Spatial analysis of biological soil crust based on Biological Soil Crust (BSCI) index
Subject Areas : Natural resources and environmental managementLeila Kashi Zenouzi 1 , Seyed Hasan Kaboli 2 , Kazem Khavazi 3 , Mohammad Sohrabi 4 , Mohammad Khosroshahi 5
1 - PhD Candidate in Combat to Desertification Department, Faculty of Desert Studies, University of Semnan, Iran
2 - Assistant Professor of Combat to Desertification Department, Faculty of Desert Studies, University of Semnan, Iran
3 - Professor, Department of Soil Biology Soil and Water Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran
4 - Assistant Professor, Iranian Research Organization for Science and Technology, Tehran, Iran
5 - Associate Professor, Desert Research Division, Research Institute of Forests and Rangelands, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran
Keywords: soil line index, Sejzi plain, Cyanolichen,
Abstract :
Background and ObjectiveBiological soil crusts are a collection of lichens, mosses, fungi, cyanobacteria, etc. that are part of the soil ecosystem. Estimation of density and distribution of biological soil crusts in arid and semi-arid regions of Iran, which is the subject of soil erosion and wastage is very important. Methods based on remote sensing techniques are important in terms of cost and time less efficient methods to achieve this goal. Segzi plain is one of the critical points of wind erosion in Iran and identifying and determining the distribution of biological soil crusts as a soil modifier is an effective step in reducing wind erosion in the region. In this research, BSCI (Biological Soil Crust) index has been used to prepare the distribution map of lichen-dominated biological soil crusts. Materials and Methods The study area is part of the Sajzi Desert (Central Deserts of Iran) which is located in Isfahan province of Iran. The study area with an area of 199.5 hectares is spread between the eastern lengths of 51o52'32" to 52o27'41" and the northern widths of 32o33'31" to 32o55'01". The average slope of Segzi plain is 1.08 percent and its average height is 1680 meters. According to the statistics of the East Isfahan Meteorological Station (Shahid Beheshti Station), the average annual rainfall in the region is 106 mm. According to the Dumarten climatic classification, the climate of the region is dry and according to the Amberge classification it is cold. The BSCI index is a combination of the relationships used to estimate vegetation and bare soil surface, and its mathematical relationship is the slope of the soil line. To calculate the soil line in an area, one must first separate the pixels that have bare soil and no vegetation. In order to calculate the soil line equation, in four seasons of a year, images of Landsat OLI 8 satellite related to 2018 were downloaded from the site of the US Geological Survey and 20 to 30 pixels of pure bare soil were extracted by drawing the reflection values of these pixels in the red and infrared band. Red near soil line coefficients was calculated for each season in the Segzi Plain. Based on BSCI index, lichen-dominated biological soil crust are identified using at least VIS-NIR spectral reflection and the slope between the red and green bands compared to bare soil and dry vegetation. Using ENVI software, the distribution shells of biological shells with lichen dominance were prepared in four seasons since 2018 in Segzi plain. Then, the prepared maps were validated based on land points and the total accuracy and kappa index were calculated in all four seasons. The collected lichen samples were identified based on their morphological characteristics and using a stereomicroscope, conventional microscope and common color reagents such as potassium hydroxide (KOH). After applying the BSCI index on the Landsat OLI 8 satellite image, using ENVI software, spectral profiles related to 4 points of Segzi plain in four seasons of the year were prepared and the spectral reflection in four seasons of the year in different points were examined. Results and Discussion The slope of the soil line is lower in the rainy season, which coincides with the growth of herbaceous and annual plants, compared to the summer season, which has the least amount of rainfall, and the annual plants have dried up and become extinct. In May, the slope of the soil line was minimal (0.39) and in late summer it has its maximum value (0.78). In fact, the slope of the soil line has decreased from mid-August to May, and then has increased with the loss of annual vegetation and the increase of bare soil surface. The distribution maps of bio-shells in all four seasons of the year were validated during field visits and the year it was found that the highest accuracy of the map related to the map produced from Landsat 8 image is related to summer with 94% total accuracy and Kappa index equal to 0.7412. Interpretation of the spectral profiles of the BSCI index shows that the reflections of the spectra related to the zephyr and strain prepared on the lichen dispersion points are very close to each other and also the spectral profiles of the mid-autumn and early spring are quite consistent. Whereas in the faults, which did not cover the biological crust, the amount of reflection was higher and there was a slight difference between the reflection diagrams of autumn and spring. Although the reflectance values of a range of agricultural lands and the distribution points of biological crusts are very close to each other, the spectral diagrams of all four seasons are very different from each other. But in all seasons of the year and in all places, the least reflection has occurred at the beginning of winter and the most reflection has occurred in summer. The climate of Segzi plain is Mediterranean and precipitation occurs in the cold season of the year. Simultaneously with the increase of precipitation from the middle of autumn, annual plants and mosses at the base of shrubs begin to grow and reach their peak in early winter and again at the beginning of spring. Decreases in rainfall have reduced their density. If the winter spectrum has the least reflection in all places. While in late summer, when the annuals and mosses have dried up, it has had the greatest spectral reflection. Fasaran, which is a barren area and a landfill, it has shown its maximum reflection. Therefore, the BSCI index relative to the percentage of organic matter has a significant error in the detection of biological soil crust, and where the organic matter is high may not provide an accurate diagnosis of soil bioshells. Of course, since the BSCI index is defined for the detection of throat compounds in lichen tissues. The error rate for organic matter is reduced to a minimum. As it has been observed in the final map, there is no cover of biological soil crusts in Fasaran and only soil biological crusts are observed in the areas around Fasaran in the agricultural areas. In agricultural areas, due to human intervention and cultivation, the amount of annual plants is different from the field of natural resources in different seasons of a year have become. Conclusion Spectral similarity of the most important soil surface, including vegetation, the involvement of human factors in increasing or decreasing soil organic matter, bare soil, etc. limits the efficiency of the BSCI index and therefore in the time period of satellite images and regional conditions have a great impact on It has the accuracy of BSCI index.
Alipour H, Hasheminasab sH, Hatefi AH, Gholamnia A, Shahnavaz Y. 2014. Estimation of the potential of wind erosion and deposition using IRIFR method in Miandasht Esfarayen region. Journal of Spatial Analysis Environmental Hazards, 1(2): 77-92. https://jsaeh.khu.ac.ir/article-71-2455-en.html. (In Persian).
Alonso M, Rodríguez-Caballero E, Chamizo S, Escribano P, Cantón Y. 2014. Evaluación de los diferentes índices para cartografiar biocostras a partir de información espectral. Revista española de teledetección: 79-98. doi:https://doi.org/10.4995/raet.2014.2317.
Belnap J. 2006. The potential roles of biological soil crusts in dryland hydrologic cycles. Hydrological Processes: An International Journal, 20(15): 3159-3178. doi:https://doi.org/10.1002/hyp.6325.
Belnap J, Beau JW, Seth MM, Richard AG. 2014. Controls on sediment production in two U.S. deserts. Aeolian Research, 14: 15-24. doi:https://doi.org/10.1016/j.aeolia.2014.03.007.
Chamizo S, Cantón Y, Lázaro R, Solé-Benet A, Domingo F. 2012. Crust Composition and Disturbance Drive Infiltration Through Biological Soil Crusts in Semiarid Ecosystems. Ecosystems, 15(1): 148-161. doi:https://doi.org/10.1007/s10021-011-9499-6.
Chamizo S, Cantón Y, Rodríguez‐Caballero E, Domingo F. 2016. Biocrusts positively affect the soil water balance in semiarid ecosystems. Ecohydrology, 9(7): 1208-1221. doi:https://doi.org/10.1002/eco.1719.
Chen J, Yuan Zhang M, Wang L, Shimazaki H, Tamura M. 2005. A new index for mapping lichen-dominated biological soil crusts in desert areas. Remote Sensing of Environment, 96(2): 165-175. doi:https://doi.org/10.1016/j.rse.2005.02.011.
Esmali A, Ahmadi H, Tahmoures M. 2014. Quantity assessment of water erosion intensity using regional model of erosion and sediment yield (Case study: Nir watershed, Ardebil). Journal of Range and Watershed Managment, 67(3): 407-417. doi:https://doi.org/10.22059/JRWM.2014.52830.
Felde VJMNL, Peth S, Uteau-Puschmann D, Drahorad S, Felix-Henningsen P. 2014. Soil microstructure as an under-explored feature of biological soil crust hydrological properties: case study from the NW Negev Desert. Biodiversity and Conservation, 23(7): 1687-1708. doi:https://doi.org/10.1007/s10531-014-0693-7.
Gong P, Pu R, Biging GS, Larrieu MR. 2003. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1355-1362. doi:https://doi.org/10.1109/TGRS.2003.812910.
Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1): 195-213. doi:https://doi.org/10.1016/S0034-4257(02)00096-2.
Karnieli A. 1997. Development and implementation of spectral crust index over dune sands. International Journal of Remote Sensing, 18(6): 1207-1220. doi:https://doi.org/10.1080/014311697218368.
Kashi Zenouzi L, Ahmadi H, Nazari Samani A. 2016. Using Statistical Hydrogeomorphology Method for Estimating Sediment Yield of Watersheds (Case study: Zonouz Chay and Zilber Chay watersheds). Journal of Watershed Management Research, 6(12): 166-174. http://jwmr.sanru.ac.ir/article-161-567-en.html. (In Persian).
Khodagholi M, Feyzi M, Jaberolansar Z, Shirani K, Alijan V. 2017. Plan for recognizing the ecological regions of the country, plant types of Isfahan province. Research Institute of Forests and Rangelands, Iran, 290 p.
Li Z, Jianmin X, Chaowen C, Lina Z, Zhengyan W, Lichao L, Dongqing C. 2020. Promoting desert biocrust formation using aquatic cyanobacteria with the aid of MOF-based nanocomposite. Science of The Total Environment, 708: 134824. doi:https://doi.org/10.1016/j.scitotenv.2019.134824.
Miralles I, Lázaro R, Sánchez-Marañón M, Soriano M, Ortega R. 2020. Biocrust cover and successional stages influence soil bacterial composition and diversity in semiarid ecosystems. Science of The Total Environment, 709: 134654. doi:https://doi.org/10.1016/j.scitotenv.2019.134654.
Miralles-Mellado I, Cantón Y, Solé-Benet A. 2011. Two‐dimensional porosity of crusted silty soils: Indicators of soil quality in semiarid rangelands? Soil Science Society of America Journal, 75(4): 1330-1342. doi:https://doi.org/10.2136/sssaj2010.0283.
Mojeddifar S, Fereydooni H. 2017. A directed matched filtering algorithm (DMF) for discriminating hydrothermal alteration zones using the ASTER remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 61: 1-13. doi:https://doi.org/10.1016/j.jag.2017.04.010.
Nazari Samani AA, Ehsani AH, Golivari A, Abdolshahnejad M. 2015. Comparing the results of RWEQ and IRIFR models for determining of land management effects on wind erosion. Desert Management, 3(6): 39-53. http://www.jdmal.ir/article_21671.html?lang=en. (In Persian).
Paruelo JM, Piñeiro G, Escribano P, Oyonarte C, Alcaraz D, Cabello J. 2005. Temporal and spatial patterns of ecosystem functioning in protected arid areas in southeastern Spain. Applied Vegetation Science, 8(1): 93-102. doi: https://doi.org/10.1111/j.1654-109X.2005.tb00633.x.
Peñuelas J, Pinol J, Ogaya R, Filella I. 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18(13): 2869-2875. doi:https://doi.org/10.1080/014311697217396.
Rodríguez-Caballero E, Cantón Y, Chamizo S, Lázaro R, Escudero A. 2013. Soil Loss and Runoff in Semiarid Ecosystems: A Complex Interaction Between Biological Soil Crusts, Micro-topography, and Hydrological Drivers. Ecosystems, 16(4): 529-546. doi:10.1007/s10021-012-9626-z.
Rodríguez-Caballero E, Escribano P, Olehowski C, Chamizo S, Hill J, Cantón Y, Weber B. 2017. Transferability of multi- and hyperspectral optical biocrust indices. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 94-107. doi:https://doi.org/10.1016/j.isprsjprs.2017.02.007.
Rouse JW, Haas RH, Schell JA, Deering DW. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351(1974): 309-317. https://ntrs.nasa.gov/citations/19740022614.
Rozenstein O, Karnieli A. 2015. Identification and characterization of Biological Soil Crusts in a sand dune desert environment across Israel–Egypt border using LWIR emittance spectroscopy. Journal of Arid Environments, 112: 75-86. doi:https://doi.org/10.1016/j.jaridenv.2014.01.017.
Thomas A, Dougill A. 2007. Spatial and temporal distribution of cyanobacterial soil crusts in the Kalahari: Implications for soil surface properties. Geomorphology, 85(1): 17-29. doi:https://doi.org/10.1016/j.geomorph.2006.03.029.
Ustin LS, Phillip GV, Shawn CK, Maria JS, Jeff FZ, Stanley DS. 2009. Remote sensing of biological soil crust under simulated climate change manipulations in the Mojave Desert. Remote Sensing of Environment, 113(2): 317-328. doi:https://doi.org/10.1016/j.rse.2008.09.013.
Weber B, Hill J. 2016. Remote sensing of biological soil crusts at different scales. In: Biological soil crusts: an organizing principle in drylands. Springer, pp 215-234. https://doi.org/210.1007/1978-1003-1319-30214-30210_30212.
Weber B, Olehowski C, Knerr T, Hill J, Deutschewitz K, Wessels DCJ, Eitel B, Büdel B. 2008. A new approach for mapping of Biological Soil Crusts in semidesert areas with hyperspectral imagery. Remote Sensing of Environment, 112(5): 2187-2201. doi:https://doi.org/10.1016/j.rse.2007.09.014.
Zhao Y, Qin N, Weber B, Xu M. 2014. Response of biological soil crusts to raindrop erosivity and underlying influences in the hilly Loess Plateau region, China. Biodiversity and Conservation, 23(7): 1669-1686. doi:https://doi.org/10.1007/s10531-014-0680-z.
_||_Alipour H, Hasheminasab sH, Hatefi AH, Gholamnia A, Shahnavaz Y. 2014. Estimation of the potential of wind erosion and deposition using IRIFR method in Miandasht Esfarayen region. Journal of Spatial Analysis Environmental Hazards, 1(2): 77-92. https://jsaeh.khu.ac.ir/article-71-2455-en.html. (In Persian).
Alonso M, Rodríguez-Caballero E, Chamizo S, Escribano P, Cantón Y. 2014. Evaluación de los diferentes índices para cartografiar biocostras a partir de información espectral. Revista española de teledetección: 79-98. doi:https://doi.org/10.4995/raet.2014.2317.
Belnap J. 2006. The potential roles of biological soil crusts in dryland hydrologic cycles. Hydrological Processes: An International Journal, 20(15): 3159-3178. doi:https://doi.org/10.1002/hyp.6325.
Belnap J, Beau JW, Seth MM, Richard AG. 2014. Controls on sediment production in two U.S. deserts. Aeolian Research, 14: 15-24. doi:https://doi.org/10.1016/j.aeolia.2014.03.007.
Chamizo S, Cantón Y, Lázaro R, Solé-Benet A, Domingo F. 2012. Crust Composition and Disturbance Drive Infiltration Through Biological Soil Crusts in Semiarid Ecosystems. Ecosystems, 15(1): 148-161. doi:https://doi.org/10.1007/s10021-011-9499-6.
Chamizo S, Cantón Y, Rodríguez‐Caballero E, Domingo F. 2016. Biocrusts positively affect the soil water balance in semiarid ecosystems. Ecohydrology, 9(7): 1208-1221. doi:https://doi.org/10.1002/eco.1719.
Chen J, Yuan Zhang M, Wang L, Shimazaki H, Tamura M. 2005. A new index for mapping lichen-dominated biological soil crusts in desert areas. Remote Sensing of Environment, 96(2): 165-175. doi:https://doi.org/10.1016/j.rse.2005.02.011.
Esmali A, Ahmadi H, Tahmoures M. 2014. Quantity assessment of water erosion intensity using regional model of erosion and sediment yield (Case study: Nir watershed, Ardebil). Journal of Range and Watershed Managment, 67(3): 407-417. doi:https://doi.org/10.22059/JRWM.2014.52830.
Felde VJMNL, Peth S, Uteau-Puschmann D, Drahorad S, Felix-Henningsen P. 2014. Soil microstructure as an under-explored feature of biological soil crust hydrological properties: case study from the NW Negev Desert. Biodiversity and Conservation, 23(7): 1687-1708. doi:https://doi.org/10.1007/s10531-014-0693-7.
Gong P, Pu R, Biging GS, Larrieu MR. 2003. Estimation of forest leaf area index using vegetation indices derived from Hyperion hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 41(6): 1355-1362. doi:https://doi.org/10.1109/TGRS.2003.812910.
Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG. 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1): 195-213. doi:https://doi.org/10.1016/S0034-4257(02)00096-2.
Karnieli A. 1997. Development and implementation of spectral crust index over dune sands. International Journal of Remote Sensing, 18(6): 1207-1220. doi:https://doi.org/10.1080/014311697218368.
Kashi Zenouzi L, Ahmadi H, Nazari Samani A. 2016. Using Statistical Hydrogeomorphology Method for Estimating Sediment Yield of Watersheds (Case study: Zonouz Chay and Zilber Chay watersheds). Journal of Watershed Management Research, 6(12): 166-174. http://jwmr.sanru.ac.ir/article-161-567-en.html. (In Persian).
Khodagholi M, Feyzi M, Jaberolansar Z, Shirani K, Alijan V. 2017. Plan for recognizing the ecological regions of the country, plant types of Isfahan province. Research Institute of Forests and Rangelands, Iran, 290 p.
Li Z, Jianmin X, Chaowen C, Lina Z, Zhengyan W, Lichao L, Dongqing C. 2020. Promoting desert biocrust formation using aquatic cyanobacteria with the aid of MOF-based nanocomposite. Science of The Total Environment, 708: 134824. doi:https://doi.org/10.1016/j.scitotenv.2019.134824.
Miralles I, Lázaro R, Sánchez-Marañón M, Soriano M, Ortega R. 2020. Biocrust cover and successional stages influence soil bacterial composition and diversity in semiarid ecosystems. Science of The Total Environment, 709: 134654. doi:https://doi.org/10.1016/j.scitotenv.2019.134654.
Miralles-Mellado I, Cantón Y, Solé-Benet A. 2011. Two‐dimensional porosity of crusted silty soils: Indicators of soil quality in semiarid rangelands? Soil Science Society of America Journal, 75(4): 1330-1342. doi:https://doi.org/10.2136/sssaj2010.0283.
Mojeddifar S, Fereydooni H. 2017. A directed matched filtering algorithm (DMF) for discriminating hydrothermal alteration zones using the ASTER remote sensing data. International Journal of Applied Earth Observation and Geoinformation, 61: 1-13. doi:https://doi.org/10.1016/j.jag.2017.04.010.
Nazari Samani AA, Ehsani AH, Golivari A, Abdolshahnejad M. 2015. Comparing the results of RWEQ and IRIFR models for determining of land management effects on wind erosion. Desert Management, 3(6): 39-53. http://www.jdmal.ir/article_21671.html?lang=en. (In Persian).
Paruelo JM, Piñeiro G, Escribano P, Oyonarte C, Alcaraz D, Cabello J. 2005. Temporal and spatial patterns of ecosystem functioning in protected arid areas in southeastern Spain. Applied Vegetation Science, 8(1): 93-102. doi: https://doi.org/10.1111/j.1654-109X.2005.tb00633.x.
Peñuelas J, Pinol J, Ogaya R, Filella I. 1997. Estimation of plant water concentration by the reflectance water index WI (R900/R970). International Journal of Remote Sensing, 18(13): 2869-2875. doi:https://doi.org/10.1080/014311697217396.
Rodríguez-Caballero E, Cantón Y, Chamizo S, Lázaro R, Escudero A. 2013. Soil Loss and Runoff in Semiarid Ecosystems: A Complex Interaction Between Biological Soil Crusts, Micro-topography, and Hydrological Drivers. Ecosystems, 16(4): 529-546. doi:10.1007/s10021-012-9626-z.
Rodríguez-Caballero E, Escribano P, Olehowski C, Chamizo S, Hill J, Cantón Y, Weber B. 2017. Transferability of multi- and hyperspectral optical biocrust indices. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 94-107. doi:https://doi.org/10.1016/j.isprsjprs.2017.02.007.
Rouse JW, Haas RH, Schell JA, Deering DW. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351(1974): 309-317. https://ntrs.nasa.gov/citations/19740022614.
Rozenstein O, Karnieli A. 2015. Identification and characterization of Biological Soil Crusts in a sand dune desert environment across Israel–Egypt border using LWIR emittance spectroscopy. Journal of Arid Environments, 112: 75-86. doi:https://doi.org/10.1016/j.jaridenv.2014.01.017.
Thomas A, Dougill A. 2007. Spatial and temporal distribution of cyanobacterial soil crusts in the Kalahari: Implications for soil surface properties. Geomorphology, 85(1): 17-29. doi:https://doi.org/10.1016/j.geomorph.2006.03.029.
Ustin LS, Phillip GV, Shawn CK, Maria JS, Jeff FZ, Stanley DS. 2009. Remote sensing of biological soil crust under simulated climate change manipulations in the Mojave Desert. Remote Sensing of Environment, 113(2): 317-328. doi:https://doi.org/10.1016/j.rse.2008.09.013.
Weber B, Hill J. 2016. Remote sensing of biological soil crusts at different scales. In: Biological soil crusts: an organizing principle in drylands. Springer, pp 215-234. https://doi.org/210.1007/1978-1003-1319-30214-30210_30212.
Weber B, Olehowski C, Knerr T, Hill J, Deutschewitz K, Wessels DCJ, Eitel B, Büdel B. 2008. A new approach for mapping of Biological Soil Crusts in semidesert areas with hyperspectral imagery. Remote Sensing of Environment, 112(5): 2187-2201. doi:https://doi.org/10.1016/j.rse.2007.09.014.
Zhao Y, Qin N, Weber B, Xu M. 2014. Response of biological soil crusts to raindrop erosivity and underlying influences in the hilly Loess Plateau region, China. Biodiversity and Conservation, 23(7): 1669-1686. doi:https://doi.org/10.1007/s10531-014-0680-z.