breast height (DBH) of dominant and codominant stems, mean and standard deviation of the DBH of all stems, maximum height, and the density of stems with DBH greater than 100 cm. SLICER performed better than any other remote sensing system in its predictions of forest structural attributes. The performance of the imaging sensors (TM, multitemporal TM, ADAR, and AVIRIS) varied with respect to which forest structural variables were being examined. For one group of variables there was little difference in the ability of the these sensors to predict forest structural attributes. For the remaining variables, we found that multitemporal TM was as or more effective than either ADAR or AVIRIS. These results indicate that multitemporal TM should be investigated as an alternative to either hyperspectral or hyperspatial sensors, which are more expensive and more difficult to process than multitemporal Landsat TM.

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Section: Methodsmentioning

confidence: 99%

Section: Avirismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon

Lefsky¹,

Cohen²,

Spies³

2001

Can. J. For. Res.

166

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“…The spatial resolution is of 3-4 m, dependent on the amount of returning samples; the pixel density decreases from the center (nadir) to the corners of the image. From the LIDAR raw data, forest-height and ground terrain digital elevation models (DEM) have been processed [16], [17]. During the three years between the LIDAR and the radar campaign, changes in the forest caused by tree growth (on the order of 1-2 m), tree dieback, and human impact may be an additional error source when comparing LIDAR and radar measurements to each other.…”

Section: B Test Sites and Ground Measurementsmentioning

confidence: 99%

Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign

Hajnsek

Kugler

Lee

et al. 2009

IEEE Trans. Geosci. Remote Sensing

241

186

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This paper addresses the potential and limitations of polarimetric synthetic aperture radar (SAR) interferometry (Pol-InSAR) inversion techniques for quantitative forest-parameter estimation in tropical forests by making use of the unique data set acquired in the frame of the second Indonesian Airborne Radar Experiment (INDREX-II) campaign-including Pol-InSAR, light detection and ranging (LIDAR), and ground measurements-over typical Southeast Asia forest formations. The performance of Pol-InSAR inversion is not only assessed primarily at Land P-band but also at higher frequencies, namely, X-band. Critical performance parameters such as the "visibility of the ground" at Land P-band as well as temporal decorrelation in shorttime repeat-pass interferometry are discussed and quantitatively assessed. Inversion performance is validated against LIDAR and ground measurements over different test sites.

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“…Field-based methods can be highly accurate but are time-consuming and thus are typically limited in scope to either mapping at fine scales or sampling at the landscape scale. Multispectral (Hyyppä et al, 1998) and hyperspectral remote sensing (Pu & Gong, 2004) have been used to map structural metrics at moderate resolution and broad scales. However, passive optical sensors have difficulty penetrating beyond upper canopy layers (Weishampel et al, 2000) and are better suited for mapping horizontal structure, e.g., land cover type.…”

Section: Introductionmentioning

confidence: 99%

Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy

Hyde

Dubayah

Walker

et al. 2006

Remote Sensing of Environment

350

210

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Verification of the potential of various remote sensing data sources for forest inventory

Cited by 3 publications

References 4 publications

An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon

An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon

Tropical-Forest-Parameter Estimation by Means of Pol-InSAR: The INDREX-II Campaign

Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy

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