ID: 54262
Title: Evaluating a thermal image sharpening model over a mixed agricultural landscape in India
Author: C Jeganathan, N A S Hamm, S Mukherjee, P M Atkinson, P L N Raju, V K Dadhwal
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Sharpening, Dis-aggregation, Land surface temperature, ASTER, MODIS
Abstract: Fine spatial resolution (e.g, <300 m) thermal data are needed regularly to characterise the temporal pattern of surface moisture status, water stress, and to forecast agriculture drought and famine. However, current optical sensors do not provide frequent thermal data at a fine spatial resolution. The TsHARP model provides a possibility to generate fine spatial resolution thermal data from coarse spatial resolution ( >1 km) data on the basis of an anticipated inverse linear relationship between the normalised difference vegetation index (NDVI) at fine spatial resolution and land surface temperature at coarse spatial resolution. The current study utilised the TsHARP model over a mixed agricultural landscape in the northern part of India. Five variants of the model were analysed, including the original model, for their efficiency. Those five variants were the global model (original); the resolution -adjusted global model; the piece-wise regression model; the stratified model; and the local model. The models were first evaluated using Advanced Space-borne Thermal Emission Radiometer (ASTER) thermal data (90m) aggregated to the following spatial resolutions: 180 m, 270 m, 450 m, 630 m, 810 m and 990 m. Although sharpening was undertaken for spatial resolutions from 990m to 90 m, root mean square error (RMSE) of < 2 K could, on average, be achieved only for 990-270 m in the ASTER data. The RMSE of the sharpened images at 270 m, using ASTER data, from the global, resolution-adjusted global, piecewise regression, stratification and local models were 1.91, 1.89, 1.96, 1.91, 1.70 K respectively. The global model, resolution-adjusted global model and local model yielded higher accuracy, and were applied to sharpen MODIS thermal data ( 1 km) to the target spatial resolutions. Aggregated ASTER thermal data were considered as a reference at the respective target spatial resolutions to assess the prediction results from MODIS data. the RMSE of the predicted sharpened image from MODIS using the global, resolution-adjusted global and the local models at 250 m were 3..08, 2.92 and 1.98 K, respectively. The local model consistently led to more accurate sharpened predictions by comparison to other variants.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54261
Title: Validation of a TRMM-based global flood detection system in Bangladesh
Author: Caitlin Balthrop Moffitt, Faisal Hossain, Robert Adler, Koray K Yilmaz, Harold F Pierce
Editor: Alfred Stein
Year: 2011
Publisher: Elsevier, Vol 13, issue 2, April 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: International Journal of Applied Earth Observation And Geoinformation
Keywords: Satellites, Transboundary flooding, Ungauged basins, Tropical Rainfall Measuring Mission, Rainfall, Ground Validation
Abstract: Although the TRMM-based Flood Detection System (FDS) has been in operation in near real-time since 2006, the flood ' detection ' capability has been validated money against qualitative reports in news papers and other types of media. In this study, a more quantitative validation of the FDS over Bangladesh against in situ measurements is presented. Using measured stream flow and rainfall data, the study analyzed the flood detection capability from space for three very distinct river systems in Bangladesh: (1) Ganges-a snowmelt-fed river regulated by upstream India, (2) Brahmaputra-a snow-fed river that is braided, and (3) Meghna - a rain-fed and relatively flashier river. The quantitative assessment showed that the effectiveness of the TRMM-based FDS can vary as a function of season and drainage basin characteristics. Overall, the study showed that the TRMM-based FDS has great potential for flood prone countries like Bangladesh that are faced with tremendous hurdles in transboundary flood management. The system had a high probability of detection overall, but produced increased false alarms during the monsoon period and in regulated basins (Ganges), undermining the credibility of the FDS flood warnings for these situations. For this reason, FDS users are cautioned to verify FDS estimates during the monsoon period and for regulated rivers before implementing flood management practices. Planned improvements by FDS developers involving physically-based hydrologic modeling should transform the system into a more accurate tool for near real-time decision making on flood management for ungauged river basins of the world.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54260
Title: Ground settlement monitoring based on temporarily coherent points between two SAR acquisitions
Author: Lei Zhang, Xiaoli Ding, Zhong Lu
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: SAR, Monitoring, Statistics, change detection, Comparison
Abstract: An InSAR analysis approach for identifying and extracting the temporarily coherent points (TCP) that exist between two SAR acquisitions and for determining motions of the TCP is presented for applications such a ground settlement monitoring . TCP are identified based on the spatial characteristics of the range and azimuth offsets of coherent radar scatterers. A method for coregistering TCP based on the offsets of TCP is given to reduce the coregistration errors at TCP. An improved phase unwrapping method based on the minimum cost flow (MCF) algorithm and local Delaunay triangulation is also proposed for sparse TCP data. The proposed algorithms are validated using a test site in Hong Kong. The test results show that the algorithms work satisfactorily for various ground features
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54259
Title: Rational function modeling for spaceborne SAR datasets
Author: Lu Zhang, Xueyan He, Timo Balz, Xiaohong Wei, Mingsheng Liao
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Rational function model, SAR, Range-Doppler model
Abstract: As a kind of generic sensor model, the rational function model (RFM) has been widely used in geometric processing of optical images, but has not yet been applied to SAR datasets. In this article the feasibility and methodology of rational function (RF) modeling for SAR datasets are investigated. After a review of the mathematic formulation of the RF model and the Range-Doppler model for SAR systems, the feasibility of applying RFM to SAR datasets is analyzed. Afterwards a two-stage approach is proposed as the key technique for SAR RF modeling to solve unknown parameters of RFM in a fast and unbiased way. The effectiveness and advantages of this approach are demonstrated by comparisons with traditional methods. Experimental results obtained for various spaceborne SAR datasets of different processing levels show that RFM is a suitable replacement of the rigorous Range-Doppler model for spaceborne SAR images. Further more, the impacts of several factors including the control point grid size, the number of elevation layers, and the orbit precision on SAR RFM solutions are evaluated quantitatively. The results show that the number of elevation layers is a key factor in SAR RF modeling, and its value showed be set carefully according to terrain conditions of study areas. Finally, potential applications of SAR RFM are discussed in brief.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54258
Title: Orthorectification of VHR optical satellite data exploiting the geometric accuracy of TerraSAR-X data
Author: Peter Reinartz, Rupert Muller, Peter Schwind, Sahil Suri, Richard Bamler
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Orthorectification, Matching, Optical data, Radar data, Mutual information
Abstract: Orthorectification of satellite data is one of the most important pre-processing steps for application oriented evaluations and for image data input into Geograpic Information Systems. Although high-and very high-resolution optical data can be rectified without ground control points (GCPs) using an underlying digital elevation model (DEM) to positional root mean square errors (RMSEs) between 3 m and several hundred meters (depending on the satellite), there is still need for ground control with higher precision to reach lower RMSE values for the orthoimages. The very high geometric accuracy of geocoded data of the TerraSAR-X satellite has been shown in several investigations. This is due to the fact that the SAR antenna measures distances which are mainly dependent on the terrain height and the position of the satellite. The latter can be measured with high precision, whereas the satellite attitude need not be known exactly. If the used DEM is of high accuracy, the resulting geocoded SAR data are very precise in their geolocation. This precision can be exploited to improve the orientation knowledge and thereby the geometric accuracy of the rectified optical satllite data. The challenge is to match two kinds of image data, which exhibit very different geometric and radiometric properties.Simple correlation techniques do not work and the goal is to develop a robust method which works even for urban areas, including radar shows,layover and foreshortening effects. First the optical data have to be rectified with the available interior and exterior orientation data or using rational polynomial coefficients (RPCs). From this approximation, the technique used is the measurement of small identical areas in the optical and radar images by automatic image matching, using a newly developed adapted mutual information procedure followed by a estimation of correction terms for the exterior orientation or the RPC coefficients. The matching areas are selected randomly from a regular grid covering the whole imagery. By adjustment calculations, parameters from falsely matched areas can be eliminated and optimal improvement parameters are found. The original optical data are orthorectified again using the delivered metadata together with these corrections and the available DEM. As proof of method the orthorectified data from IKONOS and ALOS-PRISM sensors are compared with conventional ground control information from high-precision orthoimage maps of the German Cartographic Survey. The results show that this method is robust, even for urban areas. Although the resulting RMSE values are in the order of 2-6 m, the advantage is that this result can be reached even for optical sensors which do not exhibit low RMSE values without using manual GCP measuremnts.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54257
Title: Land cover classification of VHR airborne images for citrus grove identifaction
Author: J Amoros Lopez, E Izquierdo Verdiguier, L Gomez Chova, J Munoz Mari, J Z Rodriquez Barreiro, G Camps Valls, J Calpe Maravilla
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Tree identification, Feature extraction/selection, Classification tree, Support vector machine, Artificial neural networks
Abstract: Managing land resources using remote sensing techniques is becomign a common pratice. However, data analysis procedures should satisfy the high accuracy levels demanded by users (public or private companies and governments) in order to be extensively used. This paper presents a multi-stage classification scheme to update the citrus Geographical Information System (GIS) of the Comunidad Valenciana region (Spain). Spain is the first citrus fruit producer in Europe and the fourth in the world. In particular, citrus fruits represent 67% of the agricultural production in this region, with a total production of 4.24 million tons (campaign 2006-2007). The citrus GIS inventory, created in 2001, needs to be regularly updated in order to monitor changes quickly enough, and allow appropriate policy making and citrus production forecasting. Automatic methods are proposed in this work to facilitate this update, whose processing scheme is summarized as follows. First, an object-oriented feature extraction process is carried out for each cadastral parcel from very high spatial resolution aerial images (0.5 m). Next, several automatic classifiers (decision trees, artificial neural networks, and support vector machines) are trained and combined to improve the final classification accuracy. Finally, the citrus GIS is automatically updated if a high enough level of confidence, based on the agreement between classifiers, is achieved. This is the case for 85% of the parcels and accuracy results exceed 94%. The remaining parcels are classified by expert case for 85% of the parcels and accuracy results exceed 94%. The remaining parcels are classified by expert photo-interpreters in order to guarantee the high accuracy demanded by policy makers.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54256
Title: A fuzzy topology-based maximum likelihood classification
Author: Kimfung Liu, Wenzhong Shi, Hua Zhang
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Fuzzy topology, Maximum likelihood classification (MLC), Thresholding, Remote sensing, Land cover mapping
Abstract: Classification is one of the most widely used remote sensing analysis techniques, with the maximum likelihood classification (MLC) method being a major tool for classifying pixels from an image. Fuzzy topology, in which the set concept is generalized from two values, (0, 1), to the values of a continuous interval, [0,1], is a generalization of ordinary topology and is used to solve many GIS problems, such as spatial information management and analysis. Fuzzy topology is induced by traditional thresholding and as such gives a decomposition of MLC classes.
Presented in this paper is an image classification modification, by which induced threshold fuzzy topology is integrated into the MLC method (FTMLC). Hence, by using the induced threshold fuzzy topology, each image class in spectral space can be decomposed into three parts: an interior, a boundary and an exterior. The connection theory in induced fuzzy topology enables the boundary to be combined with the interior. That is a new classification method is derived by integrating the induced fuzzy topology and the MLC method. As a result, fuzzy boundary pixels, which contain many misclassified and over classified pixels, are able to be re-classified, providing improved classification accuracy. This classification is a significantly improved pixel classification method, and hence provides improved classifcation accuracy.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54255
Title: Updating the Phase I habitat map of Wales, UK, using satellite sensor data
Author: Richard Lucas, Katie Medcalf, Alan Brown, Peter Bunting, Johanna Breyer, Dan Clewly, Steve keyworth, Philippa Blackmore
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Land cover, Habitat classification, Satellite, Wales, Object-oriented eCognition
Abstract: The Phase 1 Survey is the most comprehensive and widely used national level map of semi-natural habitats in Wales. However, the survey was based largely on field survey and was conducted over several decades, before being completed in 1997. Given that resources for a repeat survey were limited, this study has used an object-oriented rule-based classification implemented within eCognition of multi-temporal satellite sensor data acquired between 2003 and 2006 to map semi-natural habitats and agricultural land across Wales, thereby allowing a progressive update of the Phase 1 Survey. The classification of objects to Phase 1 habitat classes was undertaken in two steps; firstly the landscape of Wales was divided into objects using orthorectified SPOT-5 High Resolution Geometric (HRG) reflectance data (10 m spatial resolution) and Land Parcel Information System (LPIS) boundaries. A rule-base was then developed to progressively discriminate and map the distribution of 105 sub-habitats across Wales based on time-series of SPOT HRG, Terra-1 Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Indian Remote Sensing Satellite (IRS) LISS-3 data, derived datasets (e.g., vegetation indices, fractional images) and ancillary information (e.g., topography). The rules coupled knowledge of ecology and the information content of these remote sensing data using a combination of thresholds, Boolean operations and fuzzy membership functions. A second rule-base was then developed to translate the more detailed sub-habitat classification to Phase 1 habitat classes. Indicative accuracies of the revised Phase 1 mapping, based on comparisons with the later Phase 2 survey (for selected habitats), were >80% overall and typically between 70% and 90% for many classes. Through this exercise, Wales has become the first country in Europe to produce a national map of habitats (as opposed to land cover) through object-orientated classification of satellite sensor data. Furthermore, the approach can be adapted to allow continual monitoring of the extent and condition of habitats and agricultural land.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54254
Title: Transmission and visualization of large geographical maps
Author: Liqiang Zhang, Liang Zhang, Yingchao Ren, Zhifeng Guo
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Large maps, Voronoi diagram, Out-of-core, Progressive transmission
Abstract: Transmissio and visualization of large geographical maps have become a challenging research issue in GIS applications. This paper presents an efficient and robust way to simplify large geographical maps using frame buffers and Voronoi diagrams. The topological relationships are kept durign the simplification by removing the Voronoi diagram ' s self-overlapped regions With the simplified vector maps, we establish different levels of detail (LOD) models of these maps. Then we introduce a client/server architecture which integrates our out - of - core algorithm, progressive transmission and rendering scheme based on computer graphics hardware. The architecture allows the viewers to view different regions interactively at different LODs on the network. Experimental results show that our proposed scheme provides an effective way for powerful transmission and manipulation of large maps.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54253
Title: Assessment of radargrammetric DSMs from TerraSAR -X stripmap images in the mountainous relief area of the Amazon region
Author: Cleber Gonzales de Oliveira, Waldir Renato Paradella, Arnaldo de Queiroz da Silva
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: DSM, TerraSAR-X Stripmap, topographic mapping, Brazilian Amazon
Abstract: The Brazilian Amazon is a vast territory with an enormous need for mapping and monitoring of renewable and non-renewable resources. Due to the adverse environmental condition (rain, cloud, dense vegetation) and difficult access, topographic information is still poor, and when available needs to be updated or remapped. In this paper, the feasibility of using Digital Surface Models (DSMs) extracted from TerraSARX Stripmap stereo-pair images for detailed topographic mapping was investigated for a mountainous area in the Caraja ' s Mineral Province, located on the easternmost border of the Brazilian Amazon. The quality of the radargrammetric DSMs was evaluated regarding field altimetric measurements. Precise topographic field information acquired from a Global Positioning System (GPS) was used as Ground Control Points (GCPs) for the modelling of the stereoscopic DSMs and as Independent Check Points (ICPs) for the calculation of elevation accuracies. The analysis was performed following two ways: (1) the use of Root Mean Square Error (RMSE) and (2) calcualtions of systematic error (bias) and precision. The test for significant systematic error was based on the Student ' s-t distribution and the test of precision was based on the Chi-squared distribution. The investigation has shown that the accuarcy of the TerraSAR-X Stripmap DSMs met the requirements for 1:50,000 map (Class A) as requested by the Brazilian Standard for Cartographic Accuarcy. Thus, the use of TerraSAR -X Stripmap images can be considered a promising alternative for detailed topographic mapping in similar environments of the Amazon region, where available toptographic information is rare or presents low quality.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54252
Title: Relevance of airborne lidar and multispectral image data for urban scene classification using random forests
Author: Li Guo, Nesrine Chehata, Clement Mallet, Samia Boukir
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Lidar, Multispectral image, Urban, Random forests, Variable importance
Abstract: Airborne lidar systems have become a source for the acquisition of elevation data. They provide georeferenced, irregulary distributed 3D point clouds of high altimetric accuracy. Moreover, these systems can provide for a single laser pulse, multiple returns or echoes, which correspond to different illuminated objects. In addition to multi-echo laser scanners, full-waveform systems are able to record 1D signals representing a train of echoes caused by reflections at different targets. These systems provide more information about the structure and the physical characteristics of the targets. Many approaches have been developed, for urban mapping, based on aerial lidar solely or combined with multispectral image data. However, they have not assessed the importance of input features. In this paper, we focus on a multi-source framework uisng aerial lidar (multi-echo and full waveform) and aerial multispectral image data. We aim to study the feature relevance for dense urban scenes. The Random Forests algorithm is chosen as a classifier: it runs efficiently on large datasets, and provides measures of feature importance for each class. The margin theory is used as a confidence measure of the classifier, and to confirm ;the relevance of input features for urban classification. Teh quantitative results confirm the importance of the joint use of optical multispectral and lidar data. Moreover, the relevance of full-waveform lidar features is demonstrated for building and vegetation area discrimination.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54251
Title: Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint
Author: J L Lovell, D L B Jupp, G J Newnham, D S Culvenor
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: LIDAR, Forestry, Inventory, Laser scanning, Terrestrial
Abstract: This paper presents a method for using the intensity of returns from a scanning light detection and ranging (lidar) system from a single viewing point to identify the location and measure the diameter of tree stems within a forest. Such instruments are being used for rapid forest inventory and to provide consistent supporting information for airborne lidars. The intensity transect across a tree stem is found to be consistent with a simple model parameterised by the range and diameter of the trunk. The stem diameter is calculated by fitting the model to transect data. The angular span of the stem can also be estimated by using a simple threshold where intensity values are tested against the expected intensity for a stem of given diameter. This is useful when data are insufficient to fit the model or the stem is partially obscured. The process of identifying tree positions and trunk diameters is fully automated and is shown to be successful in identifying a high proportion of trees, including some that are partially obscured from vies. The range and bearing to trees are in excellent agreement with field data. Trunk angular span and diameter estimations are well correlated with field measurements at the plot scale. The accuracy of diameter estimation is found to decrease with range from the scanning position and is also reduced for stems subtending small angles (less than twice the scanning resolution) to the instrument. A method for adjusting survey results to compensate for trees missed due to obscuration from the scanning point and the use of angle count method is found to improve basal area estimates and achieve agreement within 4% of field measurements.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54250
Title: Generalization of DEM for terrain analysis using a compound method
Author: Qiming Zhou, Yumin Chen
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: DEM/DTM, Surface, Generalization, Triangulation, Geomorphology
Abstract: This paper reports an investigation into the generalization of a grid-based digital elevation model (DEM) for the purpose of terrain analysis. The focus is on the method of restructuring the grid-based surface elevation data to form a triangulated irregular network (TIN) that is optimized to keep the important terrain features and slope morphology with the minimum number of sample points. The critical points of the terrain surface are extracted from the DEM based on their significance, measured not only by their local relief, but also by their importance in identifying inherent geomorphological and drainage features in the DEM. A compound method is proposed by integrating the traditional point-additive and feature-point methods to construct a drainage-constrained TIN. The outcome is then compared with those derived from other selected methods including filtering, point-additive or feature-point algorithms. The results show that the compound approach is capable of taking advantage of both point-additive and feature-point algorithms to maximally keep the terrain features and to maintain RMSE at an acceptable level, while reducing the elevation data points by over 99%. The analytical result also shows that the proposed method outperforms the compared methods with better control in retaining drainage features at the same level of RMSE.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54249
Title: Predicting individual tree attributes from airborne laser point clouds based on the random forests technique
Author: Xiaowei Yu, Juha Hyyppa, Mikko Vastaranta, Markus Holopainen, Risto Viitala
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Laser scanning, Forestry, Prediction, feature, detection
Abstract: This paper depicts an approach for predicting individual tree attributes, ie., tree height, diameter at breast height (DBH) and stem volume, based on both physical and statistical features derived from airborne laser - scanning data utilizing a new detection method for finding individual trees together with random forests as an estimation method. The random forests (also called regression forests) technique is a nonparametric regression method consisting of a set of individual regression trees. Tests of the method were performed, using 1476 trees in a boreal forest area in southern Finland and laser data with a density of 2.6 points per m2. Correlation coefficients (R) between the observed and predicted values of 0.93, 0.79 and 0.87 for individual tree height, DBH and stem volume, respectively, wee achieved, based on 26 laser-derived features. The corresponding relative root-mean-squared errors (RMSEs) were 10.03%, 21.35% and 45.77% (38% in best cases), which are similar to those obtained with the linear regression method, with maximum laser heights, laser-estimated DBH or crown diameters as predictors. With random forests, however, the forest models currently used for deriving the tree attributes are not needed. Based on the results, we conclude that the method is capable of providing a stable and consistent solution for determining individual tree attributes using small-footprint laser data.
Location: 231
Literature cited 1: None
Literature cited 2: None
ID: 54248
Title: Assessing intra-annual vegetation regrowth after fire using the pixel based regeneration index
Author: S Lhermitte, J Verbesselt, W W Verstraeten, S Veraverbeke, P Coppin
Editor: George Vosselman
Year: 2011
Publisher: Elsevier, Vol 66, No 1, January 2011
Source: Centre for Ecological Sciences
Reference: None
Subject: ISPRS Journal of Photogrammetry and Remote Sensing
Keywords: Forest fire, Monitoring, Temporal, Spatial, Vegetation
Abstract: Several remote sensing studies have discussed the potential of satellite imagery as an alternative for extensive field sampling to quantify fire-vegetation impact over large areas. Most studies depend on Landsat image availability with infrequent image acquisition dates and consequently are limited for assessing intra-annual fire-vegetation dynamics or comparing different fire plots and dates. The control pixel based regeneration index (PRI) derived from SPOT-VEGETATION (VGT) normalized difference vegetation index (NDVI) is used in this study as an alternative to the traditional bi-temporal Landsat approach based on the normalized burn ratio (NBR). The major advantage of the PRI is the use of unburnt control plots which allow the expression of the intra-annual variation due to regeneration processes without external influences. In the comparison of Landsat and VGT data, (i) the inter-annual differences between the bi-temporal and control plot approach were contrasted and (ii) metrics of PRI were derived and compared with the inter-annual dynamics of both VGT and Landsat data. Results of these comparison, demonstrate the overall similarity between NBR and NDVI data, stress the importance of the elimination of external influences (e.g., phenological variations), and emphasize the failure of including post-fire vegetation responses in bi-temporal Landsat assessments, especially in quickly recovering ecotypes with a strong annual phenological cycle such as savanna. This highlights the importance of using high frequency multi-temporal approaches to estimate fire-vegetation impact in temporally dynamic vegetation types.
Location: 231
Literature cited 1: None
Literature cited 2: None