scholarly journals Modeling Stand Height, Volume, and Biomass from Very High Spatial Resolution Satellite Imagery and Samples of Airborne LiDAR

2013 ◽  
Vol 5 (5) ◽  
pp. 2308-2326 ◽  
Author(s):  
Brice Mora ◽  
Michael Wulder ◽  
Joanne White ◽  
Geordie Hobart
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Airborne Lidar ◽  
Very High Spatial Resolution ◽  
Stand Height ◽  
Very High

Forest inventory stand height estimates from very high spatial resolution satellite imagery calibrated with lidar plots

2013 ◽  
Vol 34 (12) ◽  
pp. 4406-4424 ◽  
Author(s):  
Brice Mora ◽  
Michael A. Wulder ◽  
Geordie W. Hobart ◽  
Joanne C. White ◽  
Christopher W. Bater ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Forest Inventory ◽  
High Spatial Resolution ◽  
Height Estimates ◽  
Very High Spatial Resolution ◽  
Stand Height ◽  
Very High

scholarly journals Investigation of Informal Settlement Indicators in a Densely Populated Area Using Very High Spatial Resolution Satellite Imagery

2021 ◽  
Vol 13 (9) ◽  
pp. 4735
Author(s):  
Naledzani Mudau ◽  
Paidamwoyo Mhangara
Keyword(s):  
Order Statistics ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
High Spatial Resolution ◽  
Informal Settlement ◽  
Informal Settlements ◽  
Spectral Indices ◽  
First Order ◽  
Very High Spatial Resolution ◽  
Very High

Automation of informal settlements detection using satellite imagery remains a challenging task in urban remote sensing. This is due to the fact that informal settlements vary in shape, size and spatial arrangement from one region to the other in some cases within a city. This paper investigated the methodology to detect informal settlements in a densely populated township by assessing informal settlement indicators observed from very high spatial resolution satellite imagery. We assessed twelve informal settlement indicators to determine the most effective indicators to distinguish between informal and informal classes. These indicators included the spectral indices first and second-order statistical measurements. In addition to the commonly used informal settlement indicators, we assessed the effectiveness of built-up area and iron cover. The GLCM textural measures performed poorly in separating informal and formal settlements compared to first-order statistics measurement and spectral indices. The built-up area index, coastal blue index and the first-order statistics mean measurements produced higher separability distance of informal and formal settlements. The iron index performed better in separating the two settlement types than the commonly used GLCM measure and NDVI. The proposed ruleset that uses the three features with the highest separability distance achieved producer and user accuracies of informal settlements of 95% and 82%, respectively. The results of this study will contribute towards developing methodologies to automatically detect informal settlements.

scholarly journals SEMANTIC SEGMENTATION OF FOREST STANDS OF PURE SPECIES AS A GLOBAL OPTIMIZATION PROBLEM

2017 ◽  
Vol IV-1/W1 ◽  
pp. 141-148 ◽  
Author(s):  
C. Dechesne ◽  
C. Mallet ◽  
A. Le Bris ◽  
V. Gouet-Brunet
Keyword(s):  
Spatial Resolution ◽  
Tree Species ◽  
High Spatial Resolution ◽  
Semantic Segmentation ◽  
Airborne Lidar ◽  
Forest Stands ◽  
Pure Species ◽  
Individual Tree ◽  
Very High Spatial Resolution ◽  
Very High

Forest stand delineation is a fundamental task for forest management purposes, that is still mainly manually performed through visual inspection of geospatial (very) high spatial resolution images. Stand detection has been barely addressed in the literature which has mainly focused, in forested environments, on individual tree extraction and tree species classification. From a methodological point of view, stand detection can be considered as a semantic segmentation problem. It offers two advantages. First, one can retrieve the dominant tree species per segment. Secondly, one can benefit from existing low-level tree species label maps from the literature as a basis for high-level object extraction. Thus, the semantic segmentation issue becomes a regularization issue in a weakly structured environment and can be formulated in an energetical framework. This papers aims at investigating which regularization strategies of the literature are the most adapted to delineate and classify forest stands of pure species. Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose. The local methods (such as filtering and probabilistic relaxation) are not adapted for such problem since the increase of the classification accuracy is below 5%. The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.

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