A Case-study for Comparing a Mobile DC System and a Multi-configuration EMI Sensor for Depth-to-soil Mapping

This paper presents a techno-economic assessment for a unique Isolated Hybrid Power System (IHPS) design which could be used for remote areas isolated from the grid which also has the capability of being operated as a smart the hybrid energy system considering solar and wind energy sources for the purpose of street lighting. Solar-Wind Street light is an intelligent, small scale, and off grid LED lighting system. The modelling design and simulations were based on Simulations conducted using the Data collected and HOMER Energy Planning and Design software tools. Its components are solar panel, wind generator system (PVC blowers), Dynamo, LDRs, battery, LED light, charge controller. The energy stored in battery during day time due to solar panel, get extracted by LEDs during the night time (because LDRs get activated due to absence of sun light). Wind also charges the batteries due to wind which is used for glowing street light. The advantage of this idea is to avoid daily running cost and make the system purely off-grid. In this prototype, we have used 12V DC system to supply energy to the lights.

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Comparing digital soil mapping techniques for organic carbon and clay content: Case study in Burundi's central plateaus

CATENA ◽

10.1016/j.catena.2017.04.003 ◽

2017 ◽

Vol 156 ◽

pp. 161-175 ◽

Cited By ~ 13

Author(s):

Anicet Sindayihebura ◽

Sam Ottoy ◽

Stefaan Dondeyne ◽

Marc Van Meirvenne ◽

Jos Van Orshoven

Keyword(s):

Organic Carbon ◽

Clay Content ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Mapping Techniques

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Evaluating the Relative Importance of Legacy Soil Sampling and Spatial Models in Digital Soil Mapping Performances: A Case Study in Languedoc-Roussillon (Southern France)

Springer Environmental Science and Engineering - Digital Soil Mapping Across Paradigms, Scales and Boundaries ◽

10.1007/978-981-10-0415-5_23 ◽

2016 ◽

pp. 287-290

Author(s):

Philippe Lagacherie ◽

Kévin Vaysse

Keyword(s):

Spatial Models ◽

Soil Sampling ◽

Soil Mapping ◽

Digital Soil Mapping ◽

Relative Importance ◽

Southern France

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Soil Mapping Based on the Integration of the Similarity-Based Approach and Random Forests

Land ◽

10.3390/land9060174 ◽

2020 ◽

Vol 9 (6) ◽

pp. 174

Author(s):

Desheng Wang ◽

A-Xing Zhu

Keyword(s):

Random Forests ◽

Cross Validation ◽

Soil Mapping ◽

High Quality ◽

Integrated Method ◽

Average Accuracy ◽

Soil Information ◽

Fold Cross Validation

Digital soil mapping (DSM) is currently the primary framework for predicting the spatial variation of soil information (soil type or soil properties). Random forests and similarity-based methods have been used widely in DSM. However, the accuracy of the similarity-based approach is limited, and the performance of random forests is affected by the quality of the feature set. The objective of this study was to present a method for soil mapping by integrating the similarity-based approach and the random forests method. The Heshan area (Heilongjiang province, China) was selected as the case study for mapping soil subgroups. The results of the regular validation samples showed that the overall accuracy of the integrated method (71.79%) is higher than that of a similarity-based approach (58.97%) and random forests (66.67%). The results of the 5-fold cross-validation showed that the overall accuracy of the integrated method, similarity-based approach, and random forests range from 55% to 72.73%, 43.48% to 69.57%, and 54.17% to 70.83%, with an average accuracy of 66.61%, 57.39%, and 59.62%, respectively. These results suggest that the proposed method can produce a high-quality covariate set and achieve a better performance than either the random forests or similarity-based approach alone.

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Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

Land ◽

10.3390/land9090319 ◽

2020 ◽

Vol 9 (9) ◽

pp. 319 ◽

Cited By ~ 1

Author(s):

Mohamed Ali Mohamed

Keyword(s):

Urban Areas ◽

Human Impacts ◽

Soil Mapping ◽

Fuzzy Classification ◽

Small Scale ◽

Ground Truth Data ◽

Terrain Attributes ◽

Lidar Dem

In this study, a knowledge-based fuzzy classification method was used to classify possible soil-landforms in urban areas based on analysis of morphometric parameters (terrain attributes) derived from digital elevation models (DEMs). A case study in the city area of Berlin was used to compare two different resolution DEMs in terms of their potential to find a specific relationship between landforms, soil types and the suitability of these DEMs for soil mapping. Almost all the topographic parameters were obtained from high-resolution light detection and ranging (LiDAR)-DEM (1 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-DEM (30 m), which were used as thresholds for the classification of landforms in the selected study area with a total area of about 39.40 km2. The accuracy of both classifications was evaluated by comparing ground point samples as ground truth data with the classification results. The LiDAR-DEM based classification has shown promising results for classification of landforms into geomorphological (sub)categories in urban areas. This is indicated by an acceptable overall accuracy of 93%. While the classification based on ASTER-DEM showed an accuracy of 70%. The coarser ASTER-DEM based classification requires additional and more detailed information directly related to soil-forming factors to extract geomorphological parameters. The importance of using LiDAR-DEM classification was particularly evident when classifying landforms that have narrow spatial extent such as embankments and channel banks or when determining the general accuracy of landform boundaries such as crests and flat lands. However, this LiDAR-DEM classification has shown that there are categories of landforms that received a large proportion of the misclassifications such as terraced land and steep embankments in other parts of the study area due to the increased distance from the major rivers and the complex nature of these landforms. In contrast, the results of the ASTER-DEM based classification have shown that the ASTER-DEM cannot deal with small-scale spatial variation of soil and landforms due to the increasing human impacts on landscapes in urban areas. The application of the approach used to extract terrain parameters from the LiDAR-DEM and their use in classification of landforms has shown that it can support soil surveys that require a lot of time and resources for traditional soil mapping.

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