Individual Tree Location Detection by High-Resolution RGB Satellite Imagery in Urban Area

2020 ◽  
Author(s):  
Lingyun Shen ◽  
Mingjing Yan ◽  
Gang Wu ◽  
Xiyou Su
Keyword(s):  
High Resolution ◽  
Urban Area ◽  
Satellite Imagery ◽  
Individual Tree ◽  
Location Detection

scholarly journals Unsupervised individual tree crown detection in high-resolution satellite imagery

2016 ◽  
Vol 10 (1) ◽  
pp. 010501 ◽  
Author(s):  
Alexei N. Skurikhin ◽  
Nate G. McDowell ◽  
Richard S. Middleton
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Tree Crown ◽  
Individual Tree ◽  
High Resolution Satellite Imagery

scholarly journals Mapping Seasonal Tree Canopy Cover and Leaf Area Using Worldview-2/3 Satellite Imagery: A Megacity-Scale Case Study in Tokyo Urban Area

2020 ◽  
Vol 12 (9) ◽  
pp. 1505
Author(s):  
Yutaka Kokubu ◽  
Seiichi Hara ◽  
Akira Tani
Keyword(s):  
Leaf Area ◽  
Urban Area ◽  
Satellite Imagery ◽  
Canopy Cover ◽  
Tree Canopy ◽  
Tree Level ◽  
Total Leaf Area ◽  
Individual Tree ◽  
Total Leaf ◽  
Tree Canopy Cover

This study presents a methodology for developing a high-resolution (2 m) urban tree canopy leaf area inventory in different tree phenological seasons and a subsequent application of the methodology to a 625 km2 urban area in Tokyo. Satellite remote sensing has the advantage of imaging large areas simultaneously. However, mapping the tree canopy cover and leaf area accurately is still difficult in a highly heterogeneous urban landscape. The WorldView-2/3 satellite imagery at the individual tree level (2 m resolution) was used to map urban trees based on a simple pixel-based classification method. The comparison of our mapping results with the tree canopy cover derived from aerial photography shows that the error margin is within an acceptable range of 5.5% at the 3.0 km2 small district level, 5.0% at the 60.9 km2 municipality level, and 1.2% at the 625 km2 city level. Furthermore, we investigated the relationship between the satellite data (vegetation index) and in situ tree-measurement data (leaf area index) to develop a simple model to directly map the tree leaf area from the WorldView-2/3 imagery. The estimated total leaf area in Tokyo urban area in the leaf-on season (633 km2) was twice that of the leaf-off season (319 km2). Our results also showed that the estimated total leaf area in Tokyo urban area was 1.9–6.2 times higher than the results of the moderate-resolution (30 m) satellite imagery.

scholarly journals Development of urban area geospatial information products from high resolution satellite imagery using advanced image analysis techniques

2004 ◽  
Author(s):  
◽  
Aaron K. Shackelford
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Urban Area ◽  
Satellite Imagery ◽  
Training Data ◽  
Automated Image Analysis ◽  
Data Generation ◽  
Geospatial Information ◽  
Information Products ◽  
High Resolution Satellite Imagery

The latest generation of commercial satellite imaging sensors have a number of characteristics (e.g. high spatial resolution, multispectral bands, and quick revisit time), that make them ideal data sources for a variety of urban area applications. The goal of this doctoral research was to develop advanced automated and semi-automated image analysis and classification techniques for the extraction of urban area geospatial information products from commercial high-resolution satellite imagery. We developed two semi-automated urban land cover classification approaches, as well as fully automated techniques for road network and 2-D building footprint extraction. By utilizing fully automated feature extraction techniques for training data generation, a self-supervised classification approach was also developed. The self-supervised classifier is significantly more accurate than traditional classification approaches, and unlike traditional approaches it is fully automated. The development of automated and semi-automated techniques for generation of urban geospatial information products is of high importance not only for the many applications where they can be used but also because the large volume of data collected by these sensors exceeds the human capacity of trained image specialists to analyze. In addition, many applications, especially those for the military and intelligence communities, require near real time exploitation of the image data.

scholarly journals CNN-Based Individual Tree Species Classification Using High-Resolution Satellite Imagery and Airborne LiDAR Data

Forests ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1697
Author(s):  
Hui Li ◽  
Baoxin Hu ◽  
Qian Li ◽  
Linhai Jing
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Airborne Lidar ◽  
Support Vector ◽  
Lidar Data ◽  
Individual Tree ◽  
Sensing Applications ◽  
High Resolution Satellite Imagery ◽  
Airborne Lidar Data

Deep learning (DL) has shown promising performances in various remote sensing applications as a powerful tool. To explore the great potential of DL in improving the accuracy of individual tree species (ITS) classification, four convolutional neural network models (ResNet-18, ResNet-34, ResNet-50, and DenseNet-40) were employed to classify four tree species using the combined high-resolution satellite imagery and airborne LiDAR data. A total of 1503 samples of four tree species, including maple, pine, locust, and spruce, were used in the experiments. When both WorldView-2 and airborne LiDAR data were used, the overall accuracies (OA) obtained by ResNet-18, ResNet-34, ResNet-50, and DenseNet-40 were 90.9%, 89.1%, 89.1%, and 86.9%, respectively. The OA of ResNet-18 was increased by 4.0% and 1.8% compared with random forest (86.7%) and support vector machine (89.1%), respectively. The experimental results demonstrated that the size of input images impacted on the classification accuracy of ResNet-18. It is suggested that the input size of ResNet models can be determined according to the maximum size of all tree crown sample images. The use of LiDAR intensity image was helpful in improving the accuracies of ITS classification and atmospheric correction is unnecessary when both pansharpened WorldView-2 images and airborne LiDAR data were used.

SHALLOW WATER HABITAT MAPPING AND REEF FISH STOCK ESTIMATION USING HIGH RESOLUTION SATELLITE DATA

2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Vincentius P. Siregar ◽  
Sam Wouthuyzen ◽  
Andriani Sunuddin ◽  
Ari Anggoro ◽  
Ade Ayu Mustika
Keyword(s):  
High Resolution ◽  
Reef Fish ◽  
Satellite Imagery ◽  
Field Survey ◽  
Coral Cover ◽  
Fish Density ◽  
Fish Stock ◽  
Benthic Habitat ◽  
Live Coral ◽  
Stock Estimation

Shallow marine waters comprise diverse benthic types forming habitats for reef fish community, which important for the livelihood of coastal and small island inhabitants. Satellite imagery provide synoptic map of benthic habitat and further utilized to estimate reef fish stock. The objective of this research was to estimate reef fish stock in complex coral reef of Pulau Pari, by utilizing high resolution satellite imagery of the WorldView-2 in combination with field data such as visual census of reef fish. Field survey was conducted between May-August 2013 with 160 sampling points representing four sites (north, south, west, and east). The image was analy-zed and grouped into five classes of benthic habitats i.e., live coral (LC), dead coral (DC), sand (Sa), seagrass (Sg), and mix (Mx) (combination seagrass+coral and seagrass+sand). The overall accuracy of benthic habitat map was 78%. Field survey revealed that the highest live coral cover (58%) was found at the north site with fish density 3.69 and 1.50 ind/m2at 3 and 10 m depth, respectively. Meanwhile, the lowest live coral cover (18%) was found at the south site with fish density 2.79 and 2.18  ind/m2 at 3 and 10 m depth, respectively. Interpolation on fish density data in each habitat class resulted in standing stock reef fish estimation:  LC (5,340,698 ind), DC (56,254,356 ind), Sa (13,370,154 ind), Sg (1,776,195 ind) and Mx (14,557,680 ind). Keywords: mapping, satellite imagery, benthic habitat, reef fish, stock estimation

Sign in / Sign up

Export Citation Format

Share Document