Desert Ecosystem Engineering

Desert Ecosystem Engineering

Evaluating the Effects of Vegetation Types on Surface Temperature and Surface Moisture Using Satellite-Based Indices

Document Type : Original Article

Authors
Mohammad Hossein Mokhtari 1
Setare Moazam 2
Asghar Mosleh Arany 3
Hamidreza Azimzadeh 4
Gholamhosein Moradi 1
1 Assisstant professor Faculty of Natural resources Yazd University, Yazd, Iran
2 Msc Student Faculty of Natural resources Yazd University, Yazd, Iran
3 Associate professor Faculty of Natural resources Yazd University, Yazd, Iran
4 Environmental Sciences Department, Natural Resources and Desert Studies Faculty, Yazd University
Abstract
As evaluating the influence of vegetation types on soil surface moisture (SSM) and soil surface temperature (LST) is crucially important, especially in semi-arid regions, this study set out to do so using remote sensing data. To this end, the vegetation types together with their extent of coverage in the study area were determined via the physiognomic-floristic method. In this regard, taking the results of field observations into account, eight types of vegetation were recognized and the zonation was performed.
Moreover, LST and SSM were calculated using the data collected from Landsat 8 on May 24 and July 27, 2017, followed by the evaluation of SST and SSM measurements by comparing them to the measurements made based on field studies (R2>0.6). Then, at each vegetation zone, the influence of vegetation types on the SSM and the SST was statistically analyzed.
Duncan's statistical test showed significant differences between the mean temperature (α=0.05) in all vegetation types, except for the Artemisia aucheri-Pistacia atlantica and Artemisia sieberi–Ebenus stellata-Cousinia desertii in the first period of the study and the Cynodon dactylon and Amygdalus scoparia-Pistacia atlantica in the second period of the investigation.
However, SSM differences were found to be insignificant between Artemisia sieberi–Amygdalus scoparia and Amygdalus scoparia vegetation types in the first period of the study, and between Amygdalus scoparia and Cynodon dactylon vegetation types in the second period of the study. On the other hand, most vegetation types exerted a considerably varying influence on SST and SSM. Nonetheless, in both study periods, the temperature and moisture variations did not follow the same patterns in different vegetation types.
Keywords
Vegetation Type
Soil Surface Temperature
Soil Surface Moisture
Natural forest
Remote sensing
  1. Aggarwal PK, Kalra N, Chander S, Pathak H (2006) InfoCrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description Agricultural Systems 89:1-25 doi:https://doi.org/10.1016/j.agsy.2005.08.001
  2. Blanco-Canqui H, Lal R (2008) Soil Erosion Under Forests. In: Principles of Soil Conservation and Management. Springer Netherlands, Dordrecht, pp 321-344. doi:10.1007/978-1-4020-8709-7_12
  3. Certini G. Effects of fire on properties of forest soils: a review. Oecologia. (2005) Mar;143(1):1-10. doi: 10.1007/s00442-004-1788-8.
  4. Cochran WG (2007) Sampling Techniques, 3Rd Edition. Wiley India Pvt. Limited,
  5. Donglian S, Pinker RT (2004) Case study of soil moisture effect on land surface temperature retrieval IEEE Geoscience and Remote Sensing Letters 1:127-130 doi:10.1109/LGRS.2004.824749
  6. Doran JW, Smith MS (1987) Organic matter management and utilization of soil and fertilizer nutrients. SSSA Special Publication, vol 19. Soil Science Society of America and American Society of Agronomy, Madison, WI. doi:10.2136/sssaspecpub19.frontmatter
  7. Florides GA, Kalogirou SA, Florides GA (2005) Annual ground temperature measurements at various depths.
  8. Gilbertson DD, Kent M, Pyatt FB (1985) Describing vegetation in the field — physiognomic methods. In: Practical Ecology for Geography and Biology: Survey, mapping and data analysis. Springer US, Boston, MA, pp 59-74. doi:10.1007/978-1-4684-1415-8_4
  9. Goward SN, Xue Y, Czajkowski KP (2002) Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model Remote Sensing of Environment,79:225-242 doi:https://doi.org/10.1016/S0034-4257(01)00275-9
  10. Green AA (1985) Analysis of aircraft spectrometer data with logarithmic residuals Proc Airborne Imaging Spectrometer Data Analysis Workshop 85:111-119
  11. Guo X, Price KP, Stiles JM (2000) Biophysical and Spectral Characteristics of Cool- and Warm-Season Grasslands under Three Land Management Practices in Eastern Kansas Natural Resources Research 9:321-331 doi:10.1023/A:1011513527965
  12. He Y, Yan H, Ma L, Zhang L, Qiu L, Yang S (2020) Spatiotemporal dynamics of the vegetation in Ningxia, China using MODIS imagery Frontiers of Earth Science 14:221-235 doi:10.1007/s11707-019-0767-7
  13. Hillel D (2005) THERMAL PROPERTIES AND PROCESSES. In: Hillel D (ed) Encyclopedia of Soils in the Environment. Elsevier, Oxford, pp 156-163. doi:https://doi.org/10.1016/B0-12-348530-4/00523-3
  14. Jiménez-Muñoz JC, Sobrino JA, Skoković D, Mattar C, Cristóbal J (2014) Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data IEEE Geoscience and Remote Sensing Letters 11:1840-1843 doi:10.1109/LGRS.2014.2312032
  15. Maiti SK, Kumar A (2016) Chapter 2 - Energy Plantations, Medicinal and Aromatic Plants on Contaminated Soil. In: Prasad MNV (ed) Bioremediation and Bioeconomy. Elsevier, pp 29-47. doi:https://doi.org/10.1016/B978-0-12-802830-8.00002-2
  16. Mallick K, Bhattacharya BK, Patel NK (2009) Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI Agricultural and Forest Meteorology 149:1327-1342. doi:https://doi.org/10.1016/j.agrformet.2009.03.004
  17. Markham BL, Barker JL (1986) Landsat MSS and TM Post-Calibration Dynamic Rangers, Exoatmospheric Reflectance and At-Satellite Temperatures. EOSAT Landsat Tech. Notes, 3-8.
  18. McInnes KJ (2002) Soil heat. In: Topp JHDaGC (ed) Methods of soil analysis. Madison, WI, pp 1183–1199.
  19. McIvor A, Spencer T, Spalding M, Lacambra C, Möller I (2015) Chapter 14 - Mangroves, Tropical Cyclones, and Coastal Hazard Risk Reduction. In: Shroder JF, Ellis JT, Sherman DJ (eds) Coastal and Marine Hazards, Risks, and Disasters. Elsevier, Boston, pp 403-429. doi:https://doi.org/10.1016/B978-0-12-396483-0.00014-5
  20. Onwuka B (2018) Effects of Soil Temperature on Some Soil Properties and Plant Growth Advances in Plants & Agriculture Research 8 doi:10.15406/apar.2018.08.00288
  21. Pablos M, Martínez-Fernández J, Piles M, Sánchez N, Vall-llossera M, Camps A (2016) Multi-Temporal Evaluation of Soil Moisture and Land Surface Temperature Dynamics Using in Situ and Satellite Observations Remote Sensing 8:587
  22. Pradhan NR, Downer CW, Marchenko S (2019) Catchment Hydrological Modeling with Soil Thermal Dynamics during Seasonal Freeze-Thaw Cycles Water 11:116
  23. Sabri NSA, Zakaria Z, Mohamad SE, Jaafar AB, Hara H (2018) Importance of Soil Temperature for the Growth of Temperate Crops under a Tropical Climate and Functional Role of Soil Microbial Diversity Microbes Environ 33:144-150 doi:10.1264/jsme2.ME17181
  24. Sandholt I, Rasmussen K, Andersen J (2002) A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status Remote Sensing of Environment 79:213-224 doi:https://doi.org/10.1016/S0034-4257(01)00274-7
  25. Sriboon W, Tuntiwaranuruk U, Sanoamuang N (2017) Hourly soil temperature and moisture content variations within a concrete pipe container for planting lime trees in Eastern Thailand Case Studies in Thermal Engineering 10:192-198. doi:https://doi.org/10.1016/j.csite.2017.06.005
  26. Tasumi M, Trezza R, Allen RG, Wright JL (2005) Operational aspects of satellite-based energy balance models for irrigated crops in the semi-arid U.S Irrigation and Drainage Systems 19:355-376 doi:10.1007/s10795-005-8138-9
  27. Tracey JG (1969) Edaphic Differentiation of some Forest Types in Eastern Australia: I. Soil Physical Factors Journal of Ecology 57:805-816 doi:10.2307/2258501
  28. Vanhellemont Q (2020) Combined land surface emissivity and temperature estimation from Landsat 8 OLI and TIRS ISPRS Journal of Photogrammetry and Remote Sensing 166:390-402, doi:https://doi.org/10.1016/j.isprsjprs.2020.06.007
  29. Wan Z, Wang P, Li X (2004) Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA International Journal of Remote Sensing 25:61-72 doi:10.1080/0143116031000115328
  30. Waring RH, Running SW (2007) CHAPTER 7 - Spatial Scaling Methods for Landscape and Regional Ecosystem Analysis. In: Waring RH, Running SW (eds) Forest Ecosystems (Third Edition). Academic Press, San Diego, pp 225-V. doi:https://doi.org/10.1016/B978-012370605-8.50014-1
  31. Wu Z, Wang M, Zhang H, Du Z (2019) Vegetation and soil wind erosion dynamics of sandstorm control programs in the agro-pastoral transitional zone of northern China Frontiers of Earth Science 13:430-443 doi:10.1007/s11707-018-0715-y
  32. Xiong Y, Peng F, Zou B (2019) Spatiotemporal influences of land use/cover changes on the heat island effect in rapid urbanization area Frontiers of Earth Science 13:614-627 doi:10.1007/s11707-018-0747-3
  33. Yolcubal I, Brusseau ML, Artiola JF, Wierenga P, Wilson LG (2004) 12 - ENVIRONMENTAL PHYSICAL PROPERTIES AND PROCESSES. In: Artiola JF, Pepper IL, Brusseau ML (eds) Environmental Monitoring and Characterization. Academic Press, Burlington, pp 207-239. doi:https://doi.org/10.1016/B978-012064477-3/50014-X

 

 

Desert Ecosystem Engineering
Volume 10, Issue 8 in English
June 2023
Pages 14-1