Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status
Accurate modeling of Land Surface Ecological Status (LSES) is crucial in environmental applications. Despite valuable benefits, common indices are unable to distinguish LSES of bare soils from lands affected by Anthropogenic Destructive Activities (ADAs). The objective of this study was to present a...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-04-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X21000406 |
| _version_ | 1818936079151005696 |
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| author | Mohammad Karimi Firozjaei Solmaz Fathololoumi Majid Kiavarz Asim Biswas Mehdi Homaee Seyed Kazem Alavipanah |
| author_facet | Mohammad Karimi Firozjaei Solmaz Fathololoumi Majid Kiavarz Asim Biswas Mehdi Homaee Seyed Kazem Alavipanah |
| author_sort | Mohammad Karimi Firozjaei |
| collection | DOAJ |
| description | Accurate modeling of Land Surface Ecological Status (LSES) is crucial in environmental applications. Despite valuable benefits, common indices are unable to distinguish LSES of bare soils from lands affected by Anthropogenic Destructive Activities (ADAs). The objective of this study was to present an index to distinguish LSES of different Land Use/Covers (LULCs) particularly bare soils from lands affected by ADAs using remote sensing images. Landsat multi-temporal imagery, National Land Cover Database (NLCD), Imperviousness and High Resolution Layer Imperviousness (HRLI) datasets for Arasbaran protected area in Iran and 13 cities from the United States and Europe were used in this study. First, the surface biophysical characteristics and LULC were derived from Landsat images using the single channel algorithm, spectral indices, and support vector machine. Secondly, a new index was developed based on improved Ridd's conceptual Vegetation-Impervious-Soil triangle model and specified as Land Surface Ecological Status Composition Index (LSESCI). LSESCI was developed by combining Biophysical Composition Index (BCI) information and Land Surface Temperature (LST). In the third step, the LSES was modeled based on Remote Sensing-based Ecological Index (RSEI). Variance-based global sensitivity analysis was used to calculate the impact of input parameters on the modeled LSES. Afterwards, the variations in these indices were modeled using Subtraction, Variance and Principal Component Analysis (PCA) strategies. Finally, the efficiency of these indices was assessed and compared to model from the relationships between LSESCI and RSEI with spectral indices, and LULC classes. There was an overall improvement in modelling LSES accuracy using the LSESCI over RSEI. For instance, the difference between the mean RSEI and LSESCI for the lands affected by ADAs and Bare soil lands in Arasbaran protected area in Iran were 0.04 and 0.27, respectively. LST and Wetness have the most and least impact on LSES modeling, respectively, compared to other input parameters. The mean absolute value of the correlation coefficient (r) between greenness, moisture, dryness, and heat indices and LSESCI (RSEI) were 0.90 (0.84), 0.76 (0.69) and 0.93 (0.88), respectively. The mean absolute values of r between variations of different spectral indices and variations of LSESCI (RSEI) obtained from PCA, Variance and Subtraction strategies were 0.89 (0.87), 0.73 (0.64) and 0.79 (0.73), respectively. Similarly, for selected cities in the United States and Europe, the mean of r values between RSEI and LSESCI and NLCD Imperviousness (HRLI) were 0.58 (0.77) and 0.77 (0.85), respectively. Overall, the LSESCI had high ability to distinguish the LSES of different LULC classes especially bare soils from lands affected by ADAs. Thus, the proposed LSESCI was superior in modeling LSES of the urban and non-urban regions with heterogeneous surface over RSEI. |
| first_indexed | 2024-12-20T05:30:21Z |
| format | Article |
| id | doaj.art-599e3a277066411a92fb6d4207be0f09 |
| institution | Directory Open Access Journal |
| issn | 1470-160X |
| language | English |
| last_indexed | 2024-12-20T05:30:21Z |
| publishDate | 2021-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj.art-599e3a277066411a92fb6d4207be0f092022-12-21T19:51:45ZengElsevierEcological Indicators1470-160X2021-04-01123107375Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological statusMohammad Karimi Firozjaei0Solmaz Fathololoumi1Majid Kiavarz2Asim Biswas3Mehdi Homaee4Seyed Kazem Alavipanah5Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, IranFaculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran; Corresponding authors.Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, IranSchool of Environmental Sciences, University of Guelph, Guelph, Ontario, Canada; Corresponding authors.Agrohydrology Research Group, College of Agriculture, Tarbiat Modares University, Tehran 14115-336, IranDepartment of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, IranAccurate modeling of Land Surface Ecological Status (LSES) is crucial in environmental applications. Despite valuable benefits, common indices are unable to distinguish LSES of bare soils from lands affected by Anthropogenic Destructive Activities (ADAs). The objective of this study was to present an index to distinguish LSES of different Land Use/Covers (LULCs) particularly bare soils from lands affected by ADAs using remote sensing images. Landsat multi-temporal imagery, National Land Cover Database (NLCD), Imperviousness and High Resolution Layer Imperviousness (HRLI) datasets for Arasbaran protected area in Iran and 13 cities from the United States and Europe were used in this study. First, the surface biophysical characteristics and LULC were derived from Landsat images using the single channel algorithm, spectral indices, and support vector machine. Secondly, a new index was developed based on improved Ridd's conceptual Vegetation-Impervious-Soil triangle model and specified as Land Surface Ecological Status Composition Index (LSESCI). LSESCI was developed by combining Biophysical Composition Index (BCI) information and Land Surface Temperature (LST). In the third step, the LSES was modeled based on Remote Sensing-based Ecological Index (RSEI). Variance-based global sensitivity analysis was used to calculate the impact of input parameters on the modeled LSES. Afterwards, the variations in these indices were modeled using Subtraction, Variance and Principal Component Analysis (PCA) strategies. Finally, the efficiency of these indices was assessed and compared to model from the relationships between LSESCI and RSEI with spectral indices, and LULC classes. There was an overall improvement in modelling LSES accuracy using the LSESCI over RSEI. For instance, the difference between the mean RSEI and LSESCI for the lands affected by ADAs and Bare soil lands in Arasbaran protected area in Iran were 0.04 and 0.27, respectively. LST and Wetness have the most and least impact on LSES modeling, respectively, compared to other input parameters. The mean absolute value of the correlation coefficient (r) between greenness, moisture, dryness, and heat indices and LSESCI (RSEI) were 0.90 (0.84), 0.76 (0.69) and 0.93 (0.88), respectively. The mean absolute values of r between variations of different spectral indices and variations of LSESCI (RSEI) obtained from PCA, Variance and Subtraction strategies were 0.89 (0.87), 0.73 (0.64) and 0.79 (0.73), respectively. Similarly, for selected cities in the United States and Europe, the mean of r values between RSEI and LSESCI and NLCD Imperviousness (HRLI) were 0.58 (0.77) and 0.77 (0.85), respectively. Overall, the LSESCI had high ability to distinguish the LSES of different LULC classes especially bare soils from lands affected by ADAs. Thus, the proposed LSESCI was superior in modeling LSES of the urban and non-urban regions with heterogeneous surface over RSEI.http://www.sciencedirect.com/science/article/pii/S1470160X21000406Anthropogenic Destructive Activities (ADAs)Bare soilImpervious surfacePrincipal Component Analysis (PCA)Land Surface Ecological Status (LSES)Remote sensing |
| spellingShingle | Mohammad Karimi Firozjaei Solmaz Fathololoumi Majid Kiavarz Asim Biswas Mehdi Homaee Seyed Kazem Alavipanah Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status Ecological Indicators Anthropogenic Destructive Activities (ADAs) Bare soil Impervious surface Principal Component Analysis (PCA) Land Surface Ecological Status (LSES) Remote sensing |
| title | Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status |
| title_full | Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status |
| title_fullStr | Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status |
| title_full_unstemmed | Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status |
| title_short | Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status |
| title_sort | land surface ecological status composition index lsesci a novel remote sensing based technique for modeling land surface ecological status |
| topic | Anthropogenic Destructive Activities (ADAs) Bare soil Impervious surface Principal Component Analysis (PCA) Land Surface Ecological Status (LSES) Remote sensing |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X21000406 |
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