Reliability and consistency assessment of land cover products at macro and local scales in typical cities

Shi, Wei; Zhao, Xiang; Zhao, Jiacheng; Zhao, Siqing; Guo, Yinkun; Liu, Naijing; Sun, Na; Du, Xiangdong; Sun, Mengmeng

doi:10.1080/17538947.2023.2181992

Cited by 10 publications

(5 citation statements)

References 54 publications

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“…The PA refers to the agreement between the result of tree species identification and actual tree species; the UA shows the usability of the result from the perspective of using the maps; the (OA) and Macro-F1 measure the overall type consistency between the identification result and the real tree species distribution. The indicators PA, UA, OA can be calculated using the following formulas [61,62], and the indicator Macro-F1 is defined in Refs. [63,64]:…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Individual Tree Species Identification Based on a Combination of Deep Learning and Traditional Features

Chen

Jing

et al. 2023

Remote Sensing

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Accurate identification of individual tree species (ITS) is crucial to forest management. However, current ITS identification methods are mainly based on traditional image features or deep learning. Traditional image features are more interpretative, but the generalization and robustness of such methods are inferior. In contrast, deep learning based approaches are more generalizable, but the extracted features are not interpreted; moreover, the methods can hardly be applied to limited sample sets. In this study, to further improve ITS identification, typical spectral and texture image features were weighted to assist deep learning models for ITS identification. To validate the hybrid models, two experiments were conducted; one on the dense forests of the Huangshan Mountains, Anhui Province and one on the Gaofeng forest farm, Guangxi Province, China. The experimental results demonstrated that with the addition of image features, different deep learning ITS identification models, such as DenseNet, AlexNet, U-Net, and LeNet, with different limited sample sizes (480, 420, 360), were all enhanced in both study areas. For example, the accuracy of DenseNet model with a sample size of 480 were improved to 87.67% from 85.41% in Huangshan. This hybrid model can effectively improve ITS identification accuracy, especially for UAV aerial imagery or limited sample sets, providing the possibility to classify ITS accurately in sample-poor areas.

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Section: Accuracy Assessmentmentioning

confidence: 99%

Individual Tree Species Identification Based on a Combination of Deep Learning and Traditional Features

Chen

Jing

et al. 2023

Remote Sensing

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“…The annual China Land Cover Dataset (CLCD) is adopted to analyze LUC in our study. This dataset employs 335,709 Landsat images on the Google Earth Engine (GEE) platform to construct a 30 m resolution annual land cover monitoring database of China [34,35]. As a sequential and high-resolution dataset, it has been used as the basic dataset to analyze LUC in several studies since it is able to reflect the rapid development and a series of ecological projects (e.g., Gain for Green) in China and reveal the anthropogenic implications of LUC under the condition of climate change (e.g., Liu et al (2023) [36]).…”

Section: Data Sourcesmentioning

confidence: 99%

Unpacking the Sub-Regional Spatial Network of Land-Use Carbon Emissions: The Case of Sichuan Province in China

Zhao,

Xie,

Han

2023

Land

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Land, as an essential resource for social, economic and ecological activities, is regarded as a key factor in material production. Against the background of rapid social and economic transition, land-use carbon emissions have gradually drawn due attention. However, few studies have been conducted to explore the spatial relationship of land-use carbon emissions at the sub-regional level, especially within Sichuan Province, China. This study is aimed at unpacking the spatial network of land-use carbon emissions in Sichuan Province by employing the panel data from 2006 to 2021 and using the method of Social Network Analysis. The results indicate that the net land-use carbon emissions of various prefecture-level divisions in Sichuan generally showed an inverse and asymmetrical “V-shaped” trend. The network correlation was improved and the stability was enhanced, gradually developing into a multi-centric structure. In addition, the spatial relationship among different clusters in the network undergoes a transition from intra-regional to inter-regional spillover. Based on these findings, the carbon balance zoning policy was discussed to provide references for how to coordinate roles and positions in the network when optimizing land-use carbon emission management policies in sub-regional areas with rapid social and economic development.

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“…Unlike direct assessment, the indirect assessment method evaluates relative accuracy by analyzing differences between products, primarily through regional consistency and spatial consistency analysis [12][13][14]. Specifically, regional consistency involves statistically assessing the composition of different land cover types within the study area [15]. Spatial consistency compares whether different products have the same land cover type at the same location through pixel-by-pixel spatial overlay analysis [16].…”

Section: Introductionmentioning

confidence: 99%

WenSiM: A Relative Accuracy Assessment Method for Land Cover Products Based on Optimal Transportation Theory

Zhu,

Tan,

Luo

et al. 2024

Remote Sensing

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Land cover (LC) products play a crucial role in various fields such as change detection, resource management, and urban planning. The diversity in methods and principles used to create different products poses a challenge for researchers in choosing the most suitable one for research needs. Mainstream evaluation methods typically consider only a portion of the accuracy information from the product and require a significant effort in creating validation datasets. Here, we propose a relative accuracy assessment method for LC products based on optimal transport theory, which provides a comprehensive evaluation by utilizing a broader range of accuracy information within the product. The method can directly compute the similarity between the target product and the reference truth at a global scale, addressing the issue of quantitatively assessing product accuracy in the absence of a validation dataset. To validate the effectiveness of the method, we select Beijing as the study area to assess the accuracy of four LC products. The results suggest that the method allows for precise quantification of product accuracy, aligning closely with validation outcomes, which can provide valuable guidance to researchers in both product creation and selection.

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Reliability and consistency assessment of land cover products at macro and local scales in typical cities

Cited by 10 publications

References 54 publications

Individual Tree Species Identification Based on a Combination of Deep Learning and Traditional Features

Individual Tree Species Identification Based on a Combination of Deep Learning and Traditional Features

Unpacking the Sub-Regional Spatial Network of Land-Use Carbon Emissions: The Case of Sichuan Province in China

WenSiM: A Relative Accuracy Assessment Method for Land Cover Products Based on Optimal Transportation Theory

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