Authors:Oumer S. Ahmed; Steven E. Franklin, Michael A. Wulder Abstract: Canadian Journal of Remote Sensing, Volume 39, Issue 06, Page 521-542, December 2013.
In this study we examined forest disturbance, largely via forest harvest, over three decades in a coastal temperate forest on Vancouver Island, British Columbia, Canada. We analysed how disturbance history relates to current canopy structural conditions by interpreting the relationship between light detection and ranging (lidar) derived canopy structure and forest disturbance trajectories derived from Landsat images to assess if a particular stand structural condition is to result based on disturbance histories. The lidar data were obtained in 2004, and are used to relate forest structural conditions at the end of the Landsat time series (1972–2004), essentially providing for a measure of resultant structure emerging from the spectral trends captured. Correlation analysis was applied between lidar-derived canopy structure (canopy cover and height) and Landsat spectral indices, such as the Tasseled Cap Angle (TCA), which showed a strong correlation coefficient (r = 0.86) with canopy cover. TCA was then used to characterize change in forest disturbance through the full temporal depth of the available Landsat image time series using a trajectory-based characterization method. Approximately 71.5% of the study area was found to correspond to “stable and undisturbed forest”. Four disturbance classes (areas characterized by disturbance, disturbance followed by revegetation, ongoing revegetation, and revegetation to stable state) accounted for approximately 10.2%, 5.3%, 2.2%, and 10.5% of the study area, respectively. We evaluated the forest structural and spectral separability between the disturbance classes. In terms of structural variability the mean airborne lidar-derived canopy cover showed clear differentiation between disturbance classes. Spectral mixture analysis (SMA) was used to extract the spectral characteristics for each disturbance class. The SMA-derived fractions were then used to analyse the class separability between the Landsat trajectory derived disturbance classes. The fraction images provided clear distinction between disturbance classes in abundances between sunlit canopy, non-photosynthetic vegetation, shade, and exposed soil. The extracted spectral indices and SMA fractions within the Landsat trajectory derived disturbance classes were used to assess if terminal forest structural conditions can be related to a complex suite of stand development trajectories and processes. The Landsat spectral indices and SMA fractions were separately modeled to estimate lidar-derived mean canopy cover and height data within each disturbance class using multiple regression. The results indicate canopy cover and height regression models developed using spectral indices provided a relatively better estimation than those using SMA endmember fractions. Compared with the relatively regular structure of fully grown undisturbed (stable) forests, the forest disturbance classes typically exhibited complex irregular structure, making it more difficult to accurately estimate their canopy cover and height. As a result, all models developed for the stable forest class performed better than those developed for other forest disturbance classes. Modeling canopy cover and height from Landsat temporal spectral indices resulted in modeled agreement to lidar measures of R2 0.82 (RMSE 0.09) and R2 0.67 (RMSE 3.21), respectively. Our results also indicate moderately accurate predictions of lidar-derived canopy height can be obtained using the Landsat-level disturbance class endmember fractions with R2 0.60 and RMSE 4.19. This study demonstrates the potential of using the over four decade record of Landsat observations (since 1972) to estimate forest canopy cover and height using prestratification of the data based on disturbance trajectories. PubDate: Wed, 12 Mar 2014 07:00:00 GMT
Authors:Reshu Agarwal; Pritam Ranjan, Hugh Chipman Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page 1-14, e-First articles.
Classification of satellite images is a key component of many remote sensing applications. One of the most important products of a raw satellite image is the classification that labels image pixels into meaningful classes. Though several parametric and nonparametric classifiers have been developed thus far, accurate classification still remains a challenge. In this paper, we propose a new reliable multiclass classifier for identifying class labels of a satellite image in remote sensing applications. The proposed multiclass classifier is a generalization of a binary classifier based on the flexible ensemble of regression trees model called Bayesian Additive Regression Trees. We used three small areas from the LANDSAT 5 TM image, acquired on 15 August 2009 (path–row: 08–29, L1T product, UTM map projection) over Kings County, Nova Scotia, Canada, to classify the land cover. Several prediction accuracy and uncertainty measures have been used to compare the reliability of the proposed classifier with the state-of-the-art classifiers in remote sensing. PubDate: Wed, 05 Mar 2014 14:22:25 GMT
Authors:Gabriel Gosselin; Ridha Touzi, François Cavayas Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page 1-16, e-First articles.
Wetlands play a key role in regional and global environments and are linked to climate change, water quality, and hydrological and carbon cycles. They also contribute to wildlife habitat and biodiversity and can act as indicators of overall environmental health. Unfortunately, wetlands continue to be under threat. There is an immediate need for improved mapping and monitoring of wetlands to better manage and protect these sensitive areas. Recently, the Touzi decomposition was introduced and proved very promising for wetland characterization using polarimetric airborne (Convair-580) SAR data. The purpose of this study is to assess the Touzi incoherent target-scattering decomposition (ICTD) for wetland classification using polarimetric Radarsat-2 (RS2) data collected over the RAMSAR wetland site in Lac Saint-Pierre, Canada. In particular, the sensitivity of the ICTD parameters to seasonal evolution of marsh and swamp scattering is discussed and demonstrated. The intent is to show that the dominant scattering type magnitude (αs1) and phase (Φs1), and the dominant (η1) and lowest scattering eigenvalues (η3), lead to an effective characterization of the various backscattering mechanisms of the wetland plant species. The ICTD parameters form the basis of a new hierarchical classification that is efficient for wetland classification. The use of multitemporal information obtained from multidate RS2 acquisitions between April and September allows an accurate wetland classification. The RS2 polarimetric classification is then compared with a supervised maximum-likelihood classification using a pair of Landsat-5 images. PubDate: Wed, 26 Feb 2014 13:36:25 GMT
Authors:P.R. Eddy; A.M. Smith, B.D. Hill, D.R. Peddle, C.A. Coburn, R.E. Blackshaw Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page 1-10, e-First articles.
Accurate and efficient weed detection in crop fields is a key requirement for directed herbicide application in real-time Site-Specific Weed Management (SSWM). Using very high spatial resolution (1.25 mm) hyperspectral (HS) image data (61 bands, 400–1000 nm at 10 nm spectral resolution), this study determined that reduced HS bandsets are feasible for discriminating weeds (wild oats, redroot pigweed) from crops (field pea, spring wheat, canola) using Artificial Neural Network (ANN) classification. A 7-band set identified through principal component analysis and stepwise discriminant analysis yielded ANN classification accuracies (88% to 94%) that were nearly equivalent to the full 61-band HS results (89% to 95%) at replicate field plots in southern Alberta, Canada. Therefore, low dimensional narrowband sensors or similar bandsets derived from HS data warrant consideration for SSWM. The computational savings possible from this substantial level of data reduction are potentially critical for enabling optimal use of HS data in real-time ground-based SSWM systems. Recommendations made based on these results have potentially broader implications to SSWM with respect to on-board processing efficiency, weed–crop discrimination method, and sensor and algorithm design. PubDate: Tue, 18 Feb 2014 14:16:56 GMT
Authors:Xian-zhong Shi; Mehrooz Aspandiar, Ian C Lau, David Oldmeadow Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page 1-13, e-First articles.
Acid sulphate soils (ASS) are widely spread around the world and are potentially harmful to the environment due to their strong acidity producing ability and their capability to release trace metals. Secondary iron-bearing minerals produced by ASS, have diagnostic spectral features in the visible-near infrared to short-wave infrared spectral range and can be good indicators to the severity of the effects of ASS. Therefore, it is possible to detect ASS using hyperspectral sensing by mapping these indicative iron-bearing minerals. Iron oxides, hydroxides, hydroxysulphates, as well as noniron-bearing minerals, were mapped using airborne Hyperspectral Mapper data. Subsequently, a soil pH map of the surface was deduced according to the relationship between the indicative mineral species and measured pH values. Furthermore, this study investigated the presence of ASS in the subsurface by the proximal hyperspectral sensing HyLogger system, together with soil coring and soil property measurements. This allowed the acquisition of mineralogy, pH, and other soil properties at different subsurface depths. Thus, comprehensive understanding and estimation of ASS, both on the surface and in the subsurface, were attained. PubDate: Thu, 13 Feb 2014 12:35:11 GMT
Authors:Joanne C. White; Michael A. Wulder Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page 1-13, e-First articles.
The Landsat data archive represents more than 40 years of Earth observation, providing a valuable information source for monitoring ecosystem dynamics. In excess of 605000 images of Canada have been acquired by the Landsat program since 1972. Herein we report several spatial and temporal characteristics of the Landsat observation record for Canada (1972–2012), including image availability by year, growing season, sensor, ecozone, and provincial or territorial jurisdiction. In contrast to the global Landsat archive, which is dominated by Enhanced Thematic Mapper Plus (ETM+) data, the majority of archived Landsat images of Canada were acquired by the Thematic Mapper (TM) sensor (57%). Approximately 55% of archived Landsat images were acquired within ± 30 days of 1 August, and 74% of Worldwide Reference System–2 path–row locations in Canada have more than 200 images acquired between 1 June and 30 September. Issues such as cloud cover and the availability of imagery to support pixel-based image compositing and time series analyses are explored and documented. For a pixel-based image compositing scenario whereby images (TM and ETM+) acquired after 1981 with less than 70% cloud cover and a target date of 1 August ± 30 days are considered, 60% of the path–row locations have five or fewer years of missing data in the archive. For time series analyses (i.e., ecosystem monitoring scenario) with the same temporal constraint but with less than 10% cloud cover, only 2% of path–row locations are missing five or fewer years of data, with a mean and median of 17 years of missing data. However, if a broader temporal window (1 June to 30 September) is considered for this scenario, 18% of path–row locations have five or fewer years of missing data. Free and open-access to the Landsat data archive, combined with the continuity of new data collections provided by the recently launched Landsat 8 satellite, offer many opportunities for scientific inquiry concerning the status and trends of Canada's terrestrial ecosystems. PubDate: Thu, 13 Feb 2014 12:34:52 GMT
Authors:firstname.lastname@example.org (Alessandro Montaghi) Abstract: Canadian Journal of Remote Sensing, Volume 39, Issue S1, Page S152-S173, December 2013.
The influence of scanning angle on vegetation metrics derived from a large area Airborne Laser Scanning (ALS) data acquisition was evaluated in this study. The ALS data were derived from the ongoing acquisition for the new Swedish Nationwide Elevation Model. To make a comparison of scanning angles, a random selection of 2310 sample plots (0.01 ha in size) was taken from two large forested areas in the north and south of Sweden. Only plots that had ALS data from two different acquisitions on the same day were used: the first scanned at nadir (0° scanning angle) and the second with an absolute scanning angle ranging from 0° to a nominal 20°. For each plot, 32 plot-level vegetation metrics were calculated from the ALS data for each pair of scanning angles. The ALS metrics for each pair were then compared using a nonparametric Wilcoxon signed-rank test. The results indicated that most metrics commonly used in area-based prediction of forest variables were relatively unaffected by high scanning angles, up to 20°. However, the vegetation ratio and the understory ratio from scanning angles greater than 10° were significantly different from those derived from a 0° scanning angle. PubDate: Thu, 19 Dec 2013 08:00:00 GMT
Authors:email@example.com (M.A. Wulder et al); N.C. Coops, A.T. Hudak, F. Morsdorf, R. Nelson, G. Newnham, M. Vastaranta Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page S1-S5, e-First articles.
The science associated with the use of airborne and satellite Light Detection and Ranging (LiDAR) to remotely sense forest structure has rapidly progressed over the past decade. LiDAR has evolved from being a poorly understood, potentially useful tool to an operational technology in a little over a decade, and these instruments have become a major success story in terms of their application to the measurement, mapping, or monitoring of forests worldwide. Invented in 1960, the laser and, a short time later, LiDAR, were found in research and military laboratories. Since the early 2000s, commercial technological developments coupled with an improved understanding of how to manipulate and analyze large amounts of collected data enabled notable scientific and application developments. A diversity of rapidly developing fields especially benefit from communications offered through conferences such as SilviLaser, and LiDAR has been no different. In 2002 the SilviLaser conference series was initiated to bring together those interested in the development and application of LiDAR for forested environments. Now, a little over a decade later, commercial use of LiDAR is common. In this paper – using the deliberations of SilviLaser 2012 as a source of information – we aim to capture aspects of importance to LiDAR users in the forest ecosystems community and to also point to key emerging issues as well as some remaining challenges. PubDate: Mon, 16 Dec 2013 15:09:13 GMT
Authors:firstname.lastname@example.org (Douglas K. Bolton et al); Nicholas C. Coops, Michael A. Wulder Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page S1-S13, e-First articles.
Carbon storage in forest aboveground biomass is a critical, yet difficult, component of the global carbon cycle to estimate. Canopy height, a key indicator of carbon storage, can be estimated from Light Detection and Ranging (Lidar) waveforms collected by the Geoscience Laser Altimeter System (GLAS) aboard the Ice, Cloud, and land Elevation Satellite (ICESat). Although globally distributed, GLAS does not provide spatially exhaustive coverage. Therefore, accurate methods of extrapolation are necessary to produce wall-to-wall global canopy height maps from these data. In this analysis, we compare two of these global GLAS-derived height products to canopy height estimates derived from 25000 km of discrete return airborne Lidar data over Canada's boreal forests. We selected the 95th percentile of first return height from airborne Lidar as a measure of canopy height to relate against estimates from the global GLAS-derived products. The agreement between the global GLAS-derived products and airborne Lidar-derived height estimates varied between the two products (average ecozone RMSE = 3.9 and 7.4 m), demonstrating that differences in data selection, processing, and extrapolation can influence height estimates derived from GLAS data. Where large differences existed between the global GLAS-derived products and the airborne Lidar-derived height estimates, the GLAS-derived products tended to predict taller canopies. Removing GLAS waveforms on steep terrain appeared to be a superior approach to reducing errors in height estimates, as the global GLAS-derived product that filtered these waveforms was in closer agreement with airborne Lidar-derived height estimates in regions of rough terrain (RMSE = 3.2–8.5 m compared with 8.1–13.8 m). Differences in the spatial resolution of canopy height estimates, coupled with varying definitions of canopy height within each product, should be considered when interpreting the results of this analysis. Investigating the relationship between small-footprint Lidar data and published canopy height products can identify the approaches that lead to the most accurate estimates of aboveground biomass and can help determine why discrepancies in height estimates exist between various model approaches, data and underlying environmental conditions. PubDate: Wed, 20 Nov 2013 17:38:57 GMT
Authors:email@example.com (Huaguo Huang et al); Randolph H. Wynne Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page S1-S13, e-First articles.
Our objective was to assess the effect of multiple scattering on lidar radiative transfer. We have developed a time-dependent radiosity-based model (RBL) to simulate the propagation of lidar pulses through forest canopies. This 3-D model enables simulation of lidar waveforms with varied topography and clumping vegetation. The incidence angle can also be specified. This new model has the potential to provide better approximations of return waveforms. The prototype is being tested using data from the Scanning Lidar Imager of Canopies by Echo Recovery (SLICER). Waveforms simulated by RBL resemble SLICER waveforms (R2 > 0.90) over a jack pine canopy and a black spruce canopy. There is also good agreement (R2 > 0.95) when the model results are compared with a time-dependent radiative transfer model. Results to date indicate that multiply scattered photons do increase the intensity of the reflected signal, especially the portion originating from the lower levels of the canopy. A sensitivity analysis enabled assessment of the effects of leaf area index, slope, and canopy height on multiple scattering. PubDate: Mon, 21 Oct 2013 18:29:49 GMT
Authors:firstname.lastname@example.org (Benjamin C. Bright et al); Andrew T. Hudak, Robert McGaughey, Hans-Erik Andersen, José Negrón Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page S1-S13, e-First articles. PubDate: Thu, 26 Sep 2013 12:05:16 GMT
Authors:email@example.com (Paul Romanczyk et al); Jan van Aardt, Kerry Cawse-Nicholson, David Kelbe, Joe McGlinchy, Keith Krause Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page S1-S13, e-First articles. PubDate: Fri, 12 Jul 2013 13:14:11 GMT
Authors:firstname.lastname@example.org (Jean-François Côté et al); Richard A. Fournier, Joan E. Luther Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page S1-S19, e-First articles. PubDate: Wed, 26 Jun 2013 17:26:29 GMT
Authors:email@example.com (Werner Mücke et al); Balázs Deák, Anke Schroiff, Markus Hollaus, Norbert Pfeifer Abstract: Canadian Journal of Remote Sensing, Volume 0, Issue 0, Page S1-S9, e-First articles. PubDate: Fri, 14 Jun 2013 18:03:17 GMT