Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment

Naidoo, Laven; Cho, Moses Azong; Mathieu, Renaud; Asner, Gregory P.

doi:10.1016/j.isprsjprs.2012.03.005

Cited by 257 publications

(202 citation statements)

References 24 publications

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“…Metrics of the same family that had a high correlation (r > 0.90) were discarded, keeping at least one per family. Given the usefulness of the Gini index as a variable selection and interpretation criterion demonstrated in many studies [8,54,55], our choice of the highly correlated variables to be discarded was based on this index. The crowns were equally separated into a training subset and a validation subset.…”

Section: Lidar Point and Ppc Height Correspondence Analysismentioning

confidence: 99%

Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

St-Onge

Audet

Bégin

2015

Forests

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Photogrammetric point clouds (PPC) obtained by stereomatching of aerial photographs now have a resolution sufficient to discern individual trees. We have produced such PPCs of a boreal forest and delineated individual tree crowns using a segmentation algorithm applied to the canopy height model derived from the PPC and a lidar terrain model. The crowns were characterized in terms of height and species (spruce, fir, and deciduous). Species classification used the 3D shape of the single crowns and their reflectance properties. The same was performed on a lidar dataset. Results show that the quality of PPC data generally approaches that of airborne lidar. For pixel-based canopy height models, viewing geometry in aerial images, forest structure (dense vs. open canopies), and composition (deciduous vs. conifers) influenced the quality of the 3D reconstruction of PPCs relative to lidar. Nevertheless, when individual tree height distributions were analyzed, PPC-based results were very similar to those extracted from lidar. The random forest classification (RF) of individual trees performed better in the lidar case when only 3D metrics were used (83% accuracy for lidar, 79% for PPC). However, when 3D and intensity or multispectral data were used together, the accuracy of PPCs (89%) surpassed that of lidar (86%).

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Section: Lidar Point and Ppc Height Correspondence Analysismentioning

confidence: 99%

Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

St-Onge

Audet

Bégin

2015

Forests

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“…However, choosing a classification system that comprehensively captures vegetation community composition and structure is still a major challenge for vegetation mapping from remotely sensed data (Rapp et al 2005). Traditionally, the number of vegetation units and/or the properties of vegetation units within a forest have been predefined by the prior knowledge of experts from previous experience or field sampling data (Bork and Su 2007;Carpenter et al 1999;Naidoo et al 2012). However, this could lead to biased or inconsistent classification systems across regions and might not result in optimal breaks among different vegetation communities.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Cho et al (2012), Colgan et al (2012) and Naidoo et al (2012) mapped tree species compositions in African savannas through the combination of LiDAR data and hyperspectral data using maximum likelihood, Random Forest, and Support Vector Machine classifiers, respectively; Dalponte et al (2012) and Hill and Thomson (2005) classified tree species compositions of broadleaf and coniferous mixed forests through the fusion of spectral and LiDAR data; Holmgren et al (2008) and Koukoulas and Blackburn (2005) used a maximum likelihood classifier to identify individual tree species from LiDAR-derived structure parameters and multispectral information in deciduous and coniferous forests, respectively. It has been reported that the integration of LiDAR data and optical imagery can increase the vegetation composition classification accuracy by 16%-20% in rangelands, compared to using only LiDAR data or optical imagery (Bork and Su 2007 structure characteristics, which can be estimated by statistical imputation methods that incorporate field measurements with LiDAR data and optical imagery (Falkowski et al 2010;Hummel at al.…”

Section: Introductionmentioning

confidence: 99%

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

Fry

Collins

et al. 2015

Canadian Journal of Remote Sensing

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Abstract. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (LiDAR) can accurately measure the forest vertical and horizontal structures and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and LiDAR data to map vegetation composition and structure over large spatial scales. Our approach included the use of a Bayesian information criterion algorithm to determine the optimized number of vegetation groups within mixed conifer forests in two study areas in the Sierra Nevada, California, and an unsupervised classification technique and post hoc analysis to map these vegetation groups across both study areas. The results show that the proposed strategy can recognize four and seven vegetation groups at the two study areas, respectively. Each vegetation group has its unique vegetation structure characteristics or vegetation species composition. The overall accuracy and kappa coefficient of the vegetation mapping results are over 78% and 0.64 for both study sites.Résumé. La cartographie précise de la végétation est essentielle entre autres pour la gestion des ressources naturelles, l'analysé ecologique, et la modélisation hydrologique. Les approches d'imagerie multispectrale et hyperspectrale par télédétection se sont avérées de précieuses contributions au processus de la cartographie de la végétation, mais elles ne peuvent fournir qu'un nombre limité de caractéristiques sur la structure de la végétation, qui sont essentielles pour différencier les communautés végétales dans les forêts de composition homogènes. La télédétection par laser «light detection and ranging» (LiDAR) peut mesurer avec précision les structures verticales et horizontales de la forêt, et fournit une formidable opportunité de résoudre ce problème. Cetté etude présente une stratégie qui utiliseà la fois l'imagerie multispectrale aérienne et des données LiDAR pour cartographier la composition et la structure de la végétationà grandeséchelles spatiales. Notre approche comprenait l'utilisation d'un algorithme du critère d'information Bayésien pour déterminer le nombre optimal de groupes de végétation dans les forêts mixtes de conifères sur deux zones d'étude dans les Sierra Nevada, en Californie, ainsi qu'une technique de classification non supervisée et une analyse post hoc pour cartographier ces groupes de végétation dans les deux zones d'étude. Les résultats montrent que la stratégie proposée peut reconnaitre quatre et sept groupes de végétation dans les deux zones d'étude respectivement. Chaque groupe de végétation a des caractéristiques uniques de structure de...

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“…The results of this research did not allow us to confirm the spectral variation hypothesis on a local scale, even though Oldeland et al [56] suggested that the use of hyperspectral information should improve monitoring of species diversity. In this light, recent studies [43,44] have proven that the hyperspectral information from airborne sensors can significantly enhance the predictive power of models not only because of the spectral information but also because of the improvement provided by the spatial resolution [24,26,41,58,59]. Another relevant source of information could be an analysis of seasonal and annual variability that can provide an additional element in estimating biodiversity [24], particularly in deciduous forests like Monte Oscuro.…”

Section: Ecological Implicationsmentioning

confidence: 99%

“…The first stage occupied the Random Forest (RF) package [107] in regression mode as an exploratory analysis to give priority to and select the relevant variables in the prediction of richness. We used RF because of its improvements over other tree classification techniques [59]. Its ability to construct hundreds of decision tree models using random subsets of the variables, in addition to its internal cross-validation and bootstrap aggregation (bagging) [108], make it a powerful tool for variable selection.…”

Section: Statistical Analysis and Spatializationmentioning

confidence: 99%

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

et al. 2015

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Abstract:The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. Heterogeneous environmental conditions involve an important number of niches closely related to species richness. Remote sensing information derived from satellite hyperspectral and airborne Light Detection and Ranging (LiDAR) data can be used as proxies to generate a spatial prediction of vascular plant richness. This study aimed to estimate the spatial distribution of plant species richness using remote sensing in the Andes foothills of the Maule Region, Chile. This region has a secondary deciduous forest dominated by Nothofagus obliqua mixed with sclerophyll species. Floristic measurements were performed using a nested plot design with 60 plots of 225 m 2 each. Multiple predictors were evaluated: 30 topographical and vegetation structure indexes from LiDAR data, and 32 spectral indexes and band transformations from the EO1-Hyperion sensor. A random forest algorithm was used to identify relevant variables in richness prediction, and these variables were used in turn to obtain a final multiple linear regression predictive model (Adjusted R 2 = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R 2 = 0.571, RMSE = 5.05). Selected variables were statistically significant: OPEN ACCESSRemote Sens. 2015, 7 2693 catchment slope, altitude, standard deviation of slope, average slope, Multiresolution Ridge Top Flatness index (MrRTF) and Digital Crown Height Model (DCM). The information provided by LiDAR delivered the best predictors, whereas hyperspectral data were discarded due to their low predictive power.

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Classification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment

Cited by 257 publications

References 24 publications

Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

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