Soil Type Map Research Articles

Physicochemical characteristics of andisols and their correlation to potato yield based on land mapping units

The high phosphate retention in andisol soil is often associated with a decrease in potato yield. Additionally, identification of the physical and chemical characteristics of andisols and their correlation to potato yield through soil mapping units (SMUs) is necessary to facilitate field surveys. Therefore, this study aimed to identify the physicochemical characteristics of andisols with a focus on available phosphorus (P) and the correlation to potato production based on SMUs. The experiment was conducted in Karo District, North Sumatra, Indonesia, from July 2022 to February 2023. A descriptive-analytical method was used by overlaying maps of slope, soil types, and altitude until 10 SMUs were selected that were planted with potatoes. Soil physicochemical properties were identified, while Pearson correlation analysis was performed on available-P and potato yield using IBM SPSS software. The results showed that the silt and clay fractions positively correlated with available-P in andisols. All soil physical characteristics were categorized as very weak to weak in supporting potato yield. A total of four SMUs including 1, 5, 8, and 10 were found to have lower available-P and very low to moderate correlations with soil pH, organic-C, C:N ratio, cation exchange capacity (CEC), as well as total-P. Based on the results, potato yield could increase by 1.765 and 0.380 tons ha-1 through the addition of organic-C and C:N ratio in Karo District. Therefore, soil amendment is required as an alternative to improve andisols soil.

Assessment, Characterization, Identification and Prioritization of Major Constraints and Potentials of Three Selected Community Watersheds of Agarfa District, Bale Zone, Oromia, Ethiopia

The severity of land degradation due to different socioeconomic and biophysical pressures is a key problem encountered in watersheds. In addition, the lack of research-based references to a given community watershed, particularly with regard to socioeconomic and biophysical aspects, leads to the failure of the interventions. This study aimed to assess and characterize the socioeconomic and biophysical conditions of three selected (Oda Chefo, Wabe Seada, and Oda Nagelle) Community Watersheds of the Agarfa District of Bale Zone, Oromia, Ethiopia. Furthermore, it identifies and prioritizes constraints and opportunities for scientists, planners, intervention, impact analysis, and project performance evaluation. Communities' watersheds were selected and delineated, followed by household interviews, focus group discussions, and biophysical characterization to generate data. The study used a random sampling technique and a total of 121 sample sizes for socioeconomic data, watershed delineation, slope classification, soil type, and LULC classification map developed based on the preliminary outlet identified with the help of GPS reading and ArcGIS 10.5 software. The socioeconomic parameters were analyzed using SPSS version 20 software. The result of the baseline survey identified key constraints such as soil erosion, soil fertility decline, deforestation and climate change, feed and fodder shortage, livestock disease, human disease, unemployment, food insecurity, water shortage, lack of credit access, market, road, cooperatives, high input price, pest and disease, yield decline and lack of irrigation access. The results revealed that the availability of the labor force, local market accessibility, transport services, informal farmer cooperatives, livestock clinics, youth and women associations, and informal intuitions are the main opportunities in selected community watersheds. In conclusion, baseline surveying before any watershed management practice intervention plays ....

Safety Factor Analysis on the Stability of the Retaining Wall Structure in Cimahi City, Indonesia

Retaining wall is a construction built to hold the ground on the slopes. Retaining wall can be said to be safe if its safety factors have been taken into account.In this study will be calculated the security factor based on SNI 8640:2017 on the location to be built retaining wall located in the area of Cimahi City, West Java using the help of software with two-dimensional finite element method.Based on the soil type map, the majority of the Cimahi City area consist of Tuffaceous Clay and Sandy Clay soils, which are alluvial fan deposits of volcanic origin, with soil thickness ranging from 1 m to 5 m. Retaining wall structures using cyclopean concrete appear to be slimmer compared to those using stone masonry. The use of dolken wood piles as additional reinforcement in locations using cyclopean concrete can strengthen the structure on the sliding plane, thus preventing overturning. Based on the modeling, the use of this structure resulted in a safety factor value greater than 1.5, with a displacement of 20 cm.

Sedimentation Analysis Study in Lake Tondano Due to the Development of the Surrounding Area

Background Population growth has increased the need for food, putting great pressure on the environment. This has triggered economic development and land use alternation, which have a major impact on the hydrological cycle. Natural and human factors have impacted the water catchment area of uncontrolled land cover changes in Lake Tondano, which is shallow from a depth of 36 meters to 14 meters due to soil erosion. Objective This research emphasizes the assessment of the lake, considering the efforts of conservation and analyzing its influence on the erosion rate and shallowing of the lake. The impact of land use dynamics on sedimentation is essential in providing alternative efforts for land conservation and, subsequently, optimum utilization. Methods The analyzing process requires rainfall data and land use maps, including soil type maps and land slope data, while the process itself is assisted by spreadsheets and GIS. In addition, the Universal Soil Loss Equation (USLE) and Sediment Delivery Ratio (SDR) model deliver the expected result of the total sediment production entering the lake. Results The calculated total value of actual sediment production governs the thickness of sediment deposition at the bottom of the lake. Conclusion The research concludes that the thickness of sediment deposition at lake bottom can be resolved by the total value of actual sediment production, whereas the value is analyzed from the USLE and SDR models previously correlated to bathymetry measurement.

Soil Fertility Status of the Two Eruptions of Mount Tangkuban Parahu and Mount Tampomas in Tanjungsari District Sumedang Regency

Soil fertility is one of the important factors to optimize plant growth and productivity. This study aims to analyze the status of soil fertility through soil physical analysis (texture) soil chemistry (pH H2O and KCl, C-organic, cation exchange capacity, base saturation, P2O5, and K2O) in several land units of Tanjungsari District, Sumedang Regency. The method used to determine sample points is purposive sampling method by considering several land units, namely soil type map, soil parent material map, topography map (slope), land use map, and rainfall data. Soil fertility status is determined by the matching table method between the results of soil chemical analysis with soil fertility status index that refers to the Soil Research Center (PPT) 1995. The results of the matching table data analysis showed that the research area almost entirely included low fertility status criteria except for the pine forest land unit on a moderately steep slope including medium fertility status criteria. The factors of base saturation, k-potential (K2O), and C-organic are the limiting factors of soil fertility status in the Tanjungsari District of Sumedang Regency, including the low to medium category.

Biotope Mapping of San Mariano, Isabela for Forest Conservation and Management

Abstract Forests in San Mariano, Isabela, Philippines are invaluable resources facing threats from destructive activities and natural disturbances, resulting to significant forest loss. To address this challenge, this study introduces biotope mapping as a tool for forest conservation and management in San Mariano, Isabela. The goal is to identify and map the different biotopes in San Mariano, Isabela which helps in enhancing sustainable forest management strategies, ensuring the long-term preservation of forest ecosystems. By conducting site visits, utilizing remote sensing techniques and geographic information systems (GIS) analysis, the distribution and classification of biotopes in San Mariano are determined. Through overlaying the soil type map, land cover map, and landform map, 22 biotopes were determined, and each were visually presented by showing a section-elevation diagram of each biotope. Furthermore, an assessment was also done for each biotope providing a comprehensive understanding of the different biotopes within the study area. This also serves as a reference for prioritizing conservation efforts, establishing protected areas, implementing habitat restoration initiatives, and mitigating threats to forest ecosystems. The findings and recommendations of this study contribute to the promotion of effective forest conservation and management, ensuring the long-term preservation of the forested landscapes.

Modeling flood characteristics in the Krueng Keureuto river basin in North Aceh regency using the Jams/J2000 application

Abstract Significant land use changes in the Krueng Keureuto River basin exacerbate flooding annually. Despite intensive flood studies, there’s no mapped information on past and future flood inundation. Flood discharge parameters rely on empirical formulas, overlooking climate factors and flood event data, crucial in flood analysis due to climate change. To comprehensively understand flood characteristics, this research employs the JAMS/J2000 application on climatological data (2010-2020), land use, soil type, and geological maps. Using HRU-Web, Hydrologic Response Units (HRUs) are derived for J2000 modeling, producing four runoff components: overland flow, interflow, fast groundwater, and slow groundwater. Notably, the highest overland flow, or flood runoff, was 23.21 mm on December 26, 2014, with 40.69 mm/day rainfall. Interflow peaked at 6.72 mm on October 8, 2011, with 18.10 mm/day rainfall. Fast groundwater reached 1.28 mm on November 30, 2019, with 13.18 mm/day rainfall, and slow groundwater peaked at 1.04 mm on November 13, 2020, with 48.87 mm/day rainfall. This study provides comprehensive insights into the complex dynamics of flood characteristics in the Krueng Keureuto River basin.

Characteristics and potential of tidal swamplands for sorghum (Sorghum bicolor (L.) Moench) cultivation

Abstract The characterization and potential of tidal swamps as a source of sorghum crop production (Sorghum Bicolor L. Moech), is the first research conducted in tidal lands, considering that so far sorghum has been widely planted in dry land. Aims to identify the biophysical character of the land, and multiply the potential, and technology of sorghum cultivation in tidal lands. Conducted in Kapuas Regency, Central Kalimantan, from August 2022 - April 2023. Land suitability analysis refers to 1:250,000 scale maps, 1:50,000 scale soil type maps, and soil analysis as the basis for determining technological inputs for successful site-specific sorghum cultivation. The results showed the biophysical character of tidal land with pH <5, has low nutrient content, imperfect water management, and proven to have good potential for sorghum growth. The application of dolomite as much as 1000 kg/ha and manure 5000 kg/ha and 200 kg/ha urea, 100 kg/ha SP-36, and 50 kg/ha KCL according to the results of soil analysis, proved that sorghum can produce 3.07 t/ha in tidal land, and almost the same as production in a dry land.

IMPLEMENTATION OF MAPPING-BASED MACHINE LEARNING ALGORITHM AS NON-STRUCTURAL DISASTER MITIGATION TO DETECT LANDSLIDE SUSCEPTIBILITY IN TAKARI DISTRICT

This research is primarily dedicated to providing a comprehensive exposition of the methodology applied in the deployment of a cartographic-based machine learning algorithm designed for the precise identification of areas susceptible to landslides within the geographical confines of the Takari District.This research delves into the application of mapping-based machine learning algorithms in the domain of non-structural disaster mitigation, with a specific emphasis on the detection of landslide susceptibility within the Takari District. A range of machine learning algorithms, including Support Vector Machine, Naive Bayes Classifier, Ordinal Logistic Regression, Random Forest, and Decision Tree, were harnessed to evaluate rainfall data within the context of landslide susceptibility. An evaluation of model performance, anchored in accuracy and Kappa metrics, unveiled that both the Ordinal Logistic Regression and Random Forest models exhibited noteworthy precision, reaching a commendable 74.36%. Nevertheless, a meticulous examination of Kappa values disclosed the ascendancy of the Random Forest model, which achieved a superior Kappa value of 0.5397. As portrayed in the visual representation provided, it becomes manifest that the Random Forest algorithm's prognostications yield 66 instances of cloudy atmospheric conditions, 48 occurrences of light precipitation, and 3 episodes of moderate rainfall. These predictions are influenced by several factors, including average temperature, humidity levels, wind speed, duration of sunlight, and wind direction at maximum speed. Consequently, this comprehensive analysis underscores the Random Forest algorithm as the most efficacious model for landslide susceptibility prediction. Furthermore, the study seamlessly integrated overlay maps, encompassing the Slope Inclination Map of the Takari District, Geological Map of the Takari District, and Soil Type Map of the Takari District, to contribute to the formulation of a definitive map delineating the susceptibility to landslides in the Takari District. Furthermore, further research could conduct spatial validation of the model predictions using additional datasets or remote sensing data to validate the accuracy of the landslide susceptibility map and ensure its applicability across different geographical regions.

Evaluation of Land Suitability of The Post-Mine Area for Settlement in The Kuranji Region of Padang City

The increase in population in Padang City is related to the many needs that must be met by the community such as housing or settlement needs. The availability of land that meets the criteria to be used as a settlement location guarantees a decent life for each community. This research aims to evaluate the land suitability of post-mining areas for settlements in the Kuranji area of Padang City. Data collection is done by analyzing the parameters used in determining the ideal settlement area, namely, slope level map, soil type map, erosion hazard, soil texture, flood hazard, and soil carrying capacity. The data that has been obtained is analyzed using the scoring or weighting method to determine the level of land suitability. Based on the calculations and analysis carried out on the evaluation of land suitability for settlements in the Gunung Sarik area, it can be concluded that the post-mining area in the Gunung Sarik area is suitable for settlements with a land suitability level value of 57 which with a land suitability level value of 58.7 - 37.4 is appropriate with details of 6 physical parameters that are met, namely, the level of slope 15.76%; Alluvial soil type, erosion hazard 348.83 tons/day/year, never flooded, fine soil texture (Clay), and has good soil bearing capacity (165 kg/cm 2 ).

Determination of the Groundwater Potential Zones in Babylon Using Remote Sensing & GIS Techniques

Freshwater found underground in the tiny spaces (pores) of soil and rocks is known as groundwater. Groundwater is a valuable resource that considerably contributes to the yearly supplies. To preserve water quality and oversee groundwater systems, evaluation of the potential zone of groundwater recharge is crucial. Using remote sensing and Geographic Information System (GIS) tools, groundwater potential zones are identified. This study proposes to use satellite imagery and the software program (ArcGIS) to find a groundwater potential zone in Babylon City, Iraq. A groundwater map has been created using various data, namely slope, drainage density, lineament density, land use/land cover, geology type, soil type, and rainfall map. All these data are collected using a weighted overlay tool. Five categories were created based on the groundwater results: very poor, poor, moderate, good, and exceptional. The 'good' class covers about 32% of the study area. The current study shows that excellent groundwater class exists in Al-Hindiyah Dam, Al-Muhaweel, Al-Midhatiyah, Al-Kifil, and Al-Mashrua’a. The excellent groundwater class in Al-Shommali covers approximately 198.0054 km2. Certain areas like Al-Musaib Center, Al-Hilla Center, Al-Hashimiyah, and Abi Garaq don't have much groundwater. The moderate and excellent classes cover around 19% of the study area. The main conclusion is that Remote Sensing and GIS have gained recognition as a practical approach to mapping groundwater potential zones due to their ability to integrate diverse spatial data.

Extracting Typical Samples Based on Image Environmental Factors to Obtain an Accurate and High-Resolution Soil Type Map

Soil surveying and mapping provide important support for environmental science research on soil and other resources. Due to the rapid change in land use and the long update cycle of soil maps, historical conventional soil maps (CSMs) may be outdated and have low accuracy. Therefore, there is an urgent need for accurate and up-to-date soil maps. Soil has a high correlation with its corresponding environmental factors in space, and typical samples contain an appropriate soil–environment relationship of soil types. Understanding how to extract typical samples according to environmental factors and determine the implied soil–environment relationship is the key to updating soil maps. In this study, a hierarchical typical sample extraction method based on land use type and environmental factors was designed. According to the corresponding relationship between the soil type and the land use type (ST-LU), the outdate soil map patches caused by changes in land use were excluded, follow by typical samples being extracted according to the peak intervals of the soil–environmental factor histograms. Additionally, feature selection was performed through variance analysis and mutual information, and four machine learning models were used to predict soil types. In addition, the influence of environmental factors on soil prediction was discussed, in terms of variable importance analysis. Using an overall common validation set, the results show that the prediction accuracy using typical samples for learning in the modeling set is above 0.8, while the prediction accuracy when using random samples is only about 0.4. Compared with the original soil map, the accuracy and resolution of the predicted soil maps based on typical samples are greatly improved. In general, typical samples can effectively explore the actual soil–environment knowledge implied in the soil type map. By extracting typical samples from historical soil type map and combining them with high-resolution remote sensing data, we can generate new soil type maps with high accuracy and short update cycle. This can provide some references for typical sampling design and soil type prediction.

Flood hazard mapping using a GIS-based morphometric analysis approach in arid regions, a case study in the Red Sea Region, Egypt

Flash floods are a major threat to life and properties in arid regions. In recent decades, Egypt has experienced severe flash floods that have caused significant damage across the country, including the Red Sea region. The aim of this study is to map the flood hazards in flood-prone areas along the Red Sea region using a Geographic Information System (GIS)-based morphometric analysis approach. To evaluate the flood hazard degree, the adopted methodology considers various morphometric parameters such as basin area, slope, sinuosity index, shape factor, drainage intensity, circularity ratio, and curve number. GIS techniques were employed to delineate the watershed and the drainage network. The delineated watershed was used together with the digitized maps of soil and land use types to estimate the curve number and the morphometric parameters for each subbasin. The flood hazard degrees are calculated based on the considered morphometric parameters and distinguished based on a five-degree scale ranging from very low to very high. Results indicate that 47% of the study area has a very high flood hazard degree. Furthermore, morphometric analysis results align with the runoff results simulated by a hydrological model, where, for example, basins with a high to very high hazard degree exhibited high runoff. This suggests the influence of physical characteristics on the hydrological behavior of the watershed and further validates the morphometric analysis presented in this work. The results presented here can help policy planners and decision-makers develop appropriate measures to mitigate flash floods and achieve sustainable development in arid regions.

Explorative analysis of varying spatial resolutions on a soil type classification model and it's transferability in an agricultural lowland area of Lombardy, Italy

In Digital Soil Mapping (DSM), assessing the transferability of soil type classification models across different spatial resolutions is a pivotal step in ensuring their robustness and applicability to diverse terrains. This study investigates the impact of spatial resolutions on soil type mapping within an intensively used agricultural lowland region in Lombardy, Italy, based on a Random Forest algorithm. Employing Digital Elevation Models (DEMs) at resolutions of 5 m, 10 m, and 25 m, this study aims to identify the optimal spatial resolution for accurate soil type maps and explores the transferability of models across different resolutions. The nested Leave-One-Out Cross-Validation (nested-LOOCV) results indicate a substantial impact of resolution on model performance, with higher resolutions demonstrating superior accuracy. The model developed at 10 m resolution emerges as the most robust performer, achieving an overall accuracy of 40.3%. Model transferability analysis reveals challenges when transitioning from finer to coarser resolutions, while models at coarser resolutions adapt favourably to higher resolution data. The implications extend to DSM, emphasizing the need for careful consideration of spatial resolution in model development and transfer. The findings provide valuable insights for researchers and practitioners, urging tailored approaches based on the scale and objectives of the study area. The study encourages future research to focus on advanced techniques enhancing model transferability within DSM. Overall, this research contributes to the optimization of soil classification models, advancing our understanding of soil taxonomy in agriculturally vital lowland areas.

Analysis of potential soil degradation on agricultural land based on geographic information systems in Slogohimo District, Wonogiri Regency

Slogohimo District is one of the districts located in Wonogiri Regency with an area of 6.415 ha. Population growth in Slogohimo District has increased by 7,068 people from 2017 to 2022. The total population in 2022 is 53,403 people with the majority livelihood being farmers. Population increase will have an impact on increasing the demand for biomass. Land management that does not pay attention to conservation principles will cause changes in the soil’s basic properties and impact the biomass produced. About 84,91% of the land in Slogohimo District is used as agricultural land in paddy fields, moor, and plantations. The increase in population and the need for biomass, triggers more intensive processing of agricultural land which has the potential to cause soil degradation potential degradation. Based on these problems, it is important to carry out an assessment of the potential for degradation to agricultural land in Slogohimo District. The method used is overlaying thematic maps consisting of soil type maps, rainfall maps, slope maps, and land use maps through spatial data analysis based on Geographic Information Systems (GIS). The potential for soil degradation is obtained from the scoring results which refer to the standard criteria in the Technical Guidelines for Preparation of Soil Degradation Degradation Status Maps for Biomass Production. The results showed that potential degradation to the soil on agricultural land in Slogohimo District was classified into three classes by area or proportion of land, namely low class covering 1.558,08 ha, medium class covering 2.800,70 ha, and high class covering 919,93 ha.

Flood Vulnerability and Resiliency in Coastal Areas Based on Geographic Information Systems (GIS) and Dynamic

Floods are a disaster that is very detrimental and has an extensive impact, so it needs to be managed. GIS conducted this research and a dynamic system to model flood hazards and vulnerabilities in Kijang, Bintan City, and analyze flood hazards and vulnerabilities, so the purpose of this research is to map Flood Vulnerability and Resilience in Coastal Areas. The method used in this research is the Qualitative Descriptive method. Qualitative Descriptive Analysis of Survey Data and Interviews with the Community The interview data is used to validate the flood hazard analysis. Superimpose analysis using GIS, resulting in the condition of each indicator. To obtain the infiltration map, rainfall data processing, contour maps, and soil type maps are needed. Using weighting and scoring, vulnerability analysis was then analyzed, resulting in a flood-prone map. The results of this study show that high inundation caused by uncontrolled land use and flood hazards strongly influences flood proneness when the regulations are implemented or adhered to. Scenarios 1 and 2 from human resources analyzed the policy's application of Regional Regulations, which significantly regulate land use control. Local regulations are vital in regulating land use control; therefore, less flood vulnerability will occur when implemented or adhered to. Contributed to the state of mind of local regulations by providing a clearer definition and understanding, the assessment will help develop detailed risk reduction, mitigation, and management plans in determining more appropriate flood resilience indicators for policymakers.

Characteristics of soil physical and chemical properties on Arabica coffee land in Bener Meriah Regency

Bener Meriah Regency is one of the Arabica coffee producing areas in Aceh Province. This Arabica coffee has been cultivating for years by the Gayo people with a continuous cultivation system throughout the year. Continuous cultivation for years causes a decrease in soil fertility in both physical and chemical properties, resulting in a decrease in coffee production. This study aims to determine the physical and chemical characteristics of the soil on Arabica coffee land in Bener Meriah Regency. The study used a quantitative descriptive method through land surveys and analysis of data in the field and analysis of soil samples in the laboratory. Observations were made on 15 Land Units Type (LUT) obtained from the overlay of elevation maps, slope maps, soil type maps and existing coffee plantation maps of Bener Meriah Regency. Parameters observed included soil structure, soil permeability, soil aggregate stability, soil pH, total and available P. The results showed that the soil structure was classified as crumb, soil permeability was slow to fast, soil aggregate stability was classified as unstable to rather stable, soil pH was acid to rather acid, P total was very low to very high, and P available was very low.

A fine digital soil mapping by integrating remote sensing-based process model and deep learning method in Northeast China

Accurate soil type maps provide an important basis for agricultural decision-making and land degradation control. In soil classification studies, the various environmental covariates are often selected based on the soil-forming framework. Since the mapping area and available observation data are limited, meteorological and vegetation cover factors have not been fully developed, and their role in soil classification needs to be evaluated. In addition, whether deep learning has out-performance in soil classification remains to be tested. The aim of this paper is to evaluate the accuracy of deep learning modelling techniques in classifying soil type using different combinations of input variables, and evaluate the importance of soil-forming variables in soil type classification. Therefore, we collected commonly used environmental covariates in Northeast China (NEC), including multiple meteorological factors and adopted a satellite-based biophysical model (Boreal Ecosystem Productivity Simulator, BEPS) to enrich vegetation cover factors. Next, four modeling strategies were developed: the soil-forming factors of soil and relief were considered as traditional environmental covariates (T), as well as combined with meteorologic variables (T + C), vegetation cover variables (T + V) and all available environmental covariates (T + C + V). Then, the effectiveness of different modeling strategies for soil classification was explored with convolutional neural network (CNN) model and multi-layer random forest (MRF) model based on soil separability. Finally, a 30 m resolution soil type map was established. The results demonstrated that both MRF and CNN can achieve high accuracy soil classification, while the CNN model performs better. The descending order of classification accuracy based on different modeling strategies of the CNN model is shown as T + C + V: 91.08%, T + V: 88.84%, T + C: 86.82%, and T: 83.96%. Meanwhile, the separability of different soil-forming factors for soil classification is soil properties, vegetation cover, temporal variation, meteorologic and relief in descending order. For Castanozems and Brown soils, MRF has higher classification accuracy, while CNN has better performance in Meadow soils and Fluvo-aquic soils. The methodology proposed in this paper aims to achieve high accuracy soil classification, provide an approach to understand the importance of soil-forming factors for the region, as well as for different soil types, and provide references for facilitating the interpretation of misclassified areas. Our results are accurate in the core areas, and therefore, this work facilitates researchers to be able to focus more on areas where different soil types intersect, thus significantly improves efficiency and saves resources, and promises to be a useful tool for future soil surveys.

Estimation of Soil Erosion Hazard in The Simpo Subwatershed, Indonesia

River siltation caused by sedimentation in the downstream area of Simpo River, Juli Sub-district, Bireuen Regency, is suspected to occur due to increased land erosion. The siltation leads to a decrease in river capacity and triggers overflow (flooding) during the rainy season. This affects residential areas, plantations, and tourist areas around the river. This research aims to estimate the rate of land erosion and map the Erosion Hazard Level (EHL) occurring in the Simpo Subwatershed. The estimation is carried out with the Universal Soil Loss Equation (USLE) method based on the Geographic Information System (GIS). The data used includes 10 years of monthly rainfall data, soil type maps, Digital Elevation Model (DEM) data, and land cover maps. Based on the calculation results, the rate of land erosion is found to be 52592.9 tons/ha/year. Furthermore, the erosion hazard in the Simpo Subwatershed ranges from low to high levels. The EHL in the subwatershed is dominated by the High-Moderate category, reaching 38.1%. Most of the areas affected by this erosion category are spatially located in the upstream area with moderately steep slopes (15–25%), primarily consisting of plantation areas. This indicates that soil conservation measures need to be adopted in the region.

Estimating Soil Strength Using GIS-Based Maps - A case study in Sweden

Soil strength is an important parameter for planning of forest roads and harvesting operations. Locating roads to areas with high soil strength reduce both build and maintenance costs. Locating logging trails to high strength areas minimise soil disturbances, e.g., rutting and compaction of forest soils. GIS-based maps of soil type and soil moisture can be valuable tools to estimate soil strength. The aim of this study was to evaluate the use of soil moisture map, i.e., depth-to-water (DTW), maps and soil type maps, to estimate soil strength expressed as California bearing ratio (CBR). CBR, volumetric water content, and ground penetration depth were measured in 120 sample points, separated on three soil classes (clay-silt sediments, sand sediments, glacial till) and two soil moisture classes (wet, dry). In each point, soil samples were collected for validation of the soil type maps. There was a high conformance between soil moisture predicted by DTW maps and field measurements, but conformance of the soil type between maps and field estimates varied between soil types. For sediment soils, dry soils were consistently stronger than wet soils. Soil strength of glacial till soils was more complicated with a binary CBR distribution depending on soil stoniness. Glacial till soils possible to penetrate to 20 cm depth with the dynamic cone penetrometer had CBR values close to those for sand sediments. There is a potential to estimate soil strength from DTW and soil type maps, but these variables should preferably be complemented with other data.