ISPRS International Journal of Geo-Information

Research

23 pages, 7138 KiB

Open AccessArticle

Hybrid Machine Learning Approach for Gully Erosion Mapping Susceptibility at a Watershed Scale

by Sliman Hitouri, Antonietta Varasano, Meriame Mohajane, Safae Ijlil, Narjisse Essahlaoui, Sk Ajim Ali, Ali Essahlaoui, Quoc Bao Pham, Mirza Waleed, Sasi Kiran Palateerdham and Ana Cláudia Teodoro

ISPRS Int. J. Geo-Inf. 2022, 11(7), 401; https://doi.org/10.3390/ijgi11070401 - 14 Jul 2022

Cited by 29 | Viewed by 4087

Abstract

Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem [...] Read more.

Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of “where and why” gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

24 pages, 13485 KiB

Open AccessArticle

Enhancing the Accuracy of Land Cover Classification by Airborne LiDAR Data and WorldView-2 Satellite Imagery

by Chun-Ta Wei, Ming-Da Tsai, Yu-Lung Chang and Ming-Chih Jason Wang

ISPRS Int. J. Geo-Inf. 2022, 11(7), 391; https://doi.org/10.3390/ijgi11070391 - 12 Jul 2022

Cited by 2 | Viewed by 2147

Abstract

The Full Waveform LiDAR system has been developed and used commercially all over the world. It acts to record the complete time of a laser pulse and has a high-resolution sampling interval compared to the traditional multiple-echo LiDAR, which only provides signals within [...] Read more.

The Full Waveform LiDAR system has been developed and used commercially all over the world. It acts to record the complete time of a laser pulse and has a high-resolution sampling interval compared to the traditional multiple-echo LiDAR, which only provides signals within a single target range. This study area mainly collects data from Riegl LMS-Q680i Full Waveform LiDAR and WorldView-2 satellite imagery, which focuses on buildings, vegetation, grassland, asphalt roads and other ground types as the surface objects. The amplitude and pulse width are selected as waveform basic parameters. The parameter of topography is slope, and the height classification parameters of the test ground are 0–0.5 m, 0.5–2.5 m, and 2.5 m. To eliminate noise, the neighborhood average is applied on the LiDAR parameter values and analyzed as the classification accuracy comparison. This survey uses Decision Tree as the classification method. Comparing the data between neighborhood average and non-neighborhood average, the data classification accuracy improves by 7%, and Kappa improves by 5.92%. NDVI image data are utilized to distinguish the artificial from natural ground. The results show that the neighborhood average with previous data can improve the classification accuracy by 5%, and Kappa improves by 4.25%. By adding NIR-2 of WorldView-2 satellite imagery to the neighborhood average analysis, the overall classification accuracy is improved by 2%, and the Kappa value by 1.21%. This article shows that utilizing the analysis of neighborhood average and image parameters can effectively improve the classification accuracy of land covers. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

15 pages, 5370 KiB

Open AccessArticle

Crop Identification Based on Multi-Temporal Active and Passive Remote Sensing Images

by Hebing Zhang, Hongyi Yuan, Weibing Du and Xiaoxuan Lyu

ISPRS Int. J. Geo-Inf. 2022, 11(7), 388; https://doi.org/10.3390/ijgi11070388 - 11 Jul 2022

Cited by 8 | Viewed by 3621

Abstract

Although vegetation index time series from optical images are widely used for crop mapping, it remains difficult to obtain sufficient time-series data because of satellite revisit time and weather in some areas. To address this situation, this paper considered Wen County, Henan Province, [...] Read more.

Although vegetation index time series from optical images are widely used for crop mapping, it remains difficult to obtain sufficient time-series data because of satellite revisit time and weather in some areas. To address this situation, this paper considered Wen County, Henan Province, Central China as the research area and fused multi-source features such as backscatter coefficient, vegetation index, and time series based on Sentinel-1 and -2 data to identify crops. Through comparative experiments, this paper studied the feasibility of identifying crops with multi-temporal data and fused data. The results showed that the accuracy of multi-temporal Sentinel-2 data increased by 9.2% compared with single-temporal Sentinel-2 data, and the accuracy of multi-temporal fusion data improved by 17.1% and 2.9%, respectively, compared with multi-temporal Sentinel-1 and Sentinel-2 data. Multi-temporal data well-characterizes the phenological stages of crop growth, thereby improving the classification accuracy. The fusion of Sentinel-1 synthetic aperture radar data and Sentinel-2 optical data provide sufficient time-series data for crop identification. This research can provide a reference for crop recognition in precision agriculture. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

15 pages, 6782 KiB

Open AccessArticle

Sub-Surface Geotechnical Data Visualization of Inaccessible Sites Using GIS

by Tariq Ahmed Awan, Muhammad Usman Arshid, Malik Sarmad Riaz, Moustafa Houda, Mirvat Abdallah, Muhammad Shahkar, Mirsina Mousavi Aghdam and Marc Azab

ISPRS Int. J. Geo-Inf. 2022, 11(7), 368; https://doi.org/10.3390/ijgi11070368 - 29 Jun 2022

Cited by 8 | Viewed by 2628

Abstract

Geotechnical investigation, in hilly areas, for high-rise projects, becomes a problematic issue and costly process due to difficulties in mobilization and assembling the drilling equipment on mountainous terrains. The objective of this study is to map soil properties of study areas, especially at [...] Read more.

Geotechnical investigation, in hilly areas, for high-rise projects, becomes a problematic issue and costly process due to difficulties in mobilization and assembling the drilling equipment on mountainous terrains. The objective of this study is to map soil properties of study areas, especially at inaccessible sites, for reconnaissance. Digital soil maps for Tehsil Murree, Pakistan, have been developed using the emerging Geographical Information System (GIS). The research work involved the creation of an exhaustive database, by collecting and rectifying geotechnical data, followed by the digitization of the acquired data through integration with GIS, in an attempt to visualize, analyze and interpret the collected geotechnical information spatially. The soil data of 205 explanatory holes were collected from the available geotechnical investigation (GI) reports. The collection depth of soil samples, which were initially used for the design of deep and shallow foundations by different soil consultancies in the Murree area, was approximately 50 ft. below ground level. Appropriate spatial interpolation methods (i.e., the Kriging) were applied for the preparation of smooth surface maps of soil standard penetration tester number values, soil type and plasticity index. The accuracy of developed SPT N value and plasticity maps were then evaluated using the linear regression method, in which the predicted values of soil characteristics from developed maps and actual values were compared. SPT N value maps were developed up to a depth of 9.14 m below ground level and at every 1.52 m interval. The depth of refusal was considered in the developed maps. Soil type and plasticity maps were generated up to 15.24 m depth, again at every 1.52 m intervals, using color contours, considering the maximum predicted foundation depth for high-rise projects. The study has implications for academics and practitioners to map the soil properties for inaccessible sites using GIS, as the resulting maps have high accuracy. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

19 pages, 6543 KiB

Open AccessArticle

Integration of Geostatistical and Sentinal-2AMultispectral Satellite Image Analysis for Predicting Soil Fertility Condition in Drylands

by Mohamed S. Shokr, Yasser S. A. Mazrou, Mostafa A. Abdellatif, Ahmed A. El Baroudy, Esawy K. Mahmoud, Ahmed M. Saleh, Abdelaziz A. Belal and Zheli Ding

ISPRS Int. J. Geo-Inf. 2022, 11(6), 353; https://doi.org/10.3390/ijgi11060353 - 17 Jun 2022

Cited by 2 | Viewed by 2790

Abstract

For modelling and predicting soil indicators to be fully operational and facilitate decision-making at any spatial level, there is a requirement for precise spatially referenced soil information to be available as input data. This paper focuses on showing the capacity of Sentinal-2A(S2A) multispectral [...] Read more.

For modelling and predicting soil indicators to be fully operational and facilitate decision-making at any spatial level, there is a requirement for precise spatially referenced soil information to be available as input data. This paper focuses on showing the capacity of Sentinal-2A(S2A) multispectral imaging to predict soil properties and provide geostatistical analysis (ordinary kriging) for mapping dry land soil fertility conditions (SOCs). Conditioned Latin hypercube sampling was used to select the representative sampling sites within the study area. To achieve the objectives of this work, 48 surface soil samples were collected from the western part of Matrouh Governorate, Egypt, and pH, soil organic matter (SOM), available nitrogen (N), phosphorus (P), and potassium (K) levels were analyzed. Multilinear regression (MLR) was used to model the relationship between image reflectance and laboratory analysis (of pH, SOM, N, P, and K in the soil), followed by mapping the predicted outputs using ordinary kriging. Model fitting was achieved by removing variables according to the confidence level (95%).Around 30% of the samples were randomly selected to verify the validity of the results. The randomly selected samples helped express the variety of the soil characteristics from the investigated area. The predicted values of pH, SOM, N, P, and K performed well, with R² values of 0.6, 0.7, 0.55, 0.6, and 0.92 achieved for pH, SOM, N, P, and K, respectively. The results from the ArcGIS model builder indicated a descending fertility order within the study area of: 70% low fertility, 22% moderate fertility, 3% very low fertility, and 5% reference terms. This work evidence that which can be predicted from S2A images and provides a reference for soil fertility monitoring in drylands. Additionally, this model can be easily applied to environmental conditions similar to those of the studied area. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

17 pages, 4812 KiB

Open AccessArticle

Improving LST Downscaling Quality on Regional and Field-Scale by Parameterizing the DisTrad Method

by Taha I. M. Ibrahim, Sadiq Al-Maliki, Omar Salameh, István Waltner and Zoltán Vekerdy

ISPRS Int. J. Geo-Inf. 2022, 11(6), 327; https://doi.org/10.3390/ijgi11060327 - 30 May 2022

Cited by 6 | Viewed by 2329

Abstract

Many scientists have been investigating Land Surface Temperature (LST) because of its relevance in water management science due to its direct influence on the hydrological water cycle. This effect stems from being one of the most significant variables influencing evapotranspiration. One of the [...] Read more.

Many scientists have been investigating Land Surface Temperature (LST) because of its relevance in water management science due to its direct influence on the hydrological water cycle. This effect stems from being one of the most significant variables influencing evapotranspiration. One of the most important reasons for the evapotranspiration retrieved from MODIS data’s limited suitability for scheduling and planning irrigation schemes is the lack of spatial resolution. As a result, high-resolution LST is required for estimating evapotranspiration. The goal of this study is to improve the resolution of the available LST data, to improve evapotranspiration (ET_a) estimation using statistical downscaling with Normalized Difference Vegetation Index (NDVI) as a predictor. The DisTrad (Disaggregation of Radiometric Surface Temperature) method was used for the LST downscaling procedure, which is based on aggregating the NDVI map to the LST map resolution and then calculating the coefficient of variation of the native NDVI map within the aggregated pixel and classifying the aggregated map into three classes: NDVI < 0.2 for the bare soil, 0.2 ≤ NDVI ≤ 0.5 for the partial vegetation, and NDVI > 0.5 for the full vegetation. DisTrad uses 25% of the pixels with the lowest coefficient of variation from each class to calculate the regression coefficients. In this work, adjustments to the DisTrad method were implemented to enhance downscaling LST and to examine the impacts of that alteration on the evapotranspiration estimation. The linear regression model was tested as an alternative to the original second-order polynomial. In using 10% of the pixels instead of the originally proposed 25% with the lowest coefficient of variation values, it is assumed that a group of pixels with a lower coefficient of variation represents a more homogeneous area, thus it gives more accurate values. The downscaled LST map retrieval was validated using Landsat 8 thermal maps (100 m). Applying the modified DisTrad approach to disaggregate Landsat LST to 30 m (NDVI resolution) yielded an R² of 0.72 for the 10%, 0.74 for the 25% and 0.61 for the second-order polynomial lowest coefficient of variation compared to native LST Landsat, which means that 10% can be used as an alternative. Applying the downscaled LST map to estimate ET_a yielded R² 0.84 in both cases, compared to ET_a yielded from the native Landsat LST. These results prove that using the robust linear regression provided better results than using polynomial regression. With the downscaled Land Surface Temperature data, it was possible to create detailed ET_a maps of the small agricultural fields in the test area. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

12 pages, 6769 KiB

Open AccessArticle

Fine Crop Classification Based on UAV Hyperspectral Images and Random Forest

by Zhihua Wang, Zhan Zhao and Chenglong Yin

ISPRS Int. J. Geo-Inf. 2022, 11(4), 252; https://doi.org/10.3390/ijgi11040252 - 12 Apr 2022

Cited by 23 | Viewed by 3309

Abstract

The classification of unmanned aerial vehicle hyperspectral images is of great significance in agricultural monitoring. This paper studied a fine classification method for crops based on feature transform combined with random forest (RF). Aiming at the problem of a large number of spectra [...] Read more.

The classification of unmanned aerial vehicle hyperspectral images is of great significance in agricultural monitoring. This paper studied a fine classification method for crops based on feature transform combined with random forest (RF). Aiming at the problem of a large number of spectra and a large amount of calculation, three feature transform methods for dimensionality reduction, minimum noise fraction (MNF), independent component analysis (ICA), and principal component analysis (PCA), were studied. Then, RF was used to finely classify a variety of crops in hyperspectral images. The results showed: (1) The MNF–RF combination was the best ideal classification combination in this study. The best classification accuracies of the MNF–RF random sample set in the Longkou and Honghu areas were 97.18% and 80.43%, respectively; compared with the original image, the RF classification accuracy was improved by 6.43% and 8.81%, respectively. (2) For this study, the overall classification accuracy of RF in the two regions was positively correlated with the number of random sample points. (3) The image after feature transform was less affected by the number of sample points than the original image. The MNF transform curve of the overall RF classification accuracy in the two regions varied with the number of random sample points but was the smoothest and least affected by the number of sample points, followed by the PCA transform and ICA transform curves. The overall classification accuracies of MNF–RF in the Longkou and Honghu areas did not exceed 0.50% and 3.25%, respectively, with the fluctuation of the number of sample points. This research can provide reference for the fine classification of crops based on UAV-borne hyperspectral images. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

17 pages, 4140 KiB

Open AccessArticle

Prediction and Uncertainty Capabilities of Quantile Regression Forests in Estimating Spatial Distribution of Soil Organic Matter

by Melpomeni Nikou and Panagiotis Tziachris

ISPRS Int. J. Geo-Inf. 2022, 11(2), 130; https://doi.org/10.3390/ijgi11020130 - 11 Feb 2022

Cited by 11 | Viewed by 4363

Abstract

One of the core tasks in digital soil mapping (DSM) studies is the estimation of the spatial distribution of different soil variables. In addition, however, assessing the uncertainty of these estimations is equally important, something that a lot of current DSM studies lack. [...] Read more.

One of the core tasks in digital soil mapping (DSM) studies is the estimation of the spatial distribution of different soil variables. In addition, however, assessing the uncertainty of these estimations is equally important, something that a lot of current DSM studies lack. Machine learning (ML) methods are increasingly used in this scientific field, the majority of which do not have intrinsic uncertainty estimation capabilities. A solution to this is the use of specific ML methods that provide advanced prediction capabilities, along with innate uncertainty estimation metrics, like Quantile Regression Forests (QRF). In the current paper, the prediction and the uncertainty capabilities of QRF, Random Forests (RF) and geostatistical methods were assessed. It was confirmed that QRF exhibited outstanding results at predicting soil organic matter (OM) in the study area. In particular, R² was much higher than the geostatistical methods, signifying that more variation is explained by the specific model. Moreover, its uncertainty capabilities as presented in the uncertainty maps, shows that it can also provide a good estimation of the uncertainty with distinct representation of the local variation in specific parts of the area, something that is considered a significant advantage, especially for decision support purposes. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

23 pages, 4702 KiB

Open AccessArticle

Spectral Index for Mapping Topsoil Organic Matter Content Based on ZY1-02D Satellite Hyperspectral Data in Jiangsu Province, China

by Yayu Yang, Kun Shang, Chenchao Xiao, Changkun Wang and Hongzhao Tang

ISPRS Int. J. Geo-Inf. 2022, 11(2), 111; https://doi.org/10.3390/ijgi11020111 - 4 Feb 2022

Cited by 13 | Viewed by 3333

Abstract

Estimation of soil organic matter content (SOMC) is essential for soil quality evaluation. Compared with traditional multispectral remote sensing for SOMC mapping, the distribution of SOMC in a certain area can be obtained quickly by using hyperspectral remote sensing data. The Advanced Hyper-Spectral [...] Read more.

Estimation of soil organic matter content (SOMC) is essential for soil quality evaluation. Compared with traditional multispectral remote sensing for SOMC mapping, the distribution of SOMC in a certain area can be obtained quickly by using hyperspectral remote sensing data. The Advanced Hyper-Spectral Imager (AHSI) onboard the ZY1-02D satellite can simultaneously obtain spectral information in 166 bands from visible (400 nm) to shortwave infrared (2500 nm), providing an important data source for SOMC mapping. In this study, SOMC-related spectral indices (SIs) suitable for this satellite were analyzed and evaluated in Shuyang County, Jiangsu Province. A series of SIs were constructed for the bare soil and vegetation-covered (mainly rice crops and tree seedlings) areas by combining spectral transformations (such as reciprocal and square root) and dual-band index formulas (such as ratio and difference), respectively. The optimal SIs were determined based on Pearson’s correlation coefficient (

ρ

) and satellite data quality, and applied to SOMC level mapping and estimation. The results show that: (1) The SI with the highest

ρ

in the bare soil area is the ratio index of original reflectance at 654 and 679 nm (OR-RI_(654,679)), whereas the SI in the vegetation area is the square root of the difference between the reciprocal reflectance at 551 and 1998 nm (V-RR-DSI_(551,1998)); (2) the spatial distribution trend of regional SOMC results obtained by linear regression models of OR-RI_(654,679) and V-RR-DSI_(551,1998) is consistent with the samples; (3) based on the optimal SIs, support vector machine and tree ensembles were used to predict the SOMC of bare soil and vegetation-covered areas of Shuyang County, respectively. The determination coefficient of the soil–vegetation combined prediction results is 0.775, the root mean square error is 3.72 g/kg, and the residual prediction deviation is 2.12. The results show that the proposed SIs for ZY1-02D satellite hyperspectral data are of great potential for SOMC mapping. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

18 pages, 39649 KiB

Open AccessArticle

Downscaling of AMSR-E Soil Moisture over North China Using Random Forest Regression

by Hongyan Zhang, Shudong Wang, Kai Liu, Xueke Li, Zhengqiang Li, Xiaoyuan Zhang and Bingxuan Liu

ISPRS Int. J. Geo-Inf. 2022, 11(2), 101; https://doi.org/10.3390/ijgi11020101 - 1 Feb 2022

Cited by 10 | Viewed by 2913

Abstract

Satellite retrieval can offer global soil moisture information, such as Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data. AMSR-E has been used to provide soil moisture all over the world, with a coarse resolution of 25 km × 25 km. The coarse resolution [...] Read more.

Satellite retrieval can offer global soil moisture information, such as Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data. AMSR-E has been used to provide soil moisture all over the world, with a coarse resolution of 25 km × 25 km. The coarse resolution of the soil moisture dataset often hinders its use in local or regional research. This work proposes a new framework based on the random forest (RF) model while using five auxiliary data to downscale the AMSR-E soil moisture data over North China. The downscaled results with a 1 km spatial resolution are verified against in situ measurements. Compared with AMSR-E data, the correlation coefficient of the downscaled data is increased by 0.17, and the root mean squared error, mean absolute error, and unbiased root mean square error are reduced by 0.02, 0.01, and 0.03 m³/m³, respectively. In addition, the comparison results with Multiple Linear Regression and Support Vector Regression downscaled data show that the proposed method significantly outperforms the other two methods. The feasibility of our model is well supported by the importance analysis and leave-one-out analysis. Our study, which combines RF with spatiotemporal search algorithms and efficient auxiliary data, may provide insights into soil moisture downscaling in large areas with various surface characteristics and climatic conditions. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

15 pages, 2810 KiB

Open AccessArticle

Assessment and Mapping Soil Water Erosion Using RUSLE Approach and GIS Tools: Case of Oued el-Hai Watershed, Aurès West, Northeastern of Algeria

by Aida Bensekhria and Rabah Bouhata

ISPRS Int. J. Geo-Inf. 2022, 11(2), 84; https://doi.org/10.3390/ijgi11020084 - 24 Jan 2022

Cited by 20 | Viewed by 4460

Abstract

The problem of soil water erosion is one of the primary causes of agro-pedological heritage degradation. The combined effect of natural factors and inappropriate human actions has weakened the soil, which seriously threatens the region’s fertile lands and soils, which can ultimately lead [...] Read more.

The problem of soil water erosion is one of the primary causes of agro-pedological heritage degradation. The combined effect of natural factors and inappropriate human actions has weakened the soil, which seriously threatens the region’s fertile lands and soils, which can ultimately lead to an irreversible situation of desertification. This study focuses on analysis and mapping of the vulnerability to erosion in Oued el-Hai watershed, Algeria, based on a technical methodology that combines the universal soil loss equation (USLE) with the geographic information system (GIS) tools. The results are organized into three main classes of different rate values, from one area to another, depending on the influence of different factors that control the erosion process. The highest loss rate value is greater than 30 t·ha⁻¹·yr⁻¹ and covers 23.2% of the total area, mainly located in the mountainous areas with steep slopes. However, the minimum potential erosion rate value is mainly located on the plain, with an average of 10 t·ha⁻¹·yr⁻¹ covering 45.2% of the total area of the watershed. The estimate of potential water erosion has given alarming results. The total area of the watershed could lose a rate of 16.69 t·ha⁻¹·yr⁻¹ (on average) each year. The method and results described in this article are valuable for understanding the soil erosion risk and are useful for managing and planning land use that will avoid land degradation. Hence, the results of this study are considered an important document which constitutes a decision support tool in terms of the management and protection of natural resources. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Graphical abstract

21 pages, 15666 KiB

Open AccessArticle

A Time Series Investigation to Assess Climate Change and Anthropogenic Impacts on Quantitative Land Degradation in the North Delta, Egypt

by Mohamed A. E. AbdelRahman, Ahmed A. Afifi and Antonio Scopa

ISPRS Int. J. Geo-Inf. 2022, 11(1), 30; https://doi.org/10.3390/ijgi11010030 - 31 Dec 2021

Cited by 17 | Viewed by 4029

Abstract

In the current study the processes of soil deterioration over the past five decades was evaluated. Land degradation risk, status, and rate were assessed in Kafr El-Sheikh Governorate, Egypt, in 2016 using OLI and ETM (2002) remote sensing data, and soil data from [...] Read more.

In the current study the processes of soil deterioration over the past five decades was evaluated. Land degradation risk, status, and rate were assessed in Kafr El-Sheikh Governorate, Egypt, in 2016 using OLI and ETM (2002) remote sensing data, and soil data from 1961.A quantitative deterioration was produced based on the comparative study approach in the integrated weighted sum, weighted overlay, and fuzzy model. The parameters used were soil depth, texture, pH, EC, OM, SAR, ESP, CEC, CaCO₃, BD, N, P, K. The variables were based on the measurements derived from the Universal Soil Loss Equation (USLE). The results of the implemented USLE in the GIS model-builder revealed the prevalence of severe soil deterioration processes in the region, and include four main deterioration risks: water-logging, soil compaction, salinization, and alkalization. During 2002–2016, soil sealing took place on 36,297.87 ha of the study area (9.7% of the total area). Urban sprawl was one of the most noticed problems that became apparent during the fieldwork during the inventory of land resources in the investigation area. Soil sealing is one of the hidden manifestations of desertification, and it is the implicit explanation for the lost land for the agricultural production process. The study showed that the investigated soil, as a part of north Nile Delta, is a very fragile system that needs to be protected, especially under the effect of climate change in areas overloaded with population, and because of their negative effects on soil properties. According to the results of this study, it is recommended that the same approach be applied to similar agricultural semi-arid regions to help in building a database of land resources for agricultural use that will be very useful for the decision-maker to monitor changes on agricultural lands. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

13 pages, 2125 KiB

Open AccessArticle

Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

by Prashant K. Srivastava, George P. Petropoulos, Rajendra Prasad and Dimitris Triantakonstantis

ISPRS Int. J. Geo-Inf. 2021, 10(8), 507; https://doi.org/10.3390/ijgi10080507 - 27 Jul 2021

Cited by 8 | Viewed by 2216

Abstract

Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused [...] Read more.

Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

12 pages, 2883 KiB

Open AccessArticle

The Soil Nutrient Digital Mapping for Precision Agriculture Cases in the Trans-Ural Steppe Zone of Russia Using Topographic Attributes

by Azamat Suleymanov, Evgeny Abakumov, Ruslan Suleymanov, Ilyusya Gabbasova and Mikhail Komissarov

ISPRS Int. J. Geo-Inf. 2021, 10(4), 243; https://doi.org/10.3390/ijgi10040243 - 7 Apr 2021

Cited by 24 | Viewed by 4380

Abstract

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness [...] Read more.

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R², the best predictive models were obtained for content of nitrogen (R² = 0.74), SOC (R² = 0.66), and potassium (R² = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

23 pages, 15205 KiB

Open AccessArticle

New Insight on Soil Loss Estimation in the Northwestern Region of the Zagros Fold and Thrust Belt

by Arsalan Ahmed Othman, Ahmed K. Obaid, Diary Ali Mohammed Amin Al-Manmi, Ahmed F. Al-Maamar, Syed E. Hasan, Veraldo Liesenberg, Ahmed T. Shihab and Younus I. Al-Saady

ISPRS Int. J. Geo-Inf. 2021, 10(2), 59; https://doi.org/10.3390/ijgi10020059 - 1 Feb 2021

Cited by 17 | Viewed by 3714

Abstract

Soil loss is one of the most important causes of land degradation. It is an inevitable environmental and socio-economic problem that exists in many physiographic regions of the world, which, besides other impacts, has a direct bearing on agricultural productivity. A reliable estimate [...] Read more.

Soil loss is one of the most important causes of land degradation. It is an inevitable environmental and socio-economic problem that exists in many physiographic regions of the world, which, besides other impacts, has a direct bearing on agricultural productivity. A reliable estimate of soil loss is critical for designing and implementing any mitigation measures. We applied the widely used Revised Universal Soil Loss Equation (RUSLE) in the Khabur River Basin (KhRB) within the NW part of the Zagros Fold and Thrust Belt (ZFTB). The areas such as the NW Zagros range, characterized by rugged topography, steep slope, high rainfall, and sparse vegetation, are most susceptible to soil erosion. We used the Digital Elevation Model (DEM) of the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), and Landsat imagery to estimate annual soil loss using the RUSLE model. In addition, we estimated sediment yield (SY) at sub-basin scale, in the KhRB where a number of dams are planned, and where basic studies on soil erosion are lacking. Estimation of SY will be useful in mitigation of excessive sedimentation affecting dam performance and watershed management in this region. We determined the average annual soil loss and the SY in the KhRB to be 11.16 t.ha⁻¹.y⁻¹ and 57.79 t.ha⁻¹.y⁻¹, respectively. The rainfall and runoff erosivity (R factor), slope length (L factor), and slope steepness (S factor), are the three main factors controlling soil loss in the region. This is the first study to determine soil loss at the sub-basin scale along with identifying suitable locations for check dams to trap the sediment before it enters downstream reservoirs. The study provides valuable input data for design of the dams to prevent excessive siltation. This study also aims at offering a new approach in relating potential soil erosion to the actual erosion and hypsometric integrals. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

17 pages, 4491 KiB

Open AccessArticle

Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

by Kieu Anh Nguyen, Walter Chen, Bor-Shiun Lin and Uma Seeboonruang

ISPRS Int. J. Geo-Inf. 2021, 10(1), 42; https://doi.org/10.3390/ijgi10010042 - 19 Jan 2021

Cited by 37 | Viewed by 5000

Abstract

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble [...] Read more.

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

14 pages, 8502 KiB

Open AccessArticle

Correlation between Geochemical and Multispectral Patterns in an Area Severely Contaminated by Former Hg-As Mining

by Carlos Boente, Lorena Salgado, Emilio Romero-Macías, Arturo Colina, Carlos A. López-Sánchez and José Luis R. Gallego

ISPRS Int. J. Geo-Inf. 2020, 9(12), 739; https://doi.org/10.3390/ijgi9120739 - 10 Dec 2020

Cited by 10 | Viewed by 3231

Abstract

In the context of soil pollution, plants suffer stress when exposed to extreme concentrations of potentially toxic elements (PTEs). The alterations to the plants caused by such stressors can be monitored by multispectral imagery in the form of vegetation indices, which can inform [...] Read more.

In the context of soil pollution, plants suffer stress when exposed to extreme concentrations of potentially toxic elements (PTEs). The alterations to the plants caused by such stressors can be monitored by multispectral imagery in the form of vegetation indices, which can inform pollution management strategies. Here we combined geochemistry and remote sensing techniques to offer a preliminary soil pollution assessment of a vast abandoned spoil heap in the surroundings of La Soterraña mining site (Asturias, Spain). To study the soil distribution of the PTEs over time, twenty-seven soil samples were randomly collected downstream of and around the main spoil heap. Furthermore, the area was covered by an unmanned aerial vehicle (UAV) carrying a high-resolution multispectral camera with four bands (red, green, red-edge and near infrared). Multielement analysis revealed mercury and arsenic as principal pollutants. Two indices (from a database containing up to 55 indices) offered a proper correlation with the concentration of PTEs. These were: CARI2, presenting a Pearson Coefficient (PC) of 0.89 for concentrations >200 mg/kg of As; and NDVIg, PC of −0.67 for >40 mg/kg of Hg. The combined approach helps prediction of those areas susceptible to greatest pollution, thus reducing the costs of geochemical campaigns. Full article

(This article belongs to the Special Issue Integrating GIS and Remote Sensing in Soil Mapping and Modeling)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Integrating GIS and Remote Sensing in Soil Mapping and Modeling

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (17 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI