Showing papers in "Canadian Journal of Remote Sensing in 2008"
TL;DR: The Earth Observation for Sustainable Developments of Forests (EOSD) project was initiated as a partnership between the Canadian Forest Service (CFS) and the Canadian Space Agency (CSA), with provincial and territorial participation and support as mentioned in this paper.
Abstract: Capture of land cover information is a key requirement for supporting forest monitoring and management. In Canada, provincial and territorial forest stewards use land cover information to aid in management and planning activities. At the federal level, land cover information is required to aid in meeting national and international reporting obligations. To support monitoring of Canada’s forests, the Earth Observation for Sustainable Developments of Forests (EOSD) project was initiated as a partnership between the Canadian Forest Service (CFS) and the Canadian Space Agency (CSA), with provincial and territorial participation and support. The EOSD project produced a 23 class land cover map of the forested area of Canada representing circa year 2000 conditions (EOSD LC 2000). Including image overlap outside of the forested area of Canada, over 480 Landsat-7 Enhanced Thematic Mapper Plus (ETM+) images were classified and more than 80% of Canada was mapped, culminating in the production of 630 1:250 000 map sh...
233 citations
TL;DR: A prototype upward-scanning, under-canopy, near-infrared light detection and ranging (lidar) system, the Echidna® validation instrument (EVI), built by CSIRO Australia, retrieves forest stand structural parameters, including mean diameter at breast height (DBH), stand height, distance to tree, stem count density (stems/area), leaf-area index (LAI), and stand foliage profile with height.
Abstract: A prototype upward-scanning, under-canopy, near-infrared light detection and ranging (lidar) system, the Echidna® validation instrument (EVI), built by CSIRO Australia, retrieves forest stand structural parameters, including mean diameter at breast height (DBH), stand height, distance to tree, stem count density (stems/area), leaf-area index (LAI), and stand foliage profile (LAI with height) with very good accuracy in early trials. We validated retrievals with ground-truth data collected from two sites near Tumbarumba, New South Wales, Australia. In a ponderosa pine plantation, LAI values of 1.84 and 2.18 retrieved by two different methods using a single EVI scan bracketed a value of 1.98 estimated by allometric equations. In a natural, but managed, Eucalypus stand, eight scans provided mean LAI values of 2.28–2.47, depending on the method, which compare favorably with a value of 2.4 from hemispherical photography. The retrieved foliage profile clearly showed two canopy layers. A “find-trunks” algorithm p...
198 citations
TL;DR: In this article, the authors evaluated the performance of two fundamentally different automated tree detection and measurement algorithms (spatial wavelet analysis (SWA) and variable window filters (VWF)) across a full range of canopy conditions in a mixed-species, structurally diverse conifer forest in northern Idaho, USA.
Abstract: Individual tree detection algorithms can provide accurate measurements of individual tree locations, crown diameters (from aerial photography and light detection and ranging (lidar) data), and tree heights (from lidar data). However, to be useful for forest management goals relating to timber harvest, carbon accounting, and ecological processes, there is a need to assess the performance of these image-based tree detection algorithms across a full range of canopy structure conditions. We evaluated the performance of two fundamentally different automated tree detection and measurement algorithms (spatial wavelet analysis (SWA) and variable window filters (VWF)) across a full range of canopy conditions in a mixed-species, structurally diverse conifer forest in northern Idaho, USA. Each algorithm performed well in low canopy cover conditions (<50% canopy cover), detecting over 80% of all trees with measurements, and producing tree height and crown diameter estimates that are well correlated with field measure...
137 citations
TL;DR: In this paper, a 400 ha area on Vancouver Island, British Columbia, Canada, was used to evaluate the relationship between small-footprint lidar-derived heights and stand height as derived from aerial photographic interpretation (API).
Abstract: Most countries with significant forest resources have designed and implemented monitoring systems to inventory, at regular intervals, a range of forest stand attributes such as species composition, age, volume, biomass, and disturbance. These inventory systems are typically based upon the interpretation of air photos supplemented by ground measurements, with digital remotely sensed data often used to capture changes within inventory cycles. Light detection and ranging (lidar) and high spatial resolution digital satellite imagery (e.g., QuickBird) offer additional capacity and complementary data sources for inventory assessment, as demonstrated by this study over a 400 ha area on Vancouver Island, British Columbia, Canada. A range of lidar survey parameters were applied to update an existing forest inventory. Results indicate a strong relationship between the small-footprint lidar-derived heights and stand height as derived from aerial photographic interpretation (API) (R = 0.79, p < 0.05). In addition, th...
78 citations
TL;DR: In this article, the authors present the pattern characteristics of Canada's forests as determined by the Earth Observation for Sustainable Development of Forests (EOSD) product, a new land cover classification of the forested area of Canada.
Abstract: Canada is one of the world’s largest nations, with a land area of nearly one billion hectares. This vast area is home to a number of unique ecosystems, comprised of different climate, land cover, topography, and disturbance characteristics. Depiction of forest composition, based on satellite-derived land cover, is a common means to characterize and identify trends in forest conditions and land use. Forest pattern analyses that consider the size, distribution, and connectivity of forest patches can provide insights to land use, habitat, and biodiversity. In this communication, we present the pattern characteristics of Canada’s forests as determined by the Earth Observation for Sustainable Development of Forests (EOSD) product, a new land cover classification of the forested area of Canada. The EOSD product (EOSD LC 2000) represents conditions circa the year 2000, mapping each 25 m × 25 m pixel into one of 23 categories. We used the EOSD data to assess forest patterns nationally at four spatial extents: lev...
72 citations
TL;DR: In this paper, the authors demonstrate that it is possible to use DRL to directly estimate the crown-area-weighted mean height (Hcw), which is conceptually and quantitatively similar to the Lorey's height, and can be used to calibrate GLAS without the use of field data.
Abstract: The Geoscience Laser Altimeter System (GLAS), a spaceborne light detection and ranging (lidar) sensor, has acquired over 250 million lidar observations over forests globally, an unprecedented dataset of vegetation height information. To be useful, GLAS must be calibrated to measurements of height used in forestry inventory and ecology. Airborne discrete return lidar (DRL) can characterize vegetation and terrain surfaces in detail, but its utility as calibration data for GLAS is limited by the lack of a direct relationship between the canopy height measurements collected by airborne and spaceborne lidar systems and coincident field data. We demonstrate that it is possible to use DRL to directly estimate the crown-area-weighted mean height (Hcw), which is conceptually and quantitatively similar to the Lorey’s height, which is calculated from forest inventory data, and can be used to calibrate GLAS without the use of field data. For a dataset from five sites in western North America, the two indices of heigh...
68 citations
TL;DR: In this article, the influence of the sampling design and laser beam density of ground-based light detection and ranging (lidar) measurements of forests on the quality of the collected laser datasets in terms of shadowing was studied.
Abstract: This research was undertaken to study the influence of the sampling design and laser beam density of ground-based light detection and ranging (lidar) measurements of forests on the quality of the collected laser datasets in terms of shadowing. Virtual forest stands generated by stochastic L-systems as tree descriptors are used as a basis depending on the study frame and requirements. The dynamic plant modeler and plant nursery natFX (Bionatics, CIRAD, Montpellier, France) was used to simulate deciduous forest stands of three tree species (Fagus sylvatica L., Platanus acerifolia (Ait.) Willd., and Populus nigra L.) with varying structural characteristics. Hemispherical laser measurements with different laser beam densities were simulated according to three different sampling patterns (single, diamond, corners) inside these virtual forest stands using ray-tracing technology. An adjusted sampling design has proven its effectiveness, since an average shadowing decrease of 29.10% was obtained in comparison wit...
62 citations
TL;DR: In this article, the authors proposed a spectral moment matching (SpcMM) method to remove the striping artefacts from Hyperion images, which uses spectral autocorrelation instead of spatial autocorerelation.
Abstract: As a pushbroom imaging spectrometer, the Hyperion sensor records each along-track image column with a single detector element in one of its area detector arrays. This image acquisition configuration is prone to exhibiting along-track striping artefacts, if the instrument has not undergone a recent, proper uniformity calibration. These defective columns must be corrected before performing further processing and quantitative analyses. Spatial moment matching (SpaMM) is currently the most commonly used method for destriping Hyperion imagery. However, since SpaMM was originally developed for dealing with remotely sensed multispectral data, the abundant spectral information embedded in hyperspectral data is not fully utilized in the method. This paper proposes a new methodology that can automatically and more accurately remove the striping artefacts from Hyperion imagery. The technique is called spectral moment matching (SpcMM) because it uses spectral autocorrelation instead of spatial autocorrelation to esti...
60 citations
TL;DR: In this article, partial least squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model for soil salinization.
Abstract: Soil salinization is one of the most common land degradation processes and is a severe environmental hazard. The primary objective of this study is to investigate the potential of predicting salt content in soils with hyperspectral data acquired with EO-1 Hyperion. Both partial least-squares regression (PLSR) and conventional multiple linear regression (MLR), such as stepwise regression (SWR), were tested as the prediction model. PLSR is commonly used to overcome the problem caused by high-dimensional and correlated predictors. Chemical analysis of 95 samples collected from the top layer of soils in the Yellow River delta area shows that salt content was high on average, and the dominant chemicals in the saline soil were NaCl and MgCl2. Multivariate models were established between soil contents and hyperspectral data. Our results indicate that the PLSR technique with laboratory spectral data has a strong prediction capacity. Spectral bands at 1487–1527, 1971–1991, 2032–2092, and 2163–2355 nm possessed lar...
58 citations
TL;DR: In this paper, the authors evaluate the requirements for a recently introduced alpha shape metrics approach in which computational volume and complexity metrics derived from ALS point clouds are utilized to produce actual tree-level characteristics.
Abstract: Operational pulse density affects the measurements based on airborne laser scanning (ALS) data, especially at the individual tree level. The minimum density required depends on the interpretation methodology used, i.e., knowing the requirements is a prerequisite for a successful ALS data acquisition. We evaluate these requirements for a recently introduced alpha shape metrics approach in which computational volume and complexity metrics derived from ALS point clouds are utilized to produce actual tree-level characteristics. We simulated thinnings to the ALS return data using a test dataset of a total of 92 dominant or codominant trees detected and delineated manually from very high density (approximately 40 returns/m2) initial ALS data and produced species and diameter at breast height estimates with the thinned datasets. We compared the alpha shape metrics approach with alternative methods, making additional use of tree-level ALS data, and examined the sensitivity of the different methods to pulse densit...
58 citations
TL;DR: Experiments reported in this paper confirm that the proposed method outperforms PCA for endmember extraction and mineral detection using the Cuprite data cube.
Abstract: In this paper, we propose a method that not only reduces the dimensionality of a hyperspectral data cube but also removes noise in the data cube by combining the bivariate wavelet thresholding with principal component analysis (PCA). The data cube thus obtained is applied to mineral endmember extraction and mineral detection. The reason why we incorporate bivariate wavelet denoising into PCA dimensionality reduction is because the dimensionality-reduced channels using PCA often contain significant amounts of noise. By reducing noise in the data cube, we can get better dimensionality-reduced output channels for hyperspectral data analysis and processing. Experiments reported in this paper confirm that the proposed method outperforms PCA for endmember extraction and mineral detection using the Cuprite data cube.
TL;DR: In this article, a combination of light detection and ranging (lidar) remote sensing (LVIS), an individual-based height-structured ecosystem model (ED), and detailed topographic and climate data were used to predict carbon dynamics at the Hubbard Brook Experimental Forest (HBEF) in the White Mountains of New Hampshire.
Abstract: Accurately predicting forest dynamics and associated carbon fluxes requires both knowledge of the current state of the ecosystem and an understanding of the underlying processes and environmental conditions that influence the ecosystem processes. Here, we apply a combination of light detection and ranging (lidar) remote sensing (LVIS), an individual-based height-structured ecosystem model (ED), and detailed topographic and climate data to address these requirements to predict carbon dynamics at the Hubbard Brook Experimental Forest (HBEF) in the White Mountains of New Hampshire. Lidar data provided substantial constraints on model estimates of carbon stocks and annual net ecosystem production (ANEP). Lidar-initialized model estimates of carbon stocks (10.77 kg C m−2) were within 5% of the field estimates over the domain and accounted for the 44% decrease in carbon stocks observed between minimum and maximum elevation at HBEF. Lidar-initialized model estimates of ANEP (0.023 kg C m−2 year−1) also compared ...
TL;DR: In this paper, the authors compare the change detection results generated from a reference image pair (a 1999 Landsat-7 ETM+ image and a 2003 Landsat 5 TM image) coupled with three different 2003 SLC-off products: unremediated SLC off (i.e., with gaps), histogram-based gap-filled, segment-based, and histogrambased gapfilled), on both a pixel and polygon basis.
Abstract: Since July 2003, Landsat-7 ETM+ has been operating without the scan line corrector (SLC), which compensates for the forward motion of the satellite in the imagery acquired. Data collected in SLC-off mode have gaps in a systematic wedge-shaped pattern outside of the central 22 km swath of the imagery; however, the spatial and spectral quality of the remaining portions of the imagery are not diminished. To explore the continued use of Landsat-7 ETM+ SLC-off imagery to characterize change in forested environments, we compare the change detection results generated from a reference image pair (a 1999 Landsat-7 ETM+ image and a 2003 Landsat-5 TM image) with change detection results generated from the same 1999 Landsat-7 ETM+ image coupled with three different 2003 Landsat-7 SLC-off products: unremediated SLC-off (i.e., with gaps); histogram-based gap-filled; and segment-based gap-filled. The results are compared on both a pixel and polygon basis; on a pixel basis, the unremediated SLC-off product missed 35% of ...
TL;DR: In this article, a critical analysis of annual land cover maps generated by updating and backdating object-based reference maps in a western Alberta study area revealed that sliver objects made up between 3% and 12% of the total area of change, and between 63% and 72% of change objects, despite high thematic accuracies.
Abstract: small, spurious polygons created by the inconsistent delineation of persistent change features appearing in consecutive coregistered images. The issue represents a serious methodological challenge that can limit the visual and structural quality of the finished map product if not adequately addressed. A critical analysis of annual land cover maps generated by updating and backdating object-based reference maps in a western Alberta study area revealed that sliver objects made up between 3% and 12% of the total area of change, and between 63% and 72% of the total number of change objects, despite high thematic accuracies. The results highlight the emerging need for a methodological framework designed to handle the spatial challenges posed by change analysis in an object-based environment.
TL;DR: In this article, an object-oriented land-use classification using land-cover information derived from an IKONOS image was proposed to automatically delineate and classify the land use polygons.
Abstract: The greater availability of remotely sensed high-resolution imagery and advances in object-oriented analysis have created more opportunities for automated urban land-use classifications. To date, few studies have attempted to classify land use from satellite imagery using object-oriented approaches, and those that have tend to rely on manual digitizing or ancillary data to delineate land-use polygon boundaries. This paper explores an object-oriented land-use classification using land-cover information derived from an IKONOS image to automatically delineate and classify the land-use polygons. The study area is in Mississauga (Ontario, Canada), a diverse urban setting. The first step was to classify land cover from the IKONOS image. This then served as the basis for creating a six-class and more detailed ten-class land-use map. The overall accuracies of the six- and ten-class maps were 90% and 86%, respectively. The high accuracies of individual classes suggest that the object-oriented methodology has great...
TL;DR: In this paper, a new linear regression method for tree canopy height estimation using airborne light detection and ranging (lidar) data was proposed, and theoretical analysis was performed on simulated datasets with varying crown shape, unit penetrability, and laser-missed canopy layer.
Abstract: Tree canopy height is one of the most fundamental measurements in forest inventory and is a critical variable in the quantitative assessment of tree (or stand) volume, forest biomass, carbon stocks, growth, and site productivity. In this study, we analyzed two traditional methods for tree canopy height estimation and designed a new linear regression method for improved tree canopy height estimation using airborne light detection and ranging (lidar) data. Examples of two typical crown shapes were used, and theoretical analysis was performed on simulated datasets with varying crown shape, unit penetrability, and laser-missed canopy layer(s). The final result derived from the simulated lidar data illustrates that the linear regression method can improve canopy height estimation. This method was also applied to lidar data covering a tall pine forest in Idaho, USA. An average error of 0.51 m was obtained from a comparison of the lidar-derived tree canopy heights and 79 field measurements. This error was also c...
TL;DR: In this article, the authors examined deconvolution and Gaussian decomposition as a methodology for locating ranging locations on the return pulse of ICESat/GLAS using a small-footprint full-waveform dataset.
Abstract: The Ice, Cloud and Land Elevation Satellite (ICESat) with the Geoscience Laser Altimeter System (GLAS) data provides a new potential for extracting information on the vertical structure of vegetation worldwide. Analysis of the returned full-waveform energy over areas of low topography allows for the direct retrieval of canopy heights. The returned laser energy is a complex interaction between surface scattering and the outgoing laser properties. In addition, the return pulse is convolved with the outgoing system response. Laser ranges are determined by computing the laser travel time between the outgoing pulse and the return pulse. This research examines deconvolution and Gaussian decomposition as a methodology for locating ranging locations on the return pulse. Heights retrieved from ICESat/GLAS using both deconvolution and Gaussian decomposition were evaluated against heights derived from a small-footprint, full-waveform dataset. Early results indicate that ranges derived from deconvolution revealed ver...
TL;DR: In this article, the authors developed an approach for mapping wetlands using Landsat/RADARSAT-1 satellite images based on image segmentation using the Definiens Professional software.
Abstract: (Studies on greenhouse gases (GHG) emitted by hydroelectric reservoirs have shown until now that the fate of carbon, following impoundment, seems to reach the fate of carbon in natural aquatic ecosystems after a decade or so. To adequately assess this assumption and then obtain the net GHG emissions from the Eastmain-1 hydroelectric reservoir, the carbon stock and GHG emissions from peatlands and different succession stages of forested areas need to be characterized prior to the reservoir impoundment. It is therefore important to characterize the carbon flow process (surface fluxes and sequestrated carbon) from these terrestrial systems prior to impoundment. The Canadian Wildlife Service of Environment Canada, Quebec region, has developed an approach for mapping wetlands using Landsat/RADARSAT-1 satellite images. The method is based on image segmentation using the Definiens Professional software. The top-down object-based classification is based on the Canadian Wetland Classification System and quickly an...
TL;DR: In this article, the authors compared the global coverage of bidirectional reflectance distribution function (BRDF) products from the Multi-angle Imaging Spectroradiometer (MISR) and the MODIS (Modis) with geographical information system (GIS) data coverage combining floristic polygons with Landsat thematic mapper (TM).
Abstract: The global coverage of bidirectional reflectance distribution function (BRDF) products from the Multi-angle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) has the potential to provide quantitative information on surface vegetation structure for input to process modelling and model–data assimilation schemes for regional and biome-scale assessment of carbon dynamics. The relationship of MISR Rahman–Pinty–Verstraete (RPV) model parameters, derived from inversion of MISR 275 m fine mode data, and BRDF shape indicators calculated from the latest MODIS 500 m MCD43 BRDF product to vegetation patterns in an Australian tropical savanna was examined for a time series covering the dry season period from April to October 2005. The bidirectional reflectance products were compared with geographical information system (GIS) data coverage combining floristic polygons with Landsat thematic mapper (TM) based estimates of canopy cover and height classes. The analysis showed th...
TL;DR: In this paper, the authors reviewed the literature on the remote sensing of boreal forests in order to determine if common frameworks for monitoring and assessing change in forest biophysical and inventory parameters could be developed.
Abstract: The growing need to manage vegetation resources at regional and global spatial scales has led to the increased use of remote sensing technologies among forestry scientists and managers for use in their investigation and supervision of forested landscapes. With an array of extant and developing airborne and satellite sensors, as well as multiple analysis techniques, there is a need to discern the most acceptable methods with which to examine remotely sensed imagery for forest ecosystem parameters. We have reviewed the literature on the remote sensing of boreal forests in order to determine if common frameworks for monitoring and assessing change in forest biophysical and inventory parameters could be developed. Other important remote sensing techniques such as those for change detection and land cover identification were also examined. Our review examined studies documented within the scientific literature that involved the extraction of information on forest parameters with remote sensing instruments. Pri...
TL;DR: In this paper, a brief overview of the unique assets that hyperspectral imagery can provide to Earth observation (EO) is presented, followed by a review of civilian and dual-use satellite HSI sensors that have been flown.
Abstract: A brief overview is presented of the unique assets that hyperspectral imagery can provide to Earth observation (EO), followed by a review of civilian and dual-use satellite hyperspectral sensors that have been flown, including the European Space Agency (ESA) compact high-resolution imaging spectrometer (CHRIS) on Project for On-Board Autonomy (PROBA), the National Aeronautics and Space Administration (NASA) Hyperion on EO-1, and sensors currently being developed or under consideration, including Germany’s environmental mapping and analysis program (EnMAP) plus some that were planned or built but for various reasons never got to space, e.g., the Naval EarthMap Observer (NEMO) and Warfighter. Additionally, there is information on selected airborne hyperspectral sensors, including the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The paper suggests civilian hyperspectral sensors that will possibly be operating in space in the 2008–2015 time period, based on current information regarding their...
TL;DR: Satellite interferometric synthetic aperture radar (InSAR) has proven to be an effective tool for monitoring surface deformation from volcanoes, earthquakes, landslides, and groundwater withdrawal.
Abstract: Satellite interferometric synthetic aperture radar (InSAR) has proven to be an effective tool for monitoring surface deformation from volcanoes, earthquakes, landslides, and groundwater withdrawal This paper seeks to expand the list of applications of InSAR data to include monitoring subsidence possibly associated with thaw settlement over the Alaskan Arctic Coastal Plain To test our hypothesis that InSAR data are sufficiently sensitive to detect subsidence associated with thaw settlement, we acquired all Japanese Earth Resources Satellite-1 (JERS-1) L-band data available for the summers of 1996, 1997, and 1998 over two sites on the Alaska North Slope The least amount of subsidence for both study sites was detected in the interferograms covering the summer of 1996 (2–3 cm), interferograms from 1997 and 1998 revealed that about 3 cm of subsidence occurred at the northern Cache One Lake site, and about 5 cm of subsidence was detected at the southern Kaparuk River site These preliminary results illustrat
TL;DR: In this paper, the ability to obtain reliable LAI estimates from CHRIS data was investigated using a preexisting relationship between LAI and the modified transformed vegetation index (MTVI2).
Abstract: Leaf area index (LAI) is a key variable in crop growth models. The derivation of reliable LAI maps from satellite imagery would provide a means of spatially extrapolating these models. During the 2004 and 2005 growing seasons, hyperspectral data from the Compact High Resolution Imaging Spectrometer (CHRIS) sensor were acquired over a wheat crop in southern Alberta. The ability to obtain reliable LAI estimates from CHRIS data was investigated using a preexisting relationship between LAI and the modified transformed vegetation index (MTVI2). The performance of the recently develop MTVI2 in estimating LAI was compared to that of the more commonly used normalized difference vegetation index (NDVI). Both narrow and broad bands simulated from the CHRIS data were used in calculating the NDVI. All vegetation indices provided good relationships with LAI (R2 = 0.70–0.90). The errors in the estimated LAI values varied with vegetation index and were dependent on growth stage. The MTVI2 performed better than the NDVI ...
TL;DR: In this paper, marked point patterns of vegetation structure are produced from two per-object methods, and new spatial-structural data layers are developed via moving-window statistics applied to the point patterns, the layers are differenced to highligh.
Abstract: The remote sensing of vegetation, which has predominantly applied methods that analyze each image pixel as independent observations, has recently seen the development of several methods that identify groups of pixels that share similar spectral or structural properties as objects. The outputs of “per-object” rather than “per-pixel” methods represent characteristics of vegetation objects, such as location, size, and volume, in a spatially explicit manner. Before decisions can be influenced by data products derived from per-object remote sensing methods, it is first necessary to adopt methodologies that can quantify the spatial and temporal trends in vegetation structure in a quantitative manner. In this study, we present one such methodological framework where (i) marked point patterns of vegetation structure are produced from two per-object methods, (ii) new spatial-structural data layers are developed via moving-window statistics applied to the point patterns, (iii) the layers are differenced to highligh...
TL;DR: In this paper, a procedure was developed to measure the band centers and bandwidths for imaging spectrometers using data acquired by the sensor in flight, thus allowing the measurement of the instrument's slit curvature or spectral smile.
Abstract: A procedure has been developed to measure the band centres and bandwidths for imaging spectrometers using data acquired by the sensor in flight. This is done for each across-track pixel, thus allowing the measurement of the instrument's slit curvature or spectral "smile." The procedure uses spectral features present in the at-sensor radiance that are common to all pixels in the scene. These are principally atmospheric absorption lines. The band-centre and bandwidth determinations are made by correlating the sensor-measured radiance with a modelled radiance, the latter calculated using MODTRAN 4.2. Measurements have been made for a number of instruments, including the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS), shortwave infrared full spectrum imager (SFSI), Compact Airborne Spectrographic Imager (casi), compact high-resolution imaging spectrometer (CHRIS), and Hyperion. The measurements on AVIRIS data were performed as a test of the procedure; since AVIRIS is a whisk-broom scanner, it is ex...
TL;DR: In this paper, the authors used satellite remote sensing imagery to map vegetation composition and biomass in three riparian marshes near Ottawa, Ontario, using a stepwise forward multiple regression model.
Abstract: The Ontario Wetland Evaluation System (OWES) employs mostly field-based visual assessments of wetland extent, composition, and productivity as primary indicators in determining which wetlands should be considered provincially significant and protected. A given wetland is generally assigned single attributes that are assumed to represent the whole wetland extent. High spatial resolution satellite remote sensing offers potential to map and monitor spatial variability of given attributes within a wetland. In this study, Ikonos imagery was used to map vegetation composition and biomass in three riparian marshes near Ottawa, Ontario. For vegetation composition mapping, separability and correlation analyses aided the selection of an optimum set of spectral and texture input variables. Several maximum likelihood classification tests for sets of terrestrial and aquatic vegetation classes gave best accuracies from 61% for seven classes to 88% for five classes. As an alternative, neural network classification was tested using various configurations of input variables and data. However, the best results did not match those from the maximum likelihood classification. For biomass mapping, dried green and senescent biomass collected at 75 locations were modelled using stepwise forward multiple regression. The best model produced was the logarithm of green biomass against a combination of texture and spectral variables (R 2 = 0.61). It was applied to the image data to map green biomass with an absolute error of 213 g/m 2 ,o r approximately 40% of the mean field-measured biomass. Based on this error magnitude, the output map was aggregated into three classes of biomass (high, medium, and low) that showed a strong visual correspondence with the spatial distributions observed in the field. These results indicate strong potential for monitoring of vegetation composition and biomass changes within wetlands which may contribute to improvement of wetlands evaluation and monitoring in Ontario.
TL;DR: In this paper, two approaches, linear spectral unmixing and vegetation indices, were reviewed and evaluated for the estimation of crop fraction from hyperspectral data, which were acquired three times in the 2001 growing season over four agricultural fields to monitor crop growth conditions.
Abstract: It is important to estimate vegetation fraction for forecasting regional weather and in precision agriculture for assessing crop performance during emergence and early growth phases. In this study, two approaches, linear spectral unmixing and vegetation indices, were reviewed and evaluated for the estimation of crop fraction from hyperspectral data. Compact Airborne Spectrographic Imager (casi) hyperspectral data were acquired three times in the 2001 growing season over four agricultural fields to monitor crop growth conditions and develop procedures for delineating major subunits for crop management. Crops planted in these fields included corn, soybean, and wheat. End-member spectra were extracted from casi data and used for linear spectral unmixing. Various vegetation indices, including the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), modified soil-adjusted vegetation index (MSAVI), transformed soil-adjusted veget...
TL;DR: In this article, the authors investigated the relationship between a wide range of spectral chlorophyll indices and the content of wheat crop using hyperspectral data acquired with the Hyperion Earth Observing 1 (EO-1) sensor and ground and laboratory measurements for validation purposes.
Abstract: Chlorophyll content is an essential biochemical parameter to track the main developmental stages and yield of cereals relevant for agriculture. In this perspective, several spectral chlorophyll indices have been developed to estimate chlorophyll content at both the leaf and canopy levels using remote sensing data and considering different crop types. The application of these chlorophyll indices under agricultural field conditions has not been rigorously tested and validated for wheat crops. The objective of this study is to investigate the relationship between a wide range of spectral chlorophyll indices and chlorophyll content of wheat crop using hyperspectral data acquired with the Hyperion Earth Observing 1 (EO-1) sensor and ground and laboratory measurements for validation purposes. The Hyperion data and ground measurements were acquired on 30 June 2002 over two agricultural fields near Indian Head, Saskatchewan, Canada. The image data were corrected for a spatial shift between the visible near-infrar...
TL;DR: In this paper, a speckle noise model is proposed that provides a new framework for modelling and reducing the speckble noise, which can be used in different applications, including mapping and forestry biomass estimation.
Abstract: Speckle noise is primarily due to the phase fluctuations of the electromagnetic return signals. Since inherent spatial-correlation characteristics of speckle in synthetic aperture radar (SAR) images are not exploited in existing multiplicative models for speckle noise, a speckle noise model is proposed here that provides a new framework for modelling and reducing the speckle noise. Both quantitative and qualitative criteria, including speckle reduction and texture preservation, are used to evaluate the performance of the proposed filter; one PALSAR (new Japanese sensor) image and a JERS-1 image are employed in the evaluation. The results showed that the proposed filter is slightly better than commonly used filters such as the Kuan, gamma, enhanced Lee, and enhanced Frost filters. The proposed filter can be used in different applications, including mapping and forestry biomass estimation. Furthermore, one of the benefits of the proposed filter is that it is independent of the threshold, which is required i...
TL;DR: In this article, a hybrid canopy geometric optical and radiative transfer (GORT) model was applied to study the vegetation structure characteristics and lidar signals from a terrestrial below-canopy lidar instrument, Echidna Validation Instrument (EVI), developed by CSIRO Australia.
Abstract: This study applied a hybrid canopy geometric optical and radiative transfer (GORT) model to study the vegetation structure characteristics and lidar signals from a terrestrial below-canopy lidar instrument, Echidna Validation Instrument (EVI), developed by CSIRO Australia. Off-nadir scans from the below-canopy lidar show strong laser energy returns from both leaves and tree trunks. The GORT model was modified to include the effect of both leaves and trunks on below-canopy lidar energy returns by treating the trunks as simple uniform cylinders extending to the middle of each tree crown. GORT was also extended to allow multiple canopy layers by convolution of the canopy gap probability profiles for individual canopy layers. The extended leaf-and-trunk GORT model was evaluated by comparing the modeled and EVI-derived gap probability profiles in a single-layer pine plantation and a two-layer eucalypt forest at the Tumbarumba flux tower site in southeastern New South Wales, Australia. Results show that the new...