Unlocking the Potential of Hyperspectral and LiDAR for above-Ground Biomass (AGB) and Tree Species Classification in Tropical Forests

SUMMARYTropical forests are both important stores of carbon and among the most biodiverse ecosystems on the planet. Reducing emissions from deforestation and degradation (REDD) schemes are designed to mitigate the impacts of climate change, by conserving tropical forests threatened by deforestation or degradation. REDD schemes also have the potential to contribute significantly to biodiversity conservation efforts within tropical forests, however biodiversity conservation and carbon sequestration need to be aligned more closely for this potential to be realized. This paper analyses the relationship between tree species diversity and above-ground biomass (AGB) derived from 1-ha tree plots in Central African rainforests. There was a weakly significant correlation between tree biomass and tree species diversity (r = 0.21, p = 0.03), and a significantly higher mean species diversity in plots with larger AGB estimates (M = 44.38 species in the top eight plots, compared to M = 35.22 in the lower eight plots). In these Central African plots, the relationship between tree species diversity and AGB appeared to be highly variable; nonetheless, high species diversity may often be related to higher biomass and, in such cases, REDD schemes may enhance biodiversity by targeting species diverse forests.

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Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2020.100462 ◽

2021 ◽

Vol 21 ◽

pp. 100462

Author(s):

Sadhana Yadav ◽

Hitendra Padalia ◽

Sanjiv K. Sinha ◽

Ritika Srinet ◽

Prakash Chauhan

Keyword(s):

Random Forest ◽

Tropical Forests ◽

Biomass Estimation ◽

Above Ground Biomass ◽

Ground Biomass ◽

X Band

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An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

Remote Sensing ◽

10.3390/rs13101868 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1868

Author(s):

Martina Deur ◽

Mateo Gašparović ◽

Ivan Balenović

Keyword(s):

Spatial Resolution ◽

Satellite Imagery ◽

Tree Species ◽

Classification Accuracy ◽

New Technologies ◽

Deciduous Forest ◽

Species Classification ◽

Tree Species Classification ◽

Object Based ◽

Very High

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

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