Mapping urban impervious surface using object-based image analysis with WorldView-3 satellite imagery

To provide a comprehensive understanding of the spatial distribution of forest carbon reserves, this study explores carbon storage and its spatial pattern in the horizontal and vertical directions on a provincial scale using HJ-CCD remote sensing imagery. Results show that carbon storage in the forests of Hubei Province was 784.46 Tg. In the horizontal direction, Enshi Prefecture contributed the most, with a contribution rate of 22.01%, followed by Yichang (18.74%), Shiyan (15.21%), and Xiangfan (10.61%). Coniferous forests contributed the most to the total carbon reserves of the forests, with a contribution rate of 71.34%, followed by broadleaf forests (25.36%), and mixed forests (3.30%). In the vertical direction, the environmental difference in the vertical direction of the forest ecosystem led to the obvious stratification of carbon storage in the vertical direction, that is: soil layer > tree canopy layer > shrub layer > litter layer. The soil layer had the largest carbon storage, contributing 76.63%, followed by the tree canopy layer (19.05%), shrub layer (2.39%), and litter layer (1.93%). The different contributing layers of coniferous, broadleaf, and mixed forests to carbon storage followed the same order: soil layer > tree canopy layer > shrub layer > litter layer.

Section: Discussionsupporting

confidence: 59%

Section: Discussionmentioning

confidence: 99%

Section: Information Extraction Methods For the Types Of Forestmentioning

confidence: 99%

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Spatial Pattern of Forest Carbon Storage in the Vertical and Horizontal Directions Based on HJ-CCD Remote Sensing Imagery

Luo

2019

“…Object-based methods have received increasing attention in urban impervious surface extraction from high-resolution remote sensing imagery [47][48][49][50][51]. For object-based methods, various features can serve as the model input, such as spectral information, texture features, spatial information of objects, shape features, and characteristics of proximity relationships.…”

Section: Object-based Methodsmentioning

confidence: 99%

Emerging Issues in Mapping Urban Impervious Surfaces Using High-Resolution Remote Sensing Images

Shao

Cheng

et al. 2023

Urban impervious surface (UIS) is a key parameter in climate change, environmental change, and sustainability. UIS extraction has been evolving rapidly in the past decades. However, high-resolution impervious surface mapping is a long-term need. There is an urgent requirement for impervious surface mapping from high-resolution remote sensing imagery. In this paper, we compare current extraction methods in terms of extraction units and extraction models and summarize their strengths and limitations. We discuss the challenges in impervious surface estimation from high spatial resolution remote sensing imagery in terms of selection of spatial resolution, spectral band, and extraction method. The uncertainties caused by clouds and snow, shadows, and vegetation occlusion are also analyzed. Automated sample labeling and remote sensing domain knowledge are the main directions in impervious surface extraction using deep learning methods. We should also focus on using continuous time series of high-resolution imagery and multi-source satellite imagery for dynamic monitoring of impervious surfaces.

“…Image segmentation is widely applied to urban geo-object information extraction, such as road detection [51] and impervious surface extraction [52]. Urban geo-object information extraction usually requires very high spatial resolution images for small urban geo-objects; thus, in this paper, GF-1 images with a very high-resolution of 2 m were used, collected on 8 May 2016 in Beijing, China.…”

Section: Urban Areamentioning

confidence: 99%

Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method

Wang

Meng

et al. 2018

Image segmentation is an important process and a prerequisite for object-based image analysis, but segmenting an image into meaningful geo-objects is a challenging problem. Recently, some scholars have focused on hybrid methods that employ initial segmentation and subsequent region merging since hybrid methods consider both boundary and spatial information. However, the existing merging criteria (MC) only consider the heterogeneity between adjacent segments to calculate the merging cost of adjacent segments, thus limiting the goodness-of-fit between segments and geo-objects because the homogeneity within segments and the heterogeneity between segments should be treated equally. To overcome this limitation, in this paper a hybrid remote-sensing image segmentation method is employed that considers the objective heterogeneity and relative homogeneity (OHRH) for MC during region merging. In this paper, the OHRH method is implemented in five different study areas and then compared to our region merging method using the objective heterogeneity (OH) method, as well as the full lambda-schedule algorithm (FLSA). The unsupervised evaluation indicated that the OHRH method was more accurate than the OH and FLSA methods, and the visual results showed that the OHRH method could distinguish both small and large geo-objects. The segments showed greater size changes than those of the other methods, demonstrating the superiority of considering within-and between-segment heterogeneity in the OHRH method.