Investigating Changes in Oak Forest Canopy and Its Relationship with Soil Moisture

Document Type : Scientific Letters

Authors

1 1- Assistant professor and faculty member of the Mapping Engineering Department, Technical and Engineering Faculty, Golestan University, Iran.

2 PhD student, Faculty of Natural Resources, Malayer University, Iran.

3 3- Assistant professor and faculty member, Niro Research Institute, Tehran, Iran

10.22092/irn.2025.366881.1602

Abstract

Soil surface moisture is a crucial factor in determining the extent of forest canopy cover and is influenced by land surface temperature and other factors like solar radiation and slope aspect. This study utilized remote sensing technology to quantify soil moisture and investigate its correlation with forest canopy cover. To this end, the TOTRAM soil moisture model and the FCD vegetation cover model were developed in the Sepiddasht forest area of Lorestan Province, Iran, employing Landsat 8 and 9 imagery acquired as a time series in 2015 and 2023. The FCD model incorporated four vegetation, soil, shadow, and thermal indices, along with the advanced shadow index and co-registered shadow index, utilizing appropriate thresholds. The results demonstrated a strong Pearson correlation coefficient between soil moisture (dependent variable) and temperature, vegetation cover, and forest density: 0.709, 0.813, and 0.691 in 2015, and 0.816, 0.875, and 0.702 in 2023, respectively. These findings indicate a significant relationship between soil moisture patterns and changes in vegetation cover. A strong correlation was observed between LST-NDVI and soil moisture. For example, a decrease in greenness, as evidenced by a decline in NDVI from 0.24 in 2015 to 0.19 in 2023, coupled with an increase in temperature from 29.95 to 33.62 degrees Celsius, resulted in a decrease in average soil moisture from 52% to 43%. Analysis of the results obtained from the TATRAMT model further supports the conclusion that soil moisture is highly dependent on variations in temperature and canopy cover.

Keywords

References

 
Ahmadi sani, N., Darvishsefat, A., Zobieri, M. and Farzaneh, A., 2007.  Potentiality of aster images for forest density mapping in zagros forest of Iran (case study: Marivan forests). Iranian Journal of Natural Resources, 61 (3):603-614.
Ambrosone, M., Matese, A., Di Gennaro, S. F., Gioli, B., Tudoroiu, M., Genesio, L., Miglietta, F., Baronti, S., Maienza, A., Ungaro, F., and Toscano, P., 2020. Retrieving soil moisture in rainfed and irrigated fields using Sentinel-2 observations and a modified OPTRAM approach. International Journal of Applied Earth Observation and Geoinformation, 89: 102113. https:// doi.org/10.1016/j.jag.2020.102113
Bhandari, S. K. and Nandy, S., 2023. Forest Aboveground Biomass Prediction by Integrating Terrestrial Laser Scanning Data, Landsat 8 OLI-Derived Forest Canopy Density and Spectral Indices. Journal of the Indian Society of Remote Sensing. https://doi.org/10.1007/s12524-023-01687-z
Das, B., Rathore, P., Roy, D., Chakraborty, D., Bhattacharya, B. K., Mandal, D., Jatav, R., Sethi, D., Mukherjee, J., Sehgal, V. K., Singh, A. K., and Kumar, P., 2023. Ensemble surface soil moisture estimates at farm-scale combining satellite-based optical-thermal-microwave remote sensing observations. Agricultural and Forest Meteorology, 339, 109567. https://doi.org/10.1016/j.agrformet.2023.109567
Falensky, M. A., Sulti, A. L., Putra, R. D. and Marko, K., 2020. Application of Forest Canopy Density (FCD) Model for the Hotspot Monitoring of Crown Fire in Tebo, Jambi Province. Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments), 4 (1): 59-67. https://doi.org/10.7454/jglitrop.v4i1.76
Hanson, P., Amthor, J., Wullschleger, S., Wilson, K. B., Grant, R. F., Hartley, A., Hui, D., Hunt, E. R., Johnson, D. W., Kimball, J., King, A., Luo, Y., McNulty, S., Sun, G., Thornton, P. E., Wang, S., Williams, M., Baldocchi, D. and Cushman, R. M., 2004. Oak forest carbon and water simulations: Model intercomparisons and evaluations against independent data. Ecological Monographs, 74: 443-489. https://doi.org/10.1890/03-4049
Li, B. B., Li, P. P., Zhang, W.-T., Ji, J. Y., Liu, G. B. and Xu, M. X., 2021. Deep soil moisture limits the sustainable vegetation restoration in arid and semi-arid Loess Plateau. Geoderma, 399: 115122. https://doi.org/10.1016/j.geoderma.2021.115122
Niu, X., Chen, Z., Pang, Y., Liu, X., and Liu, S., 2022. Soil moisture shapes the environmental control mechanism on canopy conductance in a natural oak forest. The Science of the total environment, 857: 159363. https://doi.org/10.1016/j.scitotenv.2022.159363
Sadeghi, M., Babaeian, E., Tuller, M., and Jones, S., 2017. The optical trapezoid model: A novel approach to remote sensing of soil moisture applied to Sentinel-2 and Landsat-8 observations. Remote Sensing of Environment, 198: 52-68. https://doi.org/10.1016/j.rse.2017.05.041
Surendran, U., Kumar, V., Ramasubramoniam, S. and Raja, P., 2017. Development of Drought Indices for Semi-Arid Region Using Drought Indices Calculator (DrinC) – A Case Study from Madurai District, a Semi- Arid Region in India. Water Resources Management, 31(11): 3593-3605. https:// https://doi.org/10.1007/s11269-017-1687-5
Vicente-Serrano, S., Beguería, S., and López-Moreno, J. I., 2010. A Multiscalar Drought Index Sensitive to Global Warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate, 23: 1696-1718. https://doi.org/10.1175/2009JCLI2909.1
Yu, S., Guo, J., Liu, Z., Wang, Y., Ma, J., Li, J., and Liu, F., 2021. Assessing the Impact of Soil Moisture on Canopy Transpiration Using a Modified Jarvis-Stewart Model. Water, 13(19), 2720. https://doi.org/10.3390/w13192720
Zhu, P., Zhang, G., Wang, H., Zhang, B., and Liu, Y., 2021. Soil moisture variations in response to precipitation properties and plant communities on steep gully slope on the Loess Plateau. Agricultural Water Management, 256: 107086. https://doi.org/10.1016/j.agwat.2021.107086
 
 
Iran Nature
Volume 9, Issue 5 - Serial Number 48
November and December 2024
Pages 51-63

Files

Share

How to cite

Statistics