scholarly journals Estimating forest above-ground carbon using object-based analysis of very high spatial resolution satellite images

2017 ◽  
Vol 11 (12) ◽  
pp. 587-600
Author(s):  
G. Workie T.
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Object Based ◽  
Very High Spatial Resolution ◽  
Very High

scholarly journals Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

2020 ◽  
Vol 12 (18) ◽  
pp. 3092 ◽  
Author(s):  
Mathieu Varin ◽  
Bilel Chalghaf ◽  
Gilles Joanisse
Keyword(s):  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Individual Species ◽  
Global Approach ◽  
Conifer Species ◽  
Object Based ◽  
Very High Spatial Resolution ◽  
Very High ◽  
Tree Type

Species identification in Quebec, Canada, is usually performed with photo-interpretation at the stand level, and often results in a lack of precision which affects forest management. Very high spatial resolution imagery, such as WorldView-3 and Light Detection and Ranging have the potential to overcome this issue. The main objective of this study is to map 11 tree species at the tree level using an object-based approach. For modeling, 240 variables were derived from WorldView-3 with pixel-based and arithmetic feature calculation techniques. A global approach (11 species) was compared to a hierarchical approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were compared: support vector machine, classification and regression tree, random forest (RF), k-nearest neighbors, and linear discriminant analysis. Each model was assessed using 16-band or first 8-band derived variables, with the results indicating higher precision for the RF technique. Higher accuracies were found using 16-band instead of 8-band derived variables for the global approach (overall accuracy (OA): 75% vs. 71%, Kappa index of agreement (KIA): 0.72 vs. 0.67) and tree type level (OA: 99% vs. 97%, KIA: 0.97 vs. 0.95). For broadleaf individual species, higher accuracy was found using first 8-band derived variables (OA: 70% vs. 68%, KIA: 0.63 vs. 0.60). No distinction was found for individual conifer species (OA: 94%, KIA: 0.93). This paper demonstrates that a hierarchical classification approach gives better results for conifer species and that using an 8-band WorldView-3 instead of a 16-band is sufficient.

scholarly journals Mapping the Mangrove Forest Canopy Using Spectral Unmixing of Very High Spatial Resolution Satellite Images

2019 ◽  
Vol 11 (3) ◽  
pp. 367 ◽  
Author(s):  
Florent Taureau ◽  
Marc Robin ◽  
Christophe Proisy ◽  
François Fromard ◽  
Daniel Imbert ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Forest Structure ◽  
Mangrove Forest ◽  
Satellite Images ◽  
Forest Canopy ◽  
High Spatial Resolution ◽  
Spectral Unmixing ◽  
Very High Spatial Resolution ◽  
Very High ◽  
Mangrove Structure

Despite the low tree diversity and scarcity of the understory vegetation, the high morphological plasticity of mangrove trees induces, at the stand level, a very large variability of forest structures that need to be mapped for assessing the functioning of such complex ecosystems. Fully constrained linear spectral unmixing (FCLSU) of very high spatial resolution (VHSR) multispectral images was tested to fine-scale map mangrove zonations in terms of horizontal variation of forest structure. The study was carried out on three Pleiades-1A satellite images covering French island territories located in the Atlantic, Indian, and Pacific Oceans, namely Guadeloupe, Mayotte, and New Caledonia archipelagos. In each image, FCLSU was trained from the delineation of areas exclusively related to four components including either pure vegetation, soil (ferns included), water, or shadows. It was then applied to the whole mangrove cover imaged for each island and yielded the respective contributions of those four components for each image pixel. On the forest stand scale, the results interestingly indicated a close correlation between FCLSU-derived vegetation fractions and canopy closure estimated from hemispherical photographs (R2 = 0.95) and a weak relation with the Normalized Difference Vegetation Index (R2 = 0.29). Classification of these fractions also offered the opportunity to detect and map horizontal patterns of mangrove structure in a given site. K-means classifications of fraction indeed showed a global view of mangrove structure organization in the three sites, complementary to the outputs obtained from spectral data analysis. Our findings suggest that the pixel intensity decomposition applied to VHSR multispectral satellite images can be a simple but valuable approach for (i) mangrove canopy monitoring and (ii) mangrove forest structure analysis in the perspective of assessing mangrove dynamics and productivity. As with Lidar-based surveys, these potential new mapping capabilities deserve further physically based interpretation of sunlight scattering mechanisms within forest canopy.

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