Details

Autor(en) / Beteiligte

• Karimi Firozjaei, Mohammad
• Fathololoumi, Solmaz
• Kiavarz, Majid
• Biswas, Asim
• Homaee, Mehdi
• Alavipanah, Seyed Kazem

Titel

Land Surface Ecological Status Composition Index (LSESCI): A novel remote sensing-based technique for modeling land surface ecological status

Ist Teil von

• Ecological indicators, 2021-04, Vol.123, p.107375, Article 107375

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• [Display omitted] •We proposed a new Land Surface Ecological Status Composition Index (LSESCI)•Biophysical Composition Index (BCI) and Land Surface Temperature (LST) are combined.•LSESCI is compared with Remote Sensing-based Ecological Index (RSEI)•LSESCI is superior in modeling SES spatial and temporal variations than RSEI.•SESCI is superior to distinguish SES of different Land Use/Cover (LULC) classes. 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.