Article
Remote Sensing
Hao Wu, Anqi Lin, Xudong Xing, Danxia Song, Yan Li
Summary: This study proposed a margin-based measure of random forest to identify the core driving factors of urban land use change, which was found to be more reliable and sensitive in detecting the driving mechanism behind land use change. Regardless of the similarity measure chosen and applied, the importance values and ranking orders of driving factors measured by the margin-based method were stable.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2021)
Article
Environmental Sciences
Sai Qu, Youngryel Ryu, Jiangong Liu, Jonathan A. Wang
Summary: South and North Korea have experienced contrasting economic developments since the 1950s despite sharing similar climates. The greening rate in North Korea over 1986-2017 was almost twice that of South Korea, with the expansion of agricultural facilities in South Korea's cropland being the main cause of the greening discrepancy. CO2 fertilization effects and transitions from grassland to cropland promoted an increase in NDVImax in North Korea, while deforestation led to decreasing NDVImax in forest areas.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Biodiversity Conservation
Wuping Huang, Mingming Zong, Zexin Fan, Yuan Feng, Shiyu Li, Changqun Duan, Haixia Li
Summary: Forests globally converted to agricultural land to meet human demands. Research found that soil organic carbon, total nitrogen, potassium, and free iron are the most important indicators of soil quality in tropical acidic red soils. Land-use change related to significant decreases in SQI.
ECOLOGICAL INDICATORS
(2021)
Article
Environmental Sciences
Wiwin Ambarwulan, Fajar Yulianto, Widiatmaka Widiatmaka, Ati Rahadiati, Suria Darma Tarigan, Irman Firmansyah, Muhrina Anggun Sari Hasibuan
Summary: This study aims to identify the land use/land cover (LULC) changes in Cisadane Watershed, Indonesia, and simulate future LULC for 2030 and 2050. The results reveal the trade-off between maintaining food security and conserving natural resources. Efficient land use planning in the future is important to meet increasing resource demand due to population growth.
EGYPTIAN JOURNAL OF REMOTE SENSING AND SPACE SCIENCES
(2023)
Article
Green & Sustainable Science & Technology
Liang Guo, Xiaohuan Xi, Weijun Yang, Lei Liang
Summary: The study revealed a continuous increase in built-up area and a decrease in vegetation area in Guangzhou City, China from 1986 to 2018. There was a strong positive correlation between GDP and built-up area, while a strong negative correlation was found between GDP and vegetation area, suggesting that the expansion of built-up area came at the expense of reduced vegetation area.
Article
Biology
Recep Sinan Arslan, Hasan Ulutas, Ahmet Sertol Koksal, Mehmet Bakir, Buelent ciftci
Summary: Sleep staging plays an important role in sleep assessment and early diagnosis of sleep disorders. This study utilized sleep data from 50 patients collected by a Philips Alice clinic polysomnography device and applied Random Forest, Extra Trees, and Decision Tree classifiers for sleep staging. The results showed that the proposed method achieved high accuracy and could alleviate the burden of sleep doctors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Geosciences, Multidisciplinary
Erjuan Yang, Xiaoli Zhao, Wei Qin, Jian Jiao, Jianqiao Han, Man Zhang
Summary: Assessing the temporal impacts of dryland-to-paddy conversion on soil quality is important for sustainable land use. A study in Sanjiang Plain evaluated the soil quality with different durations of conversion using principal component analysis and soil quality index method. The results showed that the soil quality varied over time and was influenced by factors such as soil organic carbon and available potassium. This finding can contribute to soil quality diagnosis and sustainable development in similar regions.
Article
Environmental Studies
Mohammed B. B. Altoom, Elhadi Adam, Khalid Adem Ali
Summary: This study used multitemporal Landsat observation data to investigate the spatio-temporal dynamics of rainfed agriculture in North Darfur State from 1984-2019. The results showed that there was high spatial variability in rainfed agriculture during this period, which may exacerbate regional land degradation and disputes among farmers over scarce wadi lands.
Article
Environmental Sciences
Kanika Singh, Ignacio Fuentes, Dhahi Al-Shammari, Chris Fidelis, James Butubu, David Yinil, Amin Sharififar, Budiman Minasny, David Guest, Damien J. Field
Summary: This study combined high-resolution satellite imagery with novel signal extraction methods to evaluate the feasibility and soil capacity of cocoa cultivation, and identified potential cocoa regions. The results showed that the classification accuracy of cocoa regions reached 97%, demonstrating the feasibility of this method for monitoring land use and cocoa production.
Article
Agronomy
Sa'ad Ibrahim
Summary: By using a Random Forest method for feature selection and combining satellite and radar data, the performance of land use and land cover classification models can be improved, leading to a reduction in prediction errors. This study highlights the importance of synergistically utilizing optical, radar, and elevation data in improving the accuracy of land use and land cover maps, which can assist decision-makers in spatial planning applications.
Article
Environmental Sciences
Padmanava Dash, Scott L. Sanders, Prem Parajuli, Ying Ouyang
Summary: The goal of this study is to improve the accuracy of land use and land cover (LULC) classification of satellite imagery for the Big Sunflower River Watershed, Mississippi using ancillary data, multiple classification methods, and a post-classification correction (PCC). The SVM classification method, combined with PCC, proved to be the most effective strategy for dealing with spectrally similar LULC features in both the growing season and post-harvest imagery. The strategies from this study can help evaluate LULC in agricultural and other watersheds.
Article
Environmental Sciences
Rosa Lasaponara, Nicodemo Abate, Carmen Fattore, Angelo Aromando, Gianfranco Cardettini, Marco Di Fonzo
Summary: This study utilizes Sentinel-2 NDVI time series and Google Earth Engine to detect small-scale land-use/land-cover changes in fire-disturbed environs. The analysis identifies different types of changes and evaluates their reliability.
Article
Environmental Sciences
Shahzad Ali, Huang An Qi, Malak Henchiri, Zhang Sha, Fahim Ullah Khan, Muhammad Sajid, Jiahua Zhang
Summary: This study used data from 2001 to 2015 to generate a time series of annual land use and land cover maps in South Asia using random forest classification. The findings show that forest land, farmland, and urban areas have increased, while shrublands, evergreen broadleaf forests, and water bodies have decreased in South Asia over the past 15 years.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Environmental Sciences
Dong Chen, Yafei Wang, Zhenyu Shen, Jinfeng Liao, Jiezhi Chen, Shaobo Sun
Summary: This study proposes a method for long time-series mapping and change detection of coastal zone land use based on Google Earth Engine and multi-source data fusion, using the Bohai rim coastal area of China as an example. The results show a decrease in cropland and an increase in coastal aquaculture ponds in this region.
Article
Environmental Sciences
Bibizahra Mazloum, Saeid Pourmanafi, Alireza Soffianian, Abdolrassoul Salmanmahiny, Alexander V. Prishchepov
Summary: Iran has vast dryland areas, and rapid population growth is leading to increased demand for land resources. Research shows that from 1985 to 2016, there has been a decrease in rangeland areas in Iran, with expansion of residential and industrial areas. Future predictions suggest further expansion of industries and residences at the expense of continued degradation of rangelands.
LAND DEGRADATION & DEVELOPMENT
(2021)
Article
Soil Science
Hamid Mahmoudzadeh, Hamid Reza Matinfar, Ruth Kerry, Shler Eskandari, Zohre Ebrahimi-Khusfi, Ruhollah Taghizadeh-Mehrjardi
Summary: The study evaluated the efficacy of various hybrid ANFIS models in predicting soil properties in western Iran, with ANFIS+ABC and ANFIS+PSO approaches performing the best for soil organic carbon and total nitrogen prediction. The hybrid models significantly improved prediction accuracies compared to the classic ANFIS model, supporting the main hypothesis.
SOIL USE AND MANAGEMENT
(2022)
Article
Geosciences, Multidisciplinary
Mojtaba Zeraatpisheh, Eduardo Leonel Bottega, Esmaeil Bakhshandeh, Hamid Reza Owliaie, Ruhollah Taghizadeh-Mehrjardi, Ruth Kerry, Thomas Scholten, Ming Xu
Summary: The study identified management zones through soil quality assessment and found different soil quality grades within each zone. The research provides a framework to investigate the homogeneity of delineated management zones in terms of soil quality.
Article
Geosciences, Multidisciplinary
Mojtaba Zeraatpisheh, Younes Garosi, Hamid Reza Owliaie, Shamsollah Ayoubi, Ruhollah Taghizadeh-Mehrjardi, Thomas Scholten, Ming Xu
Summary: In this study, the performance of predicting soil organic carbon (SOC) in an arid agroecosystem in Iran using different datasets and machine learning algorithms was compared. The results showed that the Cubist model performed the best with the MCC dataset and the combined dataset of MCC and remote sensing time-series (RST), while the RF model showed better results for the RST dataset. Soil properties were found to be the main factors influencing SOC variation in the MCC and combined datasets, while NDVI was the most controlling factor in the RST dataset. The study suggested that time-series vegetation indices may not significantly improve SOC prediction accuracy, but combining MCC and RST datasets could produce SOC spatial maps with lower uncertainty.
Article
Plant Sciences
Somayeh Emami, Hossein Ali Alikhani, Ahmad Ali Pourbabaee, Hassan Etesami, Fereydoon Sarmadian, Babak Motesharezadeh, Ruhollah Taghizadeh-Mehrjardi
Summary: The study investigated the potential of fluorescent pseudomonads strains in the rhizosphere and endophyte of wheat plants to reduce phosphorus fertilizer application and improve plant traits. The results showed that the combined use of Pseudomonas strains with different levels of phosphorus fertilizer increased wheat growth and yield. Furthermore, the application of these strains also increased soil enzyme activity. This study emphasizes the importance of using chemical-biological fertilizer packages for improving phosphorus nutrition and grain yield in agricultural systems.
JOURNAL OF SOIL SCIENCE AND PLANT NUTRITION
(2022)
Article
Environmental Sciences
Ruhollah Taghizadeh-Mehrjardi, Hossein Khademi, Fatemeh Khayamim, Mojtaba Zeraatpisheh, Brandon Heung, Thomas Scholten
Summary: This study tested and evaluated multiple base learners and model averaging techniques for predicting soil properties in central Iran. The results showed that model averaging approaches can improve the predictive accuracy for soil properties, with different techniques performing better for different soil attributes.
Article
Geosciences, Multidisciplinary
Khadijeh Taghipour, Mehdi Heydari, Yahya Kooch, Hassan Fathizad, Brandon Heung, Ruhollah Taghizadeh-Mehrjardi
Summary: Soil quality, one of the most important characteristics of soil, is crucial for sustainable soil management and evaluating soil degradation. This study aims to assess the impacts of deforestation on soil quality in Iran using a digital soil mapping approach. The results show that the soil quality in the protected forested area is significantly higher than the degraded/deforested area. Machine learning techniques, particularly the Random Forest model, outperform geostatistical approaches in mapping soil quality. This study provides a framework for assessing the impacts of deforestation on soil patterns, which can inform land use planning and forest resource management strategies.
Article
Environmental Sciences
Fatemeh Cheshmberah, Ali A. Zolfaghari, Ruhollah Taghizadeh-Mehrjardi, Thomas Scholten
Summary: Soil Particle Size Distribution (PSD) is a fundamental property that affects soil hydraulic properties and structure. This study evaluated different mathematical models for predicting PSD and used Random Forest to determine the relationship between covariates and the best models' parameters. The results showed that different models performed better for different particle sizes, and the Jaky model performed well in predicting soil particle fractions. The study also demonstrated the potential of combining PSD models and digital soil mapping techniques for spatial distribution analysis.
GEOCARTO INTERNATIONAL
(2022)
Article
Environmental Sciences
Ataollah Shirzadi, Himan Shahabi, Kamal Nabiollahi, Ruhollah Taghizadeh-Mehrjardi, Ivan Lizaga, John J. Clague, Sushant K. Singh, Fariba Golmohamadi, Anuar Ahmad
Summary: This study used machine learning techniques to predict soil erodibility in the Dehgolan region of Iran and compared the performance of five algorithms. The Gaussian Processes model showed the highest prediction accuracy and is valuable for studying soil erodibility in areas with similar climate and soil characteristics.
GEOCARTO INTERNATIONAL
(2022)
Article
Agronomy
Hassan Fathizad, Ruhollah Taghizadeh-Mehrjardi, Mohammad Ali Hakimzadeh Ardakani, Mojtaba Zeraatpisheh, Brandon Heung, Thomas Scholten
Summary: This study examines the spatiotemporal dynamics of soil organic carbon (SOC) for the Yazd-Ardakan Plain, Iran, using remote sensing data and digital soil mapping techniques. The Random Forest (RF) machine-learner yielded the highest accuracy among the models used, and normalized difference vegetation index, modified vegetation index, and ground-adjusted vegetation index were identified as important predictors of SOC. The decrease in SOC observed from 1986 to 2016 can be attributed to land use changes and agricultural activities.
Article
Soil Science
Ruhollah Taghizadeh-Mehrjardi, Razieh Sheikhpour, Mojtaba Zeraatpisheh, Alireza Amirian-Chakan, Norair Toomanian, Ruth Kerry, Thomas Scholten
Summary: Digital soil mapping can be used to predict soils at unvisited sites, but problems arise when predictions are needed in areas without any soil observations. A new semi-supervised learning approach was found to outperform supervised learning in extrapolating soil classes in target areas, resulting in higher accuracy and lower uncertainty.
Article
Environmental Sciences
Aram Shahabi, Kamal Nabiollahi, Masoud Davari, Mojtaba Zeraatpisheh, Brandon Heung, Thomas Scholten, Ruhollah Taghizadeh-Mehrjardi
Summary: Spatial modeling using a hybridized RF + PSO model with Vis-NIR spectroscopy, remote sensing, and topographic data accurately predicted soil characteristics in agricultural land in western Iran. The combination of spectroscopic data and environmental variables improved model performance significantly.
GEOCARTO INTERNATIONAL
(2022)
Article
Geosciences, Multidisciplinary
Zahra Sohrabizadeh, Hamid Sodaeizadeh, Mohammad Ali Hakimzadeh, Ruhollah Taghizadeh-Mehrjardi, Mohammad Javad Ghanei Bafghi
Summary: This study evaluates the spatial distribution and concentration of heavy metals in soil samples from the Kushk Mine in Bafgh, Iran. Hierarchical clustering analysis, principal component analysis, and spatial distribution patterns were used to assess the distribution of elements in the area. The analysis reveals that heavy metals can be divided into two groups, with lead, cadmium, zinc, and copper influenced by anthropogenic and lithogenic pollution, and iron and manganese impacted by both factors. Higher concentrations of heavy metals were found in the south of the mine and near the tailings.
GEOSCIENCE DATA JOURNAL
(2023)
Article
Environmental Sciences
Tom Broeg, Michael Blaschek, Steffen Seitz, Ruhollah Taghizadeh-Mehrjardi, Simone Zepp, Thomas Scholten
Summary: This study tests the transferability of soil organic carbon (SOC) models for cropland soils using five different types of covariates. The results show that satellite and combined models are transferable, but their accuracy declines. Additionally, mixed-data models significantly improve the accuracies of satellite, terrain, and combined models, while they have no effect on climate models and decrease the models based on soil covariates.
Article
Agronomy
Sedigheh Maleki, Alireza Karimi, Amin Mousavi, Ruth Kerry, Ruhollah Taghizadeh-Mehrjardi
Summary: This study aims to delineate soil management zones (MZs) based on different soil properties using machine learning methods, in order to achieve sustainable agricultural production by maximizing yields and minimizing environmental damage. A random forest model was applied to map soil properties based on environmental covariates, and the study identified four different MZs according to relationships between soil properties and environmental covariates. The ranking of zones in terms of soil fertility was MZ4 > MZ1 > MZ3 > MZ2 based on the investigated soil properties and the soil quality (SQ) map.
Article
Environmental Studies
Masoud Zolfaghari Nia, Mostafa Moradi, Gholamhosein Moradi, Ruhollah Taghizadeh-Mehrjardi
Summary: Spatial variability of soil properties is critical for soil resource planning, management, and exploitation. Different digital soil mapping models were used to estimate soil physicochemical properties in Maroon riparian forests and agricultural lands. The random forest model provided the best estimation for pH, nitrogen, potassium, and bulk density, while the cubist regression tree was more accurate for organic carbon, C:N ratio, phosphorous, and clay. Artificial neural networks showed the best results for calcium carbonate, sand, and silt contents. Geospatial information such as terrain and climate parameters, as well as satellite images, can be effectively used for soil property mapping. Specific machine learning models should be used for each soil property to ensure highly accurate maps.
