Mapping land cover and estimating forest structure using satellite imagery and coarse resolution lidar in the Virgin Islands

Kennaway, Todd A.; Helmer, Eileen H.; Lefsky, M. A.; Brandeis, Tom; Sherrill, Kirk R.

doi:10.1117/1.3063939

Cited by 20 publications

(6 citation statements)

References 28 publications

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“…In another study in the Virgin Islands, Kennaway et al [32] obtained a lower overall accuracy (72%) using the decision tree method for forest type mapping based on Landsat ETM+, DEM and LiDAR data compared to our results, where the overall accuracy reached 83-98%. Comparing the classification accuracy for the forest type mapping using the Sentinel-2 data, our results are in line with the results obtained by Laurin et al [6].…”

Section: Discussioncontrasting

confidence: 82%

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Waśniewski

Hościło

Zagajewski

et al. 2020

Forests

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This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by the Sentinel-2 satellite, normalized difference vegetation index (NDVI) and digital elevation model (DEM), and their combination on the accuracy of the classification of forest cover and forest type. Within the study area, five classes of forest type were delineated: semi-evergreen moist forest, lowland forest, freshwater swamp forest, mangroves, and disturbed natural forest. The classification was performed using the Random Forest (RF) classifier. The overall accuracy for the forest cover ranged between 92.6% and 98.5%, whereas for forest type, the accuracy was 83.4 to 97.4%. The highest accuracy for forest cover and forest type classifications were obtained using a combination of spectral bands at spatial resolutions of 10 m and 20 m and DEM. In both cases, the use of the NDVI did not increase the classification accuracy. The DEM was shown to be the most important variable in distinguishing the forest type. Among the Sentinel-2 spectral bands, the red-edge followed by the SWIR contributed the most to the accuracy of the forest type classification. Additionally, the Random Forest model for forest cover classification was successfully transferred from one master image to other images. In contrast, the transferability of the forest type model was more complex, because of the heterogeneity of the forest type and environmental conditions across the study area.

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Section: Discussioncontrasting

confidence: 82%

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Waśniewski

Hościło

Zagajewski

et al. 2020

Forests

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“…The missing vertical information can either be compensated by analyzing image texture (Wood et al 2012;Coburn and Roberts 2004) or by analyzing the patterns and fractions of shadows and gaps (Leboeuf et al 2007). Remote sensing data have been used in recent years to analyze canopy gaps and their distribution (patterns) in sub-tropical forests (Kennaway et al 2008), boreal forests (Rich et al 2010), Carpathian beech forests , and European mountain forests (Rugani et al 2013;Torresan et al 2014).…”

Section: R a F Tmentioning

confidence: 99%

Assessing the structure of primeval and managed beech forests in the Ukrainian Carpathians using remote sensing

Rehush

Waser‬

2017

Can. J. For. Res.

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Forest structure reflects the forest disturbance regime, and can provide important information about 2 the rate of human impact. A better understanding of the structural variability and large-scale 3 dynamics of natural forests is crucial for "close-to-nature" forest management planning. In this 4 study, we developed a partly automated approach to assess the structure of potential primeval and 5 managed beech forests in the Ukrainian Carpathians using WorldView-2 imagery. We analyzed the 6 local (50 × 50 m scale) canopy closure of these forests by extracting the canopy gaps, and 7 determined four forest structure types ranging from very closed to low density. The occurrence and 8 frequencies of these structure types were significantly different in the primeval and managed beech 9 forests. The four forest structure types were predicted and mapped using multinomial logistic 10 regression based on the textural features derived from the original image bands and two vegetation 11 indices. A 10-fold cross-validation resulted in an overall accuracy of 83% and a kappa coefficient of 12 75%, with the highest agreement for the very closed structure type (87%) and the lowest for the 13 medium and low density (79%). The forest structure type maps can be helpful for planning 14 management activities in beech forests. 16Keywords: beech forests, structure types, canopy gap fraction, WorldView-2, Carpathian region. 33Disturbances are a driving force for forest stand development and affect forest structure. European for the costly additional work of surveying stand structures over large areas (Barrett et al. 2016). and canopy gap properties to characterize beech forests using VHR optical imagery exclusively. 66The objective of the present research was to design a partly automated approach to assess and map 67 the structure of potential primeval and managed beech forests in the Ukrainian Carpathians using 68WorldView-2 data. Assessing the differences in the structure of managed and primeval beech 69 forests can provide important information for managing beech forests sustainably, as well as for The study area is located in the southwest Ukraine ( 89Intensive use of the wood resources in the forests in the study area started approx. 70 years ago. 90According to Roth (1932), the forests in the upper part of the Borzhava river were defined as 91 primeval beech forests because of very limited human impact and high structural variability. These 92 forests were privately owned in the 1930s and were only occasionally used for hunting. Only the 93 narrow belts along the forests adjacent to alpine meadows (approx. 100 m wide) and to grassland in 94 the lower parts (100-300 m wide) were used to a limited extent for wood products (Roth 1932). 95Since the 1950s these forests have belonged to the state forest enterprise and have mostly been information about the acquisition parameters is given in Table 1. 105The original WorldView-2 image was converted to top-of-atmospheric (TOA) reflectance prior to 112The orthorectification revealed a r...

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“…5,6 With LiDAR data becoming more accessible, its use in forest stand delineation has become widespread. Even though some studies prefer classification approaches for forest stand delineation, [7][8][9] most of the studies in the field adopt segmentation approaches for forest stand delineation. 10 Tokola et al 11 derived three artificial bands, namely, forest density index, tree size indicator, and tree species indicator from the LiDAR point cloud and used them in region-growing and watershed segmentation techniques to delineate forest stands.…”

Section: Introductionmentioning

confidence: 99%

“…With LiDAR data becoming more accessible, its use in forest stand delineation has become widespread. Even though some studies prefer classification approaches for forest stand delineation, 7 – 9 most of the studies in the field adopt segmentation approaches for forest stand delineation 10 . Tokola et al 11 .…”

Section: Introductionmentioning

confidence: 99%

Forest stand segmentation with multi-temporal Sentinel-2 imagery and superpixels

Demirpolat,

Leloglu

2024

J. Appl. Rem. Sens.

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Forest stands are the basic units of forest management, whose delineation is a fundamental and significant step for creating stand-level field inventories. Traditional and recent methods for delineation often rely on expensive data and suffer from limited spatial coverage. The Sentinel-2 mission has special bands aimed at vegetation mapping and provides the highest spatial resolution publicly available imagery of the globe. We aim to take the first step for using solely the Sentinel-2 imagery for forest stand delineation. A superpixel segmentation scheme based on a modified version of the simple linear iterative clustering (SLIC) superpixel algorithm, Gaussian processes regressor SLIC (GPR-SLIC) is employed with a feature set composed of Sentinel-2 time-series spectral bands and vegetation indices. GPR-SLIC replaces the spectral distance metric in the original SLIC algorithm with a Gaussian processes regressor, which is trained using the Sentinel-2 feature set and a ground truth forest stand map. GPR-SLIC, a modified SLIC algorithm, leverages multi-temporal Sentinel-2 spectral bands and vegetation indices as features. It replaces the original SLIC's spectral distance metric with a trained GPR. Original SLIC, simple non-iterative clustering, linear spectral clustering, and entropy rate superpixels algorithms are employed for producing reference segmentations based on the LiDAR-derived canopy height model. The findings demonstrate that by incorporating Sentinel-2 time-series features, the GPR-SLIC algorithm yields more precise results for boundary adherence performance metrics and undersegmentation error. The results underscore the potential of employing a machine learning-based superpixel segmentation algorithm with multi-temporal Sentinel-2 images for effectively delineating forest stands.

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Mapping land cover and estimating forest structure using satellite imagery and coarse resolution lidar in the Virgin Islands

Cited by 20 publications

References 28 publications

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Assessing the structure of primeval and managed beech forests in the Ukrainian Carpathians using remote sensing

Forest stand segmentation with multi-temporal Sentinel-2 imagery and superpixels

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