Combining optical satellite data and airborne laser scanner data for vegetation classification

Nordkvist, Karin; Granholm, Ann-Helen; Holmgren, Johan; Olsson, Håkan; Nilsson, Mats

doi:10.1080/01431161.2011.606240

Cited by 19 publications

(7 citation statements)

References 17 publications

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“…The superiority of CART was also observed by Mallinis, et al [22] who found that CART improved classification accuracy by over 20% compared to NN approach in classifying natural forest in Northern Greece. The improvement of sensor fusion shown here is consistent with what other studies have found although the amount of improvement in this study appears lower compared to Bork and Su [8] and Nordkvist, et al [9], who saw a 15-20% improvement when combining LiDAR surfaces with optical sensors. Using only RapidEye enables the capture of plantations reasonably well; giving a minimum producer's accuracy of 80% and a minimum user's accuracy of 83% both achieved by NN-RE only.…”

Section: Initial Land Cover Classificationsupporting

confidence: 76%

“…After excluding the harvested area and new plantings due to temporal differences between the aerial photography and satellite imagery, the producer's accuracy of plantation increased to 89%. The mapping approach used here has produced classification results comparable to previous studies [9,13,23,28,49]. The accuracy of the method was further examined by comparing the mapped plantation area with manually digitised plantation area.…”

Section: Discussionsupporting

confidence: 53%

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Mapping Net Stocked Plantation Area for Small-Scale Forests in New Zealand Using Integrated RapidEye and LiDAR Sensors

Xu¹,

Morgenroth²,

Manley³

2017

Forests

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Abstract:In New Zealand, approximately 70% of plantation forests are large-scale (over 1000 ha) with accurate resource description. In contrast, the remaining 30% of plantation forests are small-scale (less than 1000 ha). It is forecasted that these small-scale forests will supply nearly 40% of the national wood production in the next decade. However, in-depth description of these forests, especially those under 100 ha, is very limited. This research evaluates the use of remote sensing datasets to map and estimate the net stocked plantation area for small-scale forests. We compared a factorial combination of two classification approaches (Nearest Neighbour (NN), Classification and Regression Tree (CART)) and two remote sensing datasets (RapidEye, RapidEye plus LiDAR) for their ability to accurately classify planted forest area. CART with a combination of RapidEye and LiDAR metrics outperformed the other three combinations producing the highest accuracy for mapping forest plantations (user's accuracy = 90% and producer's accuracy = 88%). This method was further examined by comparing the mapped plantations with manually digitised plantations based on aerial photography. The mapping approach overestimated the plantation area by 3%. It was also found that forest patches exceeding 10 ha achieved higher conformance with the digitised areas. Overall, the mapping approach in this research provided a proof of concept for deriving forest area and mapping boundaries using remote sensing data, and is especially relevant for small-scale forests where limited information is currently available.

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Section: Initial Land Cover Classificationsupporting

confidence: 76%

Section: Discussionsupporting

confidence: 53%

Mapping Net Stocked Plantation Area for Small-Scale Forests in New Zealand Using Integrated RapidEye and LiDAR Sensors

Xu¹,

Morgenroth²,

Manley³

2017

Forests

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“…The majority of research on using height data in combination with spectral data for the classification of vegetation has been primarily made related to forestry (e.g., [9][10][11]). However, there are considerable differences in height and spatial extent between trees and aquatic plants.…”

Section: Introductionmentioning

confidence: 99%

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

2017

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Abstract:Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs) can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural features from a true-colour UAS-orthoimage with 5-cm pixels. In the present study, height data from a digital surface model (DSM) created from overlapping UAS-images has been incorporated together with the spectral and textural features from the UAS-orthoimage to test if classification accuracy can be improved further. We studied two levels of thematic detail: (a) Growth forms including the classes of water, nymphaeid, and helophyte; and (b) dominant taxa including seven vegetation classes. We hypothesized that the incorporation of height data together with spectral and textural features would increase classification accuracy as compared to using spectral and textural features alone, at both levels of thematic detail. We tested our hypothesis at five test sites (100 m × 100 m each) with varying vegetation complexity and image quality using automated object-based image analysis in combination with Random Forest classification. Overall accuracy at each of the five test sites ranged from 78% to 87% at the growth-form level and from 66% to 85% at the dominant-taxon level. In comparison to using spectral and textural features alone, the inclusion of height data increased the overall accuracy significantly by 4%-21% for growth-forms and 3%-30% for dominant taxa. The biggest improvement gained by adding height data was observed at the test site with the most complex vegetation. Height data derived from UAS-images has a large potential to efficiently increase the accuracy of automated classification of non-submerged aquatic vegetation, indicating good possibilities for operative mapping.

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“…Although primarily collected for the purpose of constructing a new national terrain model (or digital elevation model; DEM), the collected lidar data is also used by SLU on behalf of the Swedish Forest Agency for constructing a nationwide forest database [10]. Furthermore, it has been shown in earlier studies that lidar data is an excellent complement to optical satellite data for land cover classification, especially for the characterization of tree vegetation [11,12].…”

Section: Introductionmentioning

confidence: 99%

Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey

et al. 2015

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A workflow for combining airborne lidar, optical satellite data and National Forest Inventory (NFI) plots for cost efficient operational mapping of a nationwide sample of 5 × 5 km squares in the National Inventory of Landscapes in Sweden (NILS) landscape inventory in Sweden is presented. Since the areas where both satellite data and lidar data have a common data quality are limited, and impose a constraint on the number of available NFI plots, it is not feasible to perform classifications in a single step. Instead a stratified approach where canopy cover and canopy height are first predicted from lidar data trained with NFI plots is proposed. From the lidar predictions a forest stratum is defined as grid cells with more than 3 m mean tree height and more than 10% vertical canopy cover, the remaining grid cells are defined as open land. Both forest and open land are then classified into broad vegetation classes using optical satellite data. The classification of open land is trained with aerial photo interpretation and the classification of the forest stratum is trained with a new set of NFI plots. The result is a rational procedure for nationwide sample based vegetation characterization.

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Combining optical satellite data and airborne laser scanner data for vegetation classification

Cited by 19 publications

References 17 publications

Mapping Net Stocked Plantation Area for Small-Scale Forests in New Zealand Using Integrated RapidEye and LiDAR Sensors

Mapping Net Stocked Plantation Area for Small-Scale Forests in New Zealand Using Integrated RapidEye and LiDAR Sensors

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

Using Optical Satellite Data and Airborne Lidar Data for a Nationwide Sampling Survey

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