Change Detection of Soil Formation Rate in Space and Time Based on Multi Source Data and Geospatial Analysis Techniques

Qin, Li; Wang, Shijie; Bai, Xiaoyong; Luo, Geping; Song, Xiaoqing; Tian, Yuan; Hu, Zhang; Yang, Yujie

doi:10.3390/rs12010121

Cited by 14 publications

(6 citation statements)

References 45 publications

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“…For a long time, many scholars have used Pearson correlation analysis to study the relationship between variables (Li et al, 2020; Yang et al, 2019), the determination of the relationship between EHI and influencing factors is mainly accomplished by calculating and verifying the correlation coefficients (Li et al, 2020; Zhang, Feng, Jiang, & Yang, 2015). The formula is presented as follows:

R_{xy} = \frac{\sum_{i = 1}^{n} ([], ((), x_{i} - \overset{X}{true}) ((), y_{i} - \overset{Y}{true}))}{\sqrt{{false\sum}_{i = 1}^{n} {((), x_{i} - \overset{X}{true})}^{2} {false\sum}_{i = 1}^{n} {((), y_{i} - \overset{Y}{true})}^{2}}},

Where: n is the number of samples;

\overset{X}{true}

and

\overset{Y}{true}

are the means of variables x and y , respectively; and R xy is the correlation coefficient between variables x and y .…”

Section: Methodsmentioning

confidence: 99%

“…For a long time, many scholars have used Pearson correlation analysis to study the relationship between variables (Li et al, 2020;Yang et al, 2019), the determination of the relationship between EHI and influencing factors is mainly accomplished by calculating and verifying the correlation coefficients (Li et al, 2020;Zhang, Feng, Jiang, & Yang, 2015). The formula is presented as follows:…”

Section: Pearson Correlation Analysismentioning

confidence: 99%

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Evaluation of temporal and spatial changes of global ecosystem health

et al. 2020

Self Cite

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Understanding the spatial distribution pattern and driving factors behind ecosystem health is beneficial to ecosystem management and restoration. However, the literature shows little in‐depth exploration of regional heterogeneity and the factors that influence ecosystem health on a global scale. This study assesses global ecosystem health for the period 2000–2015 based on the vigor, organization, resilience, and service (VORS) model, and it explores the factors that influence regional differences in ecosystem health. Our results show that: (a) regions with high levels of ecosystem health are distributed mainly near the Equator and within the north–south regression line (0°N–13°N, 0°S–18°S); (b) seven critical zones with high levels of ecosystem health are identified (13°N–18°S, 45°N–65°N, 35°S–56°S, 47°W–80°W, 120°W–130°W, 8°E–37°E, 92°E–157°E); and (c) average annual precipitation and soil moisture play a key role in ecosystem health globally, with correlations of 0.574 and 0.399, respectively. Socioeconomic factors act as bridges, linking and reinforcing the influence of other factors in areas with medium to low levels of ecosystem health. Our study contributes to better understanding of ecosystem health, fills gaps in global ecosystem health diagnosis, and provides reference points for management and recovery of ecosystems.

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R_{xy} = \frac{\sum_{i = 1}^{n} ([], ((), x_{i} - \overset{X}{true}) ((), y_{i} - \overset{Y}{true}))}{\sqrt{{false\sum}_{i = 1}^{n} {((), x_{i} - \overset{X}{true})}^{2} {false\sum}_{i = 1}^{n} {((), y_{i} - \overset{Y}{true})}^{2}}},

Where: n is the number of samples;

\overset{X}{true}

and

\overset{Y}{true}

are the means of variables x and y , respectively; and R xy is the correlation coefficient between variables x and y .…”

Section: Methodsmentioning

confidence: 99%

“…For a long time, many scholars have used Pearson correlation analysis to study the relationship between variables (Li et al, 2020;Yang et al, 2019), the determination of the relationship between EHI and influencing factors is mainly accomplished by calculating and verifying the correlation coefficients (Li et al, 2020;Zhang, Feng, Jiang, & Yang, 2015). The formula is presented as follows:…”

Section: Pearson Correlation Analysismentioning

confidence: 99%

Evaluation of temporal and spatial changes of global ecosystem health

et al. 2020

Self Cite

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“…Affected by soil thickness, there are differences in CS at different depths in the same area. For example, in the karst area of southwest China, despite abundant hydrothermal conditions, most of the soil thickness in this area is less than 1 meter (Li et al, 2020), so the high value of CSF is mostly only within the depth of 0 to 100cm, while the CS potential of minerals at the depth of 100-200cm is greatly reduced (Fig. 2d).…”

Section: Spatial Differentiation Of Carbon Sinkmentioning

confidence: 99%

Large invisible carbon sink potential of global clay minerals to mitigate climate change

Bai

et al. 2021

Preprint

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The chemical weathering of clay minerals widely distributed in the soil have great potential of carbon sink (CS), but the magnitude and influence mechanisms of this CS are unclear. Here we analyse recent changes in five major clay minerals (chlorite, smectite, mica, illite, and vermiculite) carbon sink and its driving factors, using a process-based model (PROFILE) and satellite data assimilation. We show that magnitude of CS in five major clay minerals is about 0.11 Pg C yr-1 from 0 to 2m depth of soil, which is one third of CS in rocks and also may be mainly responsible for the world's missing carbon sink. According to our simulations, the linear trend of CS during 1970-2018 showing that CS in 56% of the world increasing significantly, although the intensification of CS cannot be explained by soil moisture (SM) or soil temperature (STMP) alone, they are the dominant cause of the intensification of CS in the high latitude area and the decrease of CS in parts of the tropics, while in areas where SM is drier, STMP may weaken the former’s negative effect on CS. Besides, simulation results based on medium emission scenarios indicating that CS may increase by about 36% by the end of this century. These results highlight that a more comprehensive understanding of the magnitude and driving mechanism of the soil minerals’ CS is the key to realizing their potential as a nature-based climate solution.

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“…Another study entitled: change detection of soil formation rate in space and time based on multi-source data and geospatial analysis techniques, estimate the dissolution rate and soil formation rate in karst areas of China and analyzed their spatial diversity has been done [10], or in another study, change detection techniques based on multispectral images for investigating land cover dynamics using image processing and mining have been investigated [11]. In another study entitled: Change detection techniques for remote sensing applications, about the distribution of change detection methods [12].…”

Section: Introductionmentioning

confidence: 99%

Spatial Change Recognition Model Using Image Processing and Fuzzy Inference System to Remote Sensing

Mirbod¹

2023

Intelligent Video Surveillance - New Perspectives

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After the advent of satellites whose job is to image the surface of the earth, a huge database of imaging data of the surface of the earth was made available to researchers in various sciences to exploit a large data set in their field of work, and the subject of remote sensing gradually came to the attention of researchers in various sciences. For example, geography, environmental science, civil engineering, etc., each analyzed the visual data of the earth’s surface from the perspective of their field. According to this research, the issue of spatial change recognition and their location and calculating the percentage of changes at the ground level has been considered, and the model presented is based on machine vision, image processing, and a fuzzy interface system to reveal features. This research is in the category of applied research and finally, an application will be presented that can lead to the development of software such as Google Earth and can be added to that as an option. Another of the advantages of this model is its easy use compared to specialized software such as Arc GIS, and this is the novelty of this research.

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Change Detection of Soil Formation Rate in Space and Time Based on Multi Source Data and Geospatial Analysis Techniques

Cited by 14 publications

References 45 publications

Evaluation of temporal and spatial changes of global ecosystem health

Evaluation of temporal and spatial changes of global ecosystem health

Large invisible carbon sink potential of global clay minerals to mitigate climate change

Spatial Change Recognition Model Using Image Processing and Fuzzy Inference System to Remote Sensing

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