LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy

Singh, Kaushlendra; Vogler, John; Shoemaker, Douglas A.; Meentemeyer, Ross K.

doi:10.1016/j.isprsjprs.2012.09.009

Cited by 115 publications

(68 citation statements)

References 61 publications

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“…The multispectral aerial/satellite imagery was combined with normalized digital surface model derived from LiDAR data (Huang et al, 2008;Chen et al, 2009) or combined with LiDAR height and intensity data (Charaniya et al, 2004;Hartfield et al, 2011;Singh et al, 2012). Compared to previous studies, the presented work in this research used the LiDAR data only and achieved an overall accuracy of 95.1% based on NDVI C2-C3 .…”

Section: Comparison With Previous Studiesmentioning

confidence: 98%

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Morsy

Shaker

El-Rabbany

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the evolution of the LiDAR technology, multispectral airborne laser scanning systems are currently available. The first operational multispectral airborne LiDAR sensor, the Optech Titan, acquires LiDAR point clouds at three different wavelengths (1.550, 1.064, 0.532 m), allowing the acquisition of different spectral information of land surface. Consequently, the recent studies are devoted to use the radiometric information (i.e., intensity) of the LiDAR data along with the geometric information (e.g., height) for classification purposes. In this study, a data clustering method, based on Gaussian decomposition, is presented. First, a ground filtering mechanism is applied to separate non-ground from ground points. Then, three normalized difference vegetation indices (NDVIs) are computed for both non-ground and ground points, followed by histograms construction from each NDVI. The Gaussian function model is used to decompose the histograms into a number of Gaussian components. The maximum likelihood estimate of the Gaussian components is then optimized using Expectation -Maximization algorithm. The intersection points of the adjacent Gaussian components are subsequently used as threshold values, whereas different classes can be clustered. This method is used to classify the terrain of an urban area in Oshawa, Ontario, Canada, into four main classes, namely roofs, trees, asphalt and grass. It is shown that the proposed method has achieved an overall accuracy up to 95.1% using different NDVIs.

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Section: Comparison With Previous Studiesmentioning

confidence: 98%

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Morsy

Shaker

El-Rabbany

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Subsequently the hyperspectral data were orthorectified using a digital terrain model (DTM) derived from LiDAR data. To retain the original pixel values, the nearest neighbor resampling method was used [39,51]. To obtain the entire study area, CASI images were mosaicked using the mosaicking function in the ENVI software.…”

Section: Hyperspectral Datamentioning

confidence: 99%

Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

Luo

Wang

et al. 2015

Remote Sensing

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Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact Airborne Spectrographic Imager) hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity) and CASI data (48 bands) with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA) methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM) and maximum likelihood (MLC) classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions): (1) the layer stacking fusion data; (2) the PCA fusion data; (3) the LiDAR data alone; and (4) the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964) using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the SVM classifier generally performed better than the MLC when classifying multisource data; however, none of the classifiers consistently produced higher accuracies at all spatial resolutions.

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“…Potential problems measuring height may be the result of mixed pixels, which combine spectral information of small tree crowns with the height information of neighboring rooftops [15,61], and possible false measurements due to disturbed reflection caused by free spaces in the canopy [20,62]. Particularly in non-continuous data, the probability is relatively high that the top of a tree is not "hit" by a laser pulse [59].…”

Section: Discussionmentioning

confidence: 99%

Using Airborne LiDAR and QuickBird Data for Modelling Urban Tree Carbon Storage and Its Distribution—A Case Study of Berlin

et al. 2014

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While CO2 emissions of cities are widely discussed, carbon storage in urban vegetation has been rarely empirically analyzed. Remotely sensed data offer considerable benefits for addressing this lack of information. The aim of this paper is to develop and apply an approach that combines airborne LiDAR and QuickBird to assess the carbon stored in urban trees of Berlin, Germany, and to identify differences between urban structure types. For a transect in the city, dendrometric parameters were first derived to estimate individual tree stem diameter and carbon storage with allometric equations. Field survey data were used for validation. Then, the individual tree carbon storage was aggregated at the level of urban structure types and the distribution of carbon storage was analysed. Finally, the results were extrapolated to the entire urban area. High accuracies of the detected tree locations were reached with 65.30% for all trees and 80.1% for dominant trees. The total carbon storage of the study area was 20,964.40 t (σ = 15,550.11 t). Its carbon density equaled 13.70 t/ha. A general center-to-periphery increase in carbon storage was identified along the transect. Our approach methods can be used by scientists and decision-makers to gain an empirical basis for the comparison of carbon storage capacities

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LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy

Cited by 115 publications

References 61 publications

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

Using Airborne LiDAR and QuickBird Data for Modelling Urban Tree Carbon Storage and Its Distribution—A Case Study of Berlin

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