Application of Google Earth Engine Platform and Random Forest Algorithm in Investigating the Trend of Land Use Changes (Case Study: Part of the Abyek Region, Qazvin Province)

Document Type : Research Paper

Authors

1 Department of Soil Science, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.

2 soil science department< faculty of agricultural engineering and technology, university of Tehran

Abstract

Transformations induced by land use and cover change, particularly in arid and semi-arid ecosystems, are among the most fundamental concerns in sustainable development and ecological conservation. Such alterations have direct implications for the resilience of natural resources and food security. This study aimed to trace land use changes over a 40-year period (1985–2025) in approximately 60,000 hectares of Abyek Region, Qazvin Province. For this purpose, Landsat satellite images were processed in the Google Earth Engine environment and classified using the Random Forest algorithm. In order to enhance the accuracy of the results, in addition to spectral bands, a set of complementary variables including spectral indices (NDVI, EVI, MNDWI, SAVI), topographic variables, and the Global Human Settlement Layer (GHSL) were incorporated into the modeling. The land use classes examined in this study included residential-industrial lands, irrigated agriculture, rainfed agriculture, orchards, rangelands, saline rangelands, highly saline rangelands, and wetlands. The accuracy assessment of the results indicated an overall accuracy ranging from 0.91 to 0.95 and a Kappa coefficient in the range of 0.88 to 0.93. The results of the study demonstrate that the use of high-spatial-resolution satellite data and the Random Forest algorithm can be introduced as an efficient method for preparing land use maps. The findings of this study emphasize the necessity of developing land use management programs with an approach based on the ecological capacity of the region, in such a way that regional development is directed toward the conservation of natural resources and the achievement of environmental sustainability.

Keywords

Main Subjects

Introduction

Land use and land cover change (LUCC) dynamics represent one of the most critical indicators of environmental and socio-economic transformations, with far-reaching implications for biodiversity, climatic patterns, water resource conditions, and food security. Continuous and up-to-date assessment of land use change dynamics plays a pivotal role in the optimal management of natural resources and the formulation of regional development strategies. Numerous studies have demonstrated that Google Earth Engine has found extensive application in various research fields, including urban planning, water resource management, public health, forestry, agriculture, and land use change. By integrating the capabilities of the Google Earth Engine platform with modern analytical methods, the rapid evaluation of issues related to human-environment interactions has become feasible.

The present research innovatively integrates three categories of variables, including spectral indices, topographic parameters, and Global Human Settlement Layer data (GHSL: Global Built-up Surface 1975–2030, P2023A), for land use classification. While the majority of previous studies have relied solely on spectral bands or a single index, the combined approach employed in this study is expected to provide higher accuracy, particularly in distinguishing spectrally similar classes such as rangelands and rainfed agricultural lands, as well as saline and highly saline rangelands. Furthermore, the examination of change trajectories in specific classes such as highly saline rangelands and orchards in an area characterized by high sensitivity to agricultural activities and water resources constitutes another distinguishing feature of this study compared to similar research conducted previously.

Method

The study area is located within the Abyek region in Qazvin Province. Data processing and analysis for land use mapping was conducted using Google Earth Engine as a powerful cloud computing platform. To assess these changes over the period from 1985 to 2025, Landsat satellite imagery (Level-2 Surface Reflectance) with a decadal interval was utilized. The image selection timeframe was from early May to late July of each year, corresponding to the period of peak vegetation growth. Images with cloud cover less than 10% were filtered, and to reduce temporal noise, a median composite was generated from the filtered image collection. The construction of the median composite is intended to represent actual ground surface conditions, meaning that the reflectance values of pixels are computed across the entire temporal range. This method produces a stable and uniform image by eliminating the effects of transient phenomena such as clouds and shadows.

In addition to the spectral bands of the Landsat sensor, various spectral indices were employed to improve the discrimination of land use classes, including NDVI, EVI, SAVI, and MNDWI. The SRTM digital elevation model was also utilized to extract variables such as elevation and slope. Furthermore, to account for the effect of urban development, the GHSL: Global Built-up Surface 1975–2030 (P2023A) dataset was incorporated. Training points were uploaded as Feature Collection within the Google Earth Engine environment. The land use classes employed included "Residential-Industrial","Rainfed Agriculture","Irrigated Agriculture", "Orchards","Rangelands", "Highly Saline Rangelands","Saline Rangelands" and "Wetlands". Depending on the extent and spatial distribution of each class, between 50 and 200 training polygons were selected for each year. The Random Forest algorithm with 200 trees was employed for land use classification, utilizing the spectral bands and aforementioned indices as model inputs. The data were split with a ratio of 70% for training and 30% for testing. Finally, as a post-processing step, a Majority filter with a 3×3 neighborhood matrix was applied.

Accuracy assessment is considered an essential component in the land use mapping process using remote sensing data. In this study, overall accuracy, producer's accuracy, user's accuracy, and the Kappa coefficient were evaluated using an confusion matrix and independent validation datasets.

Results

The analysis of area changes in these classes reveals that from 1985 to 2025, Residential-Industrial areas increased by 406.38%, Rainfed Agriculture by 765.47%, Orchards by 615.51%, and Highly Saline Rangelands by 298.90%. In contrast, Irrigated Agriculture decreased by 8.49%, Rangelands by 53.59%, Saline Rangelands by 14.44%, and Wetlands by 75.33%.

The results of the classification accuracy assessment indicate that the Random Forest model, utilizing spectral variables, vegetation indices, and topographic variables, demonstrated acceptable performance in discriminating different land use classes within the study area. The overall classification accuracy across all years examined exceeded 91%, and the Kappa coefficient was above 88%, indicating a high level of agreement between the classification results and ground truth conditions.

Analysis of land use/land cover reveals that residential-industrial areas have increased more than fivefold, mainly due to infrastructure development, industrial and agricultural activities in Abyek County, and proximity to Tehran. Rainfed agricultural lands have experienced a ninefold increase, resulting from self-sufficiency policies for strategic crops such as wheat, as well as water scarcity. In contrast, irrigated agricultural lands have decreased by 9%, and rangelands have declined by more than 50%. Furthermore, very saline rangelands have increased fourfold due to declining groundwater levels, rising temperatures, and increased soil salinity, leading to reduced land productivity and desertification. The region's wetland area has also decreased by 75% because of over-extraction of upstream water resources, drainage schemes, decreased precipitation, and drought, resulting in consequences such as wind erosion and habitat destruction.

Conclusions

The study area, as part of the Qazvin Plain, is considered one of the strategic regions for agricultural production in the country. In this research, utilizing Landsat satellite imagery and integrating spectral indices with Global Human Settlement Layer data, land use change maps were produced at ten-year intervals over a forty-year period. The application of the Google Earth Engine platform and the Random Forest algorithm enabled the analysis of land use changes while achieving high classification accuracy. The combination of upward trends in classes such as Residential-Industrial, Orchards, and Rainfed Agriculture, along with the downward trends in Rangelands and Wetlands, reflects a transformation in land utilization patterns. Population growth, declining water resources, and overexploitation of natural resources are identified as the strategic drivers of these changes. Based on the findings of this study, promoting sustainable agricultural practices and formulating land use policies aligned with the ecological capacity of the region are imperative. Urban and industrial development should be planned in a manner that prevents the reduction of valuable agricultural and rangeland areas. Ultimately, these measures can steer regional development toward optimal and sustainable utilization of soil and water resources.

Funding

The study was funded by the University of Tehran, Country Iran.

Authorship contribution

Conceptualization, Fereydoon Sarmadian; methodology, Fereydoon Sarmadian; software, Fereydoon Sarmadian, Ayeh Javidfar; validation, Fereydoon Sarmadian, Ayeh Javidfar; formal analysis, Fereydoon Sarmadian, Ayeh Javidfar; investigation, Ayeh Javidfar; resources, Ayeh Javidfar; data curation, Fereydoon Sarmadian, Ayeh Javidfar; writing—original draft preparation, Ayeh Javidfar; writing—review and editing, Ayeh Javidfar; visualization, Ayeh Javidfar; supervision, Fereydoon Sarmadian; project administration, Fereydoon Sarmadian; funding acquisition, Fereydoon Sarmadian.

All authors have read and agreed to the published version of the manuscript.

All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.

Declaration of Generative AI and AI-assisted technologies in the writing process

The authors declare that any AI and AI-assisted software was not used for writing process.

Data availability statement

Data available on request from the authors.

Acknowledgements

The authors sincerely appreciate the support and facilities provided by University of Tehran.

Ethical considerations

The authors avoided data fabrication, falsification, and plagiarism, and any form of misconduct.

Conflict of interest

The authors declare no conflict of interest. 

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Iranian Journal of Soil and Water Research
Volume 57, Issue 5
July 2026
Pages 1099-1117

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