Showing 33 results for Forest
Y. Kooch, S. M. Hosseini, J. Mohammadi, S. M. Hojjati,
Volume 16, Issue 60 (7-2012)
Abstract
To investigate spatial variability of soil characteristics in the most valuble forest stands in the northern Iran using geostatistical approach, a twenty hectare area in Experimental Forest Station of Tarbiat Modares University was considered. Soil samples were taken from pits, mounds, canopy gaps, under single trees and closed canopy positions at 0 - 15, 15 - 30 and 30 - 45 cm depths using core soil sampler (81cm2 cross section). pH and organic matter were measured in the laboratory, and then carbons to nitrogen ratio and carbon sequestration were calculated. Spatial variability for soil characteristics revealed anisotropic variogram due to the same variability of surface variogram in different depths. pH in all the depths and carbon sequestration in 30 - 45cm depth showed exponential model whereas the other soil characteristics resulted in a linear model in different layers. The results of spatial structure showed pH with medium structure in all the depths, organic matter and carbon to nitrogen ratio with pure nugget effect (non structure), carbon sequestration in 0 - 30cm depth with weak structure and 30 - 45cm depth with medium spatial structure.
M. Ajami, F. Khormali,
Volume 16, Issue 61 (10-2012)
Abstract
In order to study land degradation from the soil genesis and micromorphological perspective, ten soil profiles were dug and described on five slope positions in both forest and deforested cultivated land. The soil samples were taken from all horizons for physico-chemical analysis and micromorphological studies. Forest soils had a well developed argillic and calcic horizons and also mollic epipedon. These soils were classified as Alfisols and Mollisols. Carbonate leaching into the depth of soil profile and translocation of clays to lower layers and formation of developed soils are by no means related closely with the dense forest cover and its subsequent landscape stability and favorable leaching conditions. Dominant soil orders in this area were Inceptisols and Mollisols. In cultivated landuse, no argillic horizon was observed except in toeslope position. Absence of argillic horizon or its elimination following deforestation is one of the most important and obvious pedogenetic evidences of land degradation after land use change. Outcropping of high carbonatic layers (calcic horizon), disappearance of mollic and formation of ochric epipedon, presence of redoximorphic features attributed to runoff in lowland, decrease of solum thickness, and change of soil color were the other pedogenic indicators for land degradation in the study area. Microscopic observations showed that granular and crumb microstructures with high porosity were converted to massive and compact ones with low porosity in the deforested area. Disappearance of clay coatings besides absence of excremental pedofeatures were the other important micromorphological evidences of erosion and land degradation.
S. Ezzati , A. Najafi,
Volume 16, Issue 61 (10-2012)
Abstract
Increase in soil bulk density and reduces in porosity and infiltration rate are the most common disturbances during timber harvesting and ground-base skidding. The present study was conducted to study soil physical properties e.g., soil bulk density, moisture, porosity and soil hydrological properties e.g., soil infiltration from ground-base skidding in twenty years since logging. After initial survey, four abandonment skid trails were selected with similar pedologic, climatic conditions and physiographic and different age in Necka-Zalemrod catchment in east of Mazandaran province. Results indicated that impacts of machinery traffices in high traffic intensity have remained yet 20 years since logging. Means soil bulk density, porosity and void ratio were 42.4 greater, 24.6 and 46.7 percent, respectively, lower than the undisturbed areas. The least infiltration rate was recorded in 1-5 years old skid trail, so the reduction of water was not considerable within 18 minutes after experiment into soil in inner ring in this skid trail. Results in each skid trail firmed that in low traffic intensity soil physical and hydrologic properties was tent to “normal” recover in compare to the undisturbed areas.
Gh. Vahabzadeh, A. Safari, M.h. Farhoudi, H.r. Abdollahi, H. Fathizad, Gh.r. Khosravi,
Volume 18, Issue 70 (3-2015)
Abstract
In this research, sediment production and delivery amount by Darabkola forest roads was estimated using the SEDMODL model. To evaluate the model results, the sedimentation rate in the above roads was directly measured using rainfall simulator. Also, the paired t-test, BIAS, RE and RMSE were used to assess the results. The analysis showed that the rate of sediment production from study roads' surface using the SEDMODL model and direct measurement under the rainfall simulation were 420.97 and 341.19 tons per year, respectively, and rate of sediment delivered to the stream with sediment delivery ratios of 42% and 51%, respectively, was about 177.58 and 174.02 tons per year. Also, results of the statistical methods of BIAS, RE and RMSE for the aforesaid model were 0.04, 17.59 and 0.71, respectively, and at 95% confidence level, no significant difference was obtained between the observed and estimated data. Therefore, the aforesaid model has the appropriate accuracy and efficiency to estimate the sedimentation rate of the Darabkola forest roads. It was also found that from among the input parameters of model, longitudinal slope of road, precipitation and sediment delivery factors were the most influential factors in the sediment production and transport, respectively.
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Volume 20, Issue 78 (1-2017)
Abstract
Due to water shortage in country, more accurate estimate of water reserve can be one of the most important guidelines on the optimal management of water resource and cycle for development of water productivity efficiency. Therefore, using artificial neural network techniques the water supply of 174 fallen trees from different species was simulated. From any part of each bole, components of constant volume were extracted and placed in 105ºC to be oven-dried to measure specific drought index and wood density. Three input layers of diameter at breast height, height and specific wood density were used to simulate the response variable. The method of trial and test were used for neural network topology architecture. The results showed that the use of only diameter as input layer based on the validation indices explained 65% of variance of test of data. Using the three layers in the neural network, optimized output including function of Tan-sigmoid in the designed architecture with the number of 15 neurons demonstrated the highest accuracy (R2=0/92, MSE= 0/001, RMSE=81/08). In order to save the costs and manpower and to avoid a destructive method, the optimized output in the form of black box has the wide applicability to predict the water reserve in the mixed-beech forests to manage water cycle in the studied ecosystem.
M. Nadi, A. Golchin, E. Sedaghati, S. Shafie, S. J. Hosseini Fard, G. Füleky,
Volume 21, Issue 1 (6-2017)
Abstract
Soil organic matter is the largest source of organic carbon in the soil surface which played an enormous role in restoring balance, environmental sustainability, soil elements and climatic conditions. Organic materials influence physical, chemical and biological properties of soil and thus soil fertility directly and indirectly. The amount, type and composition of organic matter are different in different soil and climatic conditions. Different soil components can hold different combination of soil organic matter. In this study soil samples were collected from virgin, Populus and Alnus forests from Guilan Province, Iran. The amount of organic matter in the original samples and sand, silt and clay fractions were measured and the quality of organic carbon (13C) and hydrogen (1H) were determined in fractions by Nuclear Magnetic Resonance. Based on the results the amount of organic carbon in the original samples of Alnus forest was highest. among fractions, clay had the highest amount of organic carbon. The result of Nuclear Magnetic Resonance showed presence of different aliphatic and aromatic carbons and hydrogen in different samples. Virgin forest had the highest proportion of alkyl to oxygenated alkyl carbon ratio and Alnus forest showed the highest ratio of aliphatic to aromatic carbon,. Both mentioned ratios was higher in clay fraction than other two fractions.
S. Ashrafi-Saeidlou, Mh. Rasouli-Sadghiani, M. Barin,
Volume 21, Issue 3 (11-2017)
Abstract
The Firing effect on soil depends on its intensity and duration. In order to investigate influence of different firing backgrounds on some soil physical and chemical properties, 80 soil samples were taken from two depths (0-5 cm and 5-20 cm) with different time of firing background (2 and 12 months). Some soil physical and chemical characteristics were measured at soil samples. The results showed that there was a significant difference in the amount of pH, EC, bulk density and ammonium in soils with different history of burning. The amount of studied indices increased after firing in burned soils compared to the control ones. However 12 months later they reach to their pre-fire levels. Total nitrogen amount in soils with 2 and 12 months firing history were 1.18 and 1.11 times higher than the control soils, respectively. The amount of organic carbon in surface depth (0-5 cm) of burned soils with 2 and 12 month firing backgrounds 37.25 and 24.7 percent increased in comparison to control soils, respectively. Also, fire led to a significant reduction in the amount of clay (29.25 percent) in burned areas compared to the control ones. Soil particle size distribution in control sites were in clay up to loam and in burned areas were in clay loam up to sandy loam classes. Therefore forest firing causes obvious changes in soil properties, remediation of which takes more than one year.
S. Zahedi, K. Shahedi, M. Habibnejhad Roshan, K. Solaimani, K. Dadkhah,
Volume 21, Issue 4 (2-2018)
Abstract
Soil depth is a major soil characteristic commonly used in distributed hydrological modeling in order to present watershed subsurface attributes. It strongly affects water infiltration and accordingly runoff generation, subsurface moisture storage, vertical and lateral moisture movement, saturation thickness and plant root depth in the soil. The objective of this study is to develop a statistical model that predicts the spatial pattern of soil depth over the watershed from topographic and land cover variables derived from DEM and satellite image, respectively. A 10 m resolution DEM was prepared using 1:25000 topographic maps. Landsat8 imagery, OLI sensor (May 06, 2015) was used to derive different land cover attributes. Soil depth, topographic curvature, land use and vegetation characteristics were surveyed at 426 profiles within the four sub-watersheds. Box Cox transformations were used to normalize the measured soil depth and each explanatory variable. Random Forest prediction model was used to predict soil depth using the explanatory variables. The model was run using 336 data points in the calibration dataset with all 31 explanatory variables (18 variables from DEM and 13 variables from remote sensing image), and soil depth as the response of the model. Prediction errors were computed for validation data set. Testing dataset was done with the model soil depth values at testing locations (93 points). The Nash-Sutcliffe Efficiency coefficient (NSE) for testing data set was 0.689. The results showed that land use, Specific Catchment Area (SCA), NDVI, Aspect, Slope and PCA1 are the most important explanatory variables in predicting soil depth.
A. Ashraf Amirinejad, S. Ghotbi,
Volume 22, Issue 2 (9-2018)
Abstract
The soil quality is defined as the ability of soil to function as an essential part of the human habitat. In this study, the effects of land use change (conversion of forest lands into agricultural lands) on the soil physical quality were studied in the Gilan-e-Gharb region. For this study, soil samples were collected from surface and subsurface layers of both land uses, and the peak and shoulder slope positions, in Miandar and Vidjanan catchments. Soil physical properties such as soil texture and particle size distribution, soil hydraulic conductivity, bulk density, mean weight diameter of aggregates, water holding capacity, and the soil organic carbon content were measured. The results showed that land use change of the forest to agricultural lands resulted in a sharp decline in the soil organic matter (52%) and an increase in silt and sand percentage and soil bulk density. Also, deforestation decreased the mean weight diameter of aggregates (from 0.39 to 0.14 mm in Miandar) and clay percent. It caused a reduction in the total porosity followed by a decrease of soil water holding capacity, and a decrease in the saturated hydraulic conductivity (from 10.34 to 1.86 cm/h), as well. In general, the results proved that the land use change from forest to agriculture severely decreased soil physical quality and its productivity.
F. S. Tarighat, Y. Kooch,
Volume 22, Issue 2 (9-2018)
Abstract
The effect of broad-leaved forest trees (Alnus glotinusa, Ulmus glabra, Popolus caspica and Parrotia persica) and their canopy position on soil C and N storage and mineraization in the plain forest areas of Noor was investigated. Soil samples were taken from two positions (near and away from the main stem) with the microplots of 30×30×15 cm. Litter (C and N), soil physical (bulk density, texture and water content), chemical (pH, EC, organic C, total N and available Ca), biochemical and biological (N mineralization and microbial respiration) characteristics were measured at the laboratory. Carbon mineralization rate (CMR) was calculated using the equation [incubation time period (hour) ×soil volume (gr) / CO2 amount (mol C)]. Soil C and N storage (ton/ha) was calculated by C and N contents, bulk density, and the soil sampling depth. The results showed that there was no significant difference between the C storage under the studied tree spcies, whereas N storage presented significantly greater amounts, under Alnus glotinusa (0.79 ton/ha) rather than Ulmus glabra, Popolus caspica and Parrotia persica (0.69, 0.45 and 0.21 ton/ha, respectively). The higher values of soil C (0.001 mol C/kg) and N (0.3 ml N/kg) mineralization were significantly recorded under Alnus glotinusa instead of tree species. Soil C and N storage and mineralization process were not affected by the sampling positions. According to the results, soil C and N storage and mineralization were influenced by litter quality and soil chemistry.
F. Jahanbakhshi, M. R. Ekhtesasi,
Volume 22, Issue 4 (12-2018)
Abstract
Land use/cover maps are the basic inputs for most of the environmental simulation models; hence, the accuracy of the maps derived from the classification of the satellite images reduces the uncertainty in modeling. The aim of this study was to assess the accuracy of the maps produced by machine learning based on classification methods (Random Forest and Support Vector Machine) and to compare them with a common classification method (Maximum Likelihood). For this purpose, the image of the OLI sensor of Landsat 8 for the study area (Sattarkhan Dam’s basin in the Eastern Azerbaijan) was used after the initial corrections. Five land uses including urban, irrigated and rain-fed agriculture, range and water body were considered. For conducting the supervised classification, ground truth data were used in two sets of educational (70% of the total) and test (30%) data.
Accuracy indexes were used and the McNemar test was employed to show the significant statistical difference between the performances of the methods. The results indicates that the overall accuracy of Support Vector Machine, Random Forest, and Maximum Likelihood methods was 96.6, 90.8, and 90.8 %, respectively; also the Kappa coefficient for these methods was 0.93, 0.81 and 0.83, respectively. The
existence of a significant statistical difference at the 95% confidence between the performances of the Support Vector Machine algorithm and the other two algorithms was confirmed by the McNemar test.
Z. Maghsodi, M. Rostaminia, M. Faramarzi, A Keshavarzi, A. Rahmani, S. R. Mousavi,
Volume 24, Issue 2 (7-2020)
Abstract
Digital soil mapping plays an important role in upgrading the knowledge of soil survey in line with the advances in the spatial data of infrastructure development. The main aim of this study was to provide a digital map of the soil family classes using the random forest (RF) models and boosting regression tree (BRT) in a semi-arid region of Ilam province. Environmental covariates were extracted from a digital elevation model with 30 m spatial resolution, using the SAGAGIS7.3 software. In this study area, 46 soil profiles were dug and sampled; after physico-chemical analysis, the soils were classified based on key to soil taxonomy (2014). In the studied area, three orders were recognized: Mollisols, Inceptisols, and Entisols. Based on the results of the environmental covariate data mining with variance inflation factor (VIF), some parameters including DEM, standard height and terrain ruggedness index were the most important variables. The best spatial prediction of soil classes belonged to Fine, carbonatic, thermic, Typic Haploxerolls. Also, the results showed that RF and BRT models had an overall accuracy and of 0.80, 0.64 and Kappa index 0.70, 0.55, respectively. Therefore, the RF method could serve as a reliable and accurate method to provide a reasonable prediction with a low sampling density.
N. Moradian Paik, S. Jafari,
Volume 26, Issue 4 (12-2022)
Abstract
Changes in land quality factors were investigated according to the change in land use of two conventional cropping systems in Khuzestan (Dimcheh region, periodic cultivation system, sugarcane, forest, and deforesting in Zaras region). The results showed that by the change of forest land use, organic carbon from 0.93 to 0.55%, cation exchange capacity (CEC) from 19.6 to 13.3 cmol/kg, C/N from 7.4 to 3.8%, the mean weight diameter of aggregate (MWD) from 1.7 to 1.3%, and microbial respiration from 0.11 to 0.06 mg of CO2 /gr of soil per day decreased and in contrast, the dispersible clay from 4.6 to 19.3% increased. PCA analysis for the parameters showed that five factors justified more than 90% of the variance in the values of FC, PWP, AW, and AF. In the Dimcheh region, the average volumetric moisture content of FC from 31.3% to 27.3%, available water from 12.9% to 9.8%, dispersible clay from 56.1% to 12.3%, and bulk density reduced from 1.6 to 1.4%, organic carbon from 0.45 to 0.78%, C/N from 6.3 to 10.0%, microbial respiration from 0.01 to 0.04 mg of CO2 /gr soil per day and MWD of aggregates increased from 0.77 to 1.3 mm. Five factors including FC, AW, BD, DC, and OM explained more than 90% of the variance.
M. Khajeh, C. B. Komaki, M. Rezaei, V. Sheikh, L. Ebadi,
Volume 28, Issue 2 (8-2024)
Abstract
In the future, the risk of land subsidence due to water resources shortage crisis and improper water resources management will become more and more dangerous. It is necessary to assess and identify areas susceptible to subsidence risk and take necessary actions to reduce risks related to land subsidence. In this study, first, the risk of land subsidence was identified and evaluated using a radar interferometry method called LiCSBAS. Then, the spatial relationship between the occurrence of land subsidence hazard and effective factors such as ground elevation, slope, slope aspect, lithology, land use, groundwater decline, distance from rivers, distance from faults, topographic moisture index, and arc curvature was investigated using the random forest (RF) model. In the end, the land subsidence hazard sensitivity map was prepared after calibrating the random forest algorithm. The analysis of LiCSBAS interferometric time series data from 2015 to 2022 showed that the center of the Marvdasht-Kharameh plain and adjacent agricultural areas are continuously subsiding and the mean deformation rate map showed a subsidence rate of 11.6 centimeters per year. The results of determining the spatial relationship between subsidence occurrence and effective factors confirmed the positive impact of distance from rivers, urban and agricultural land uses, depth of bedrock (aquifer thickness), groundwater decline, and alluvial and fine-grained formations on this phenomenon. Also, the results of subsidence modeling using the random forest algorithm showed that factors such as bedrock depth, groundwater decline, land use, and geology have the greatest impact on the potential for subsidence occurrence in the study area. Also, based on the results, about 3 to 4 percent of the areas are in the very high and extremely high-risk classes of land subsidence, especially in the center and suburbs of Mervdasht. Therefore, water resources management and control and developing a systematic program to reduce subsidence risk and aquifer recharge conservation in Merudasht-Kharameh Plain is essential.
H. Ramezani Etedali, M. Ahmadi,
Volume 29, Issue 2 (7-2025)
Abstract
change, accurately predicting wheat production is essential for developing precision agriculture. Remote sensing enables the indirect prediction of crop production before harvest. This research investigates the application of the random forest method and support vector regression for simulating wheat production across ten selected farms in Qazvin Plain from 2019 to 2020, employing NDVI, MSAVI, and EVI vegetation indices. Sentinel 2 satellite data was utilized for the vegetation indices. Production data for the ten wheat fields was obtained from the Agricultural Jihad Organization of Qazvin Province. Evaluation of support vector regression and random forest to assess both the observed and simulated wheat production data was conducted using R2, MBE, RMSE, and MAE statistics. To explore the simulation of wheat production using vegetation indices, seven methods were defined: methods 1 to 3 examine each index separately; methods 4 to 6 focus on binary combinations of the indices; and method 7 considers the combined effects of all three indices. The support vector regression model provided good estimates of wheat production in all methods, except methods one and four, in the test phase, with a coefficient of determination of more than 0.98 and a low RMSE. The random forest model showed significant results in all methods except methods two and six during the test phase, achieving a 95% probability (P-value=0.00) with a coefficient of determination greater than 0.8. Overall, this research highlights the importance and potential of machine learning techniques for timely crop production prediction as a strong foundation for regional food security.
S. Yousefi, S.n. Emami, M. Nekooeimehr, S. Mardanian,
Volume 29, Issue 2 (7-2025)
Abstract
In the present study, the Road Sediment Delivery Model (SEDMODL) and Geographic Information System (GIS) were utilized to estimate the average annual sedimentation caused by the forest road network in the oak forests in the west of Iran, Chaharmahal and Bakhtiari Provinces (Nazi forest road with a length of 5171 meters). Sedimentation from the study forest road network was estimated based on three basic factors in the model. Also, 30 erosion benchmarks were installed to measure the erosion and sedimentation rate at different distances from the road and in different parts of the study road and changes were measured during a year. The results showed that the average soil erosion at different distances from the Nazi road based on erosion benchmarks is 5.7 mm per year. In addition, the estimated erosion and sedimentation rate of the entire study road network based on the SEDMODL model is 2875 and 570 tons per year per kilometer, respectively. Model evaluation using erosion benchmarks showed that SEDMODL is a suitable model for estimating soil erosion on forest roads in the west of Iran (R2=0.78 and RMSE=0.73). It should be noted that statistical analysis of erosion hot-spot analysis showed that 39 percent of forest roads in Nazi showed very high erosion. Based on the results of the present study, it is suggested that conservative, protective, and road maintenance measures in areas with high erosion risk should be prioritized by decision-makers.
Mohammad Shayannejad, Elham Fazel Najafabadi, Fahimeh Hatamian Jazi,
Volume 29, Issue 3 (10-2025)
Abstract
Regarding the increasing need for water resources and the decline of surface water resources, awareness of these resources is a crucial need in planning, developing, and protecting them. This research was conducted to model the water quality index (the most widely used feature of determining water quality) using machine learning models (Random Forest and Support Vector Machine) in the Zayandehrood River. Regarding the large number of water quality indices, the NSFWQI index was used in this study. First, this index was calculated, and then, input data, including water quality characteristics of 8 stations over 31 years, and the river water quality index were used. In this research, 80% of the data was used in the training stage, and the remaining 20% was used in the evaluation stage. The optimal model was selected based on the evaluation criteria, including R2, CRM, and NRMSE. The results showed that the Support Vector Machine algorithm (0.931 < R² < 0.982, 1.321
Iman Saleh, Seyed Masoud Soleimanpour, Majid Khazaei, Omid Rahmati, Samad Shadfar,
Volume 29, Issue 4 (12-2025)
Abstract
Soil loss and extensive degradation caused by gully erosion have always caused serious damage. Because direct field measurement and monitoring of gully erosion are costly and time-consuming, it is very difficult to determine the amount of soil loss caused by gully erosion. The present research was conducted to calculate the volume of soil loss due to gully erosion using machine learning models in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province based on field studies. Machine learning models include random forest, support vector machine, artificial neural network, and adaptive neural fuzzy inference system. The location of 68 gullies in the area was recorded. Hence, initially, digital layers of factors affecting the expansion of gullies, including topography, pedology, lithology, and hydrology, were prepared as independent variables to model soil loss caused by gullies. Then, representative gullies were selected in the studied watershed, and the volume of soil loss due to gully erosion was directly measured in the field as a dependent variable. The measured gullies were randomly divided into two training and validation groups. The results of the models were evaluated using root mean square error (RMSE) and R2, and the models were compared. According to the results, gully erosion in the Abgendi watershed of Kohgiluyeh and Boyar Ahmad province is increasing every year. Also, the amount of erosion and soil loss will increase when the amount of rainfall and the frequency of intense rainfall (≥5mm) are high. Among the machine learning models used in the present research, the random forest (RF) model was selected as the best model to predict soil loss generated by gully erosion.
Mehdi Feyzolahpour, Behrouz Mohamady Yeganeh, Maryam Amri,
Volume 29, Issue 4 (12-2025)
Abstract
By utilizing land surface temperature (LST), valuable insights can be gained regarding the impact of land use on energy balance processes. Therefore, this study aimed to investigate the trend of LST changes due to land use changes in the Gorab rural district. Four land use types, including water bodies, bare land, Agricultural area, and forest, were determined from 2013 to 2024 for the maximum likelihood classification (MLC) and support vector machine (SVM) models. The surveys showed that the area of water in the dry period decreased from 0.9 km2 in 2013 to 0.4 km2 in 2024, a decrease of 0.5 km2. In contrast, the area of forest areas increased from 136.1 km2 in the dry period of 2013 to 147.2 km2 in 2024. The Kappa coefficient values for the SVM and MLC models during the wet season of 2021 were 53.94 and 68.7, respectively. Based on this, it was found that the MLC model has higher accuracy. To match spectral indices with LST values, NDVI, NDSI, and NDWI were calculated. Land use changes during the 2013-2024 period affected land surface temperatures, causing fluctuations from 11.5°C to 21.18°C in the wet season and from 13.81°C to 31.45°C in the dry season. The highest LST values were associated with barren land, while water bodies and vegetation cover had the lowest LST values. Among the spectral indices, the highest positive correlation was observed with NDWI, with a value of 0.64 in 2024. The highest negative correlation, -0.66, was observed with NDVI in the same year. Over the 11 years, the area of forest cover increased by 8.15%, while agricultural land decreased by 33.5%. The most significant change occurred in agricultural lands, which declined in area from 35.5 km² to 23.6 km².
Homa Chegini, Chooghi Bairam Komaki, Majid Owneq, Hamidreza Asgari, Khalil Ghorbani,
Volume 30, Issue 1 (3-2026)
Abstract
This study aimed to analyze the spatial–temporal correlation between the Vegetation Health Index (VHI) and climatic variables, including precipitation, potential evapotranspiration (PET), and mean temperature, in Golestan Province during the period 2000–2024. MODIS satellite products were used for vegetation and land surface temperature data, while the TerraClimate dataset provided precipitation and PET variables. After spatial–temporal alignment, the Cross-Correlation Function (CCF) was applied to identify optimal time lags, and the Random Forest model was employed to assess the relative importance of the climatic drivers. Turning to the results, increasing trends in mean temperature and PET were observed, alongside a significant decrease in precipitation, which led to intensified climatic stress and reduced VHI across the province, especially during summer in croplands and rangelands. The relationship between VHI and precipitation was positive (maximum correlation of 0.299 in croplands), negative with PET (−0.287), and non-linear with temperature (0.275). Notably, VHI responded to precipitation with short-term lags (0–1 month), whereas PET and temperature effects emerged with longer lags (2–4 months). The Random Forest analysis highlighted precipitation as the most influential factor on VHI, followed by PET and temperature, achieving strong predictive performance (R² = 0.78, RMSE = 0.09). Overall, these findings emphasize precipitation as the immediate driver of vegetation health, while PET and temperature act as secondary, cumulative stressors. The results provide valuable insights for developing climate adaptation and sustainable resource management strategies in agriculture and natural ecosystems of Golestan Province.
