Using XVIII–XIX Cent. Maps and Modern Remote Sensing Data for Detecting the Changes in the Land Use at Bulgarian Fortified Settlements in the Volga Region

Иванов, М. А.; Abdullin, Halim M.; Gainullin, Iskander; Gafurov, Artur; Usmanov, Bulat M.; Williamson, James

doi:10.3390/earth2010004

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…We optimized the RF parameters for two response variables, SOM and response variables (Landsat bands [1][2][3][4][5][6][7], bands 10 and 11, DEM, NDVI, SAVI, and BI), using the OOB error estimate. The optimization process focuses on two key parameters: ntree, which refers to the total number of trees generated by the algorithm, and mtry, which pertains to the number of random input parameters used for constructing each tree.…”

Section: Rfmentioning

confidence: 99%

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Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

Khalaf,

Mustafa,

Fayyadh

2023

Applied Sciences

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Soil organic matter (SOM) is an essential component of soil fertility that plays a vital role in the preservation of healthy ecosystems. This study aimed to produce an SOM-level map of the Batifa region in northern Iraq. Random forest (RF) and extreme gradient boosting (XGBoost) models were used to predict the SOM spatial distribution. A total of 96 soil samples were collected from the surface layer (0–30 cm) of both cropland and soil areas in Batifa. In addition, remote sensing data were obtained from Landsat 8, including bands 1–7, 10, and 11. Supplementary variables such as the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), brightness index (BI), and digital elevation model (DEM) were employed as tools to predict SOM levels across the region. To evaluate the accuracy of the RF and XGBoost models in predicting SOM levels, statistical metrics, including mean absolute error (MAE), root mean square error (RMSE), and determination coefficient (R2), were used, with 80% of the data used for prediction and 20% for validation. The findings of this study revealed that the XGBoost model exhibited higher accuracy (MAE = 0.41, RMSE = 0.62, and R2 = 0.92) in predicting SOM than the RF model (MAE = 0.65, RMSE = 0.96, R2 = 0.79). Band 10, DEM, SAVI, and NDVI were identified as the most important predictors for both the models. The methodology employed in this study, which utilizes machine learning models, has the potential to map SOM in similar settings. Furthermore, the results offer significant insights for the stakeholders involved in soil management, thereby facilitating the enhancement of agricultural techniques.

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Section: Rfmentioning

confidence: 99%

“…Remote sensing has been an essential data source for mapping soil and research for several decades [3]. Wilcox et al [4] demonstrated that a linear association can be created between data obtained from remote sensing and soil organic carbon content.…”

Section: Introductionmentioning

confidence: 99%

Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

Khalaf,

Mustafa,

Fayyadh

2023

Applied Sciences

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The “Country of cities” web-GIS: development experience and approaches used in creating a history-oriented geoportal

Gafurov

Usmanov

Yermolayev

et al. 2021

ICIGIS

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Providing convenient access to spatial information for multiple users is a big challenge. To solve this problem, researchers present the results of their research on thematic geoportals—geographic information systems (GIS) located on the Web. The main functionality of such web-GIS, in addition to directly displaying interactive spatial information, is the possibility of making custom searches, measuring lengths and areas, and providing access to related multimedia materials. Archaeological geoportals, unlike other thematic GIS, require special approaches to the implementation, as there is a necessity to provide convenient access not only to spatial information, but also a gallery of multimedia materials, as well as detailed descriptions. This paper describes the approach to the development of cartographic web-resource “Country of Cities” which provides access to the results of interdisciplinary research of Volga Bulgarian settlements. for user-friendly access to both cartographic and descriptive materials, it was decided to divide the geoinformation and analytical component into two separate but interconnected portals within one web resource. Web-GIS is based on the leaflet library and provides interactive access, besides the information about each ancient settlement itself, to the series of analytical maps—risk map of the development of exogenous processes, risk map of the destruction of ancient settlements, their present state, and many others. The main graphic and descriptive materials are presented on the web portal, where a special page was created for each ancient settlement with a descriptive block, a gallery with images of the settlement from an unmanned aerial vehicle, thematic and historical maps, as well as an interactive three-dimensional textured model of the corresponding archaeological site.

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Using XVIII–XIX Cent. Maps and Modern Remote Sensing Data for Detecting the Changes in the Land Use at Bulgarian Fortified Settlements in the Volga Region

Cited by 2 publications

References 23 publications

Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach

The “Country of cities” web-GIS: development experience and approaches used in creating a history-oriented geoportal

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