Long-term vegetation changes in semi-arid regions using remote sensing and spatial statistics functions in order to prepare a road map

Subject Areas : Agriculture, rangeland, watershed and forestry

Mohamad Mahdi Ali Molaee ¹ , marzeyh Rezai ^{2 *} , Rasool Mahdavi ³ , Hamid Gholami ⁴ , Mohamad Kazemi ⁵

1 - Emdad committee
2 - Assistant Professor of Natural Resources Engineering, University of Hormozgan, Bandar Abbas, Iran
3 - Associate Professor of Natural Resources Engineering Group
4 - Department of natural resources engineering, University of Hormozgan
5 - Hormoz Research Center, University of Hormozgan

Received: 2022-09-03 Accepted : 2023-01-18 Published : 2025-07-22

Keywords: Fars, remote sensing, statistical functions, Vegetation,

Abstract :

Currently, Remote sensing is a useful technology that can be used to extract different layers such as soil, rainfall, vegetation and so on. The ultimate goal in most remote sensing analysis operations that are used to investigate different factors of vegetation cover is that the data of various spectral bands that can represent data such as percentage of vegetation cover, biomass and leaf area index into a single value in each pixel. Reduce. Over time, environmental and human factors have caused positive and negative changes in the quantity and quality of vegetation; This situation will continue in the future. Temporal changes in vegetation may be in the form of increasing or decreasing trends. Recognizing these changes and determining their trends in the past and future can open the way for decision-making for the image of the land. One of the ways to study vegetation changes as the most important indicator of land degradation is remote sensing. Based on this, in this research, using the NDVI normalized vegetation difference index in HDF format and MODIS sensor with a pixel size of 250 meters in a 16-day period, monitoring the long-term changes in the vegetation cover of Fars province during a 20-year period from 2000 to 2020 and was investigated using spatial statistics functions in order to prepare a road map in the year 1400. For this purpose, using spatial statistics functions to prepare a road map. For this purpose, MADIS data has been used for a period of twenty years. Then the data were analyzed by classical statistics and spatial statistics. The results show the increasing trend of the vegetation level in a period of 20 years, and the distribution of the vegetation over time was clustered. At the end, a roadmap for long-term vegetation monitoring in Fars province was proposed.

References:


1. Abdolalizadeh, Z., Ebrahimi, A. & Mostafazadeh, R. 2019. Landscape pattern change in Marakan protected area, Iran, Regional Environmental Change, 19 :1683–1699.
2. Abdolalizadeh, Z., Ghorbani, Z. Mostafazadeh, R. & Moameri, M. 2020. Rangeland canopy cover estimation using Landsat OLI data and vegetation indices in Sabalan rangelands, Iran, Arabian Journal of Geosciences, 13(245): 1-16.
3. Arkhi, P. 2014. Detecting land use changes by object-oriented processing of satellite images using software (Idrisi), case study: Abdanan region, Sepehr Geographical Information Quarterly, 24 (95): 51-62. (In persion)
4. Bagan H, Yang Y, Ma J. 2005. Land cover classification from MODIS EVI times-series data using SOM neural network. Int J Re-mote Sens 26:4999–5012.
5. Bai, Y. 2021. Analysis of vegetation dynamics in the Qinling-Daba Mountains region from MODIS time series data. Ecological Indicators, 129, 108029.
6. Ekrami, M., Mahdavi, R., Rezaei, M. Waqarfard, H., Barkhori, J. 2021. Calculating water requirements and providing a model of horticultural cultivation that requires less water and is compatible with dry areas, in order to reduce the vulnerability of agriculture to drought in Pishkoh watershed, Yazd province). Journal of Irrigation and Water Engineering. 12(46) 1-18. (in persion).
7. Ekrami, M., Mahdavi, R., Rezai, M., Vagharfard, H., Barkhori, J., 2021. Zoning of rangeland drought vulnerability in arid and semi-arid regions Case study: (Pishkuh watershed of Yazd province), Iranian Journal of Range and Desert Research, 27(3): 577-595. (in persion).
8. Fan, Q., Ding, S. 2017. Landscape Pattern Changes at a County Scale: A Case Study in Fengqiu, Henan Province, China. CATENA, 137 (1): 152-160.
9. Fouladi, M., Mahdavi Najafabadi, R. and M., Rezai, M., 2020. Development of management strategies for protection and rehabilitation of Jazmourian wetland using VIKOR multi-criteria decision model, Geographical explorations of desert areas, 8 (2):107-135.
10. Geng L, Che T, Wang X, Wang H. 2019. Detecting Spatiotemporal Changes in Vegetation with the BFAST Model in the Qilian Mountain Region during 2000-2017. Remote Sensing, 11(2): 103.
11. James T. 2020. Changes in Land Use Land Cover (LULC), Surface Water Quality and Modelling Surface Discharge in Beaver Creek Watershed, Northeast Tennessee and Southwest Virginia, thesis, East Tennessee State University.
12. Jabari S, Khajehuddin J, Jafari R, Soltani S. 2017. Investigating changes in the vegetation percentage of pastures using satellite images in the Semiram region of Isfahan, Applied Ecology, 3(10): 27- 38. (In persion)
13. Karimi M, Oladi Tarikaei Mohammadi J. 2017. Investigating changes in vegetation cover in internal and external foci of fine dust using MODIS satellite images (case study of Kermanshah province). Ecology of Iran's forests, 6(12): 39-49. (In persion)
14. Khodaqoli, M., Fayaz, M., Shirani, K., Faizi, M. T., Yazdani, M. 2016. Plan to recognize the ecological regions of the country: plant types of Fars province, Forestry and Pasture Research Institute. 189 p. (In persion)
15. Kiapasha K, Darvishsefat, A. A. Zargham N, Attarod P, Nadi M, Schaepman M. 2017. Greening trend in the Hyrcanian forests using NOAA NADVI time series during 1981-2012. Forest and Wood Products, 70 (3): 409-420.
16. Kim D, Baik J, Umair M, Choi M. 2021. Water use efficiency in terrestrial ecosystem over Asia: Effects of climate regimes and land cover types. Science of The Total Environment, 773, 145519.
17. Kowe P. Mutanga, O., Timothy Dubec A. 2020. A quantitative framework for analyzing long term spatial clustering and vegetation fragmentation in an urban landscape using multi-temporal Landsat data. International Journal of Applied Earth Observation and Geoinformation. 88. 102057.
18. Li, Z., Wu, Y., Li, J., Zhang, S. B., Zu, J., Paudel, B. 2019. Increasing sensitivity of alpine grasslands to climate variability along an elevational gradient on the Qinghai-Tibet Plateau. Science of the Total Environment, 678, 21-29.
19. Masoud, A., Kike, A. 2006. Arid land salinization detected by remotely –sensed land cover changes, A case study in the Siwa region. Egypt. Journal of Arid Environments, 66,151-167.
20. McGarigal, K., Compton, B.W., Plunkett, E.B., DeLuca, W. V., Grand, J., Ene, E., Jackson, S. D. 2018. A landscape index of ecological integrity to inform landscape conservation. Landscape Ecology, 33(1) 1029–1048.
21. Mirghasemi, S. H.; Banjad, B; Farid Hosseini, A. F., 2021. Application of remote sensing in hydraulic modeling and determination of riverbed limits (case study: Ardak River), Journal of Remote Sensing and Geographical Information System in Natural Resources 14 (1), 9-12.
22. Moussaid, A., Fkihi, S., Zennayi, Y. Lahlou, O. Kassou, I. Bourzeix, F., Mansouri, L. and Imani, Y. 2022. Article Machine Learning Applied to Tree Crop Yield Prediction Using Field Data and Satellite Imagery: A Case Study in a Citrus Orchard, Land. 9 (80).
23. Mo, K., Chen, Q., Chen, C., Zhang, J., Wang, L., Bao, Z. 2019. Spatiotemporal variation of correlation between vegetation cover and precipitation in an arid mountain-oasis river basin in northwest China. Journal of Hydrology. 574, 138-147.
24. Mostafazadeh, R., Moradzadeh, V., Alaei, N. and Hizbavi, Z., 2021. Application of Hurst index in determining the long memory model of rainfall and discharge time series of selected stations of Ardabil, 11(2): 113-131. (in persion)
25. Olafsson, H., & Rousta, I., 2021. Influence of atmospheric patterns and North Atlantic Oscillation (NAO) on vegetation dynamics in Iceland using Remote Sensing, 351-363.
26. Olafsson, H., Rousta, I. 2022. Remote sensing analysis to map inter-regional spatio-temporal variations of the vegetation in Iceland during 2001–2018. Acta Geographica slovenica, 62(1), 1-17.
27. Omidvar, K., Naraki-Gord M, Abbasi, H. 2014. Revealing changes in land use and vegetation in Yasouj using remote sensing. Quarterly Journal of Geography and Urban-Regional Studies, 1(16): 111-126. (In persion)
28. Pourkhbaz, H., Mohammadyari F., Tavakoli, M., Afder, H. 2015. Preparation of vegetation map and its changes system using remote sensing techniques and geographic information (case study: Behbahan city), Geographical Information Scientific-Research Quarterly, 23(92): 2-34. (In persion)
29. Pineda, M., Matilde, B., Maria-Luisa, P. 2021. Review Thermal Imaging for Plant Stress Detection and Phenotyping, Remote sensing, 13(68).
30. Rajaei, F., Ismaili Salari, A., Salman Mahini, A., Dalour, M., Masah Bhavani, A. R. 2019. Evaluation of land use changes in Tajen River watershed based on topography measurements, Environmental Science and Technology, 22:351-366.
31. Rezai, M., Javizadeh, S. and Kaviani, E., 2020. Applied spatial statistics, University of Hormozgan, 189 p. (In persion)
32. Rezai, M. 2020. Spatial distribution of the use of scientific recreation with emphasis on the model of determining the corridor of wildlife of poisonous pairs and terrestrial mammals in desert protected areas, Geographical explorations of desert areas, 9(1): 1-19. (In persion)
33. Rezai, M., Zerehi, F. 2022. Spatial distribution and stability of accumulated sediments around Salvadora persica L. and Alhaji camelorum L. and modeling of prediction of its change, Desert Management, 9 (4): 39-52. (In persion)
34. Rousta, I.,Olafsson, H., Moniruzzaman, Md., Ardö, J., Zhang, H., Darlington Mushore, T., Shahin, Sh. And Azim, S., 2020. The 2000–2017 drought risk assessment of the western and southwestern basins in Iran, Modeling Earth Systems and Environment, 6, 1201–1221.
35. Rousta, I., Olafsson, H., Nasserzadeh, M. H., Zhang, H. and Krzyszczak, J., Piotr Baranowski, P., 2021. Dynamics of Daytime Land Surface Temperature (LST) Variabilities in the Middle East Countries During 2001–2018. Pure and Applied Geophysics, 178, 2357–2377.
36. Rousta, I., Olafsson, H., Zhang, H., Moniruzzaman, Md. Baranowski, P., and Krzyszczak, J., 2022. Anthropogenic Factors Affecting the Vegetation Dynamics in the Arid Middle East, 26 (1): 774 – 805.
37. Rousta, I. Moniruzzaman, Md., Olafsson, H. and Zhang, H. 2022. Investigation of the Vegetation Coverage Dynamics and its Relation to Atmospheric Patterns in Kabul River Basin in Afghanistan, Pure and Applied Geophysics, 179(4) 1-14.
38. Rousta, I., Mansourmoghaddam, M., Olafsson, H., Krzyszczak, J., Baranowski, P., Zhang, H. and Tkaczyk, P. 2022. Analysis of the recent trends in vegetation dynamics and its relationship with climatological factors using remote sensing data for Caspian Sea watersheds in Iran Int. Agrophys, 36(3): 139–153.
39. Shimizu, K., Ota, T., Onda, N., Mizoue, N., 2022. Combining post-disturbance land cover and tree canopy cover from Landsat time series data for mapping deforestation, forest degradation, and recovery across Cambodia, International Journal of Digital Earth. 15(1):832-852.
40. Talebi Khiavi, H., Mostafazadeh, R., 2021. Land use change dynamics assessment in the Khiavchai region, the hillside of Sabalan mountainous area. Arabian Journal of Geosciences, 14 (2257), 1683-1699.
41. Tonny, J. O., Florence, M. M. 2018. Spatial Analysis, Visualization, and Computational Methods. CRC Press.
42. Vakil al-Raaya, M. A.; Melmasi, S.; Mozhgan Zaimdar, M. and Mirza Ebrahim-Tahrani, M., 2021. Fire risk zoning in urban areas using the logistic regression method (Case study: Kashan city), Journal of Remote Sensing and Geographical Information System in Natural Resources, 13 (4) 10-13. (In persion)
43. Verbesselt, J., Hyndman, R., Newnham, G., Culvenor, D. 2010. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1): 106-115.
44. Vianas, O., Baulies, X. 2004. 1:250000 Land use map Landsat TM data, int. Journal of Remote sensing, 16)1(,129-146.
45. Vijitharan, Sh., Sasaki, N. Venkatappa, M., Tripathi, N. K., Abe, I., and Takuji, W. 2022. Article Assessment of Forest Cover Changes in Vavuniya District, Sri Lanka: Implications for the Establishment of Subnational Forest Reference Emission Level. Land .11, 1061.
46. Wellens J. 1993. Monitoring and modeling rangeland vegetation in Tunisia using satellite and meteorological data. PhD thesis, University of Reading.
47. Yamani, M., Mazidi, A. 2014. Investigating surface changes and desert vegetation from remote sensing data. Journal of Geographical Research, 40(64),1-12. (In persion)
48. Yousefi Rubiat, E., Sayadi, M. H., Chamanepour, H., 2021. Evaluation of national standards for locating industrial waste landfills using geographic information system, Journal of Remote Sensing and Geographical Information System in Natural Resources, 13(2), 41-61. (In persion)
49. Zerehi, F., Rezai, M. 2022. Changes in sand dune expansion and wind surface cover in deserts adjacent to wetland ecosystems, Journal of Environmental Erosion Research, 12 (1), 95- 112. (In persion)