Relief parameters and fuzzy logic for land evaluations of mango crops (Mangifera indica L.) in Colombia

The aim of this paper is to illustrate the use of digital elevation models (DEM) to calculate relief parameters and include them in suitability studies of land for mango crops in Colombia. Data from SRTM (Shuttle Radar Topography Mission) DEMs with 30 meter of spatial resolution and elevation in meters were used to calculate the slope, aspect, curvature, solar radiation, and topographic wetness index for inclusion in a land evaluation study. Fuzzy logic rules were developed and applied to define the degree of suitability by matching land use requirements with land characteristics. When integrated with geographic information systems, DEMs have significant potential for quantitatively defining and characterizing relief and for generating more detailed data to improve land evaluation processes. The Fuzzy logic proved to be a more realistic approach for evaluating the degree of land suitability than traditional bivalent logic, allowing for the use of membership degrees.

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A NEW METHOD TO DETECT REGIONS ENDANGERED BY HIGH WIND SPEEDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-59-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 59-63

Author(s):

P. Fischer ◽

S. Ehrensperger ◽

T. Krauß

Keyword(s):

Shuttle Radar Topography Mission ◽

Boosted Regression Trees ◽

Slope Aspect ◽

Maximum Wind ◽

Wind Speeds ◽

Landform Classification ◽

Area Of Interest ◽

Land Cover Data ◽

Digital Elevation ◽

Elevation Model

In this study we evaluate whether the methodology of Boosted Regression Trees (BRT) suits for accurately predicting maximum wind speeds. As predictors a broad set of parameters derived from a Digital Elevation Model (DEM) acquired within the Shuttle Radar Topography Mission (SRTM) is used. The derived parameters describe the surface by means of quantities (e.g. slope, aspect) and quality (landform classification). Furthermore land cover data from the CORINE dataset is added. The response variable is maximum wind speed, measurements are provided by a network of weather stations. The area of interest is Switzerland, a country which suits perfectly for this study because of its highly dynamic orography and various landforms.

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A NEW METHOD TO DETECT REGIONS ENDANGERED BY HIGH WIND SPEEDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-59-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 59-63

Author(s):

P. Fischer ◽

S. Ehrensperger ◽

T. Krauß

Keyword(s):

Shuttle Radar Topography Mission ◽

Boosted Regression Trees ◽

Slope Aspect ◽

Maximum Wind ◽

Wind Speeds ◽

Landform Classification ◽

Area Of Interest ◽

Land Cover Data ◽

Digital Elevation ◽

Elevation Model

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Digital elevation models (DEM) used to assess soil erosion risks: a case study in Boyaca, Colombia

Agronomía Colombiana ◽

10.15446/agron.colomb.v34n2.56145 ◽

2016 ◽

Vol 34 (2) ◽

pp. 239-249 ◽

Cited By ~ 1

Author(s):

Jeiner Yobany Buitrago E. ◽

Luis Joel Martínez M.

Keyword(s):

Slope Aspect ◽

Mountainous Area ◽

Stream Power ◽

Topographic Wetness Index ◽

Alos Palsar ◽

Erosion Risk ◽

Digital Elevation ◽

Stream Power Index ◽

Logic Functions ◽

Soil Surveys

The objective of this research was to develop a model for assessing the risk of erosion, exploring the potential of DEMs from SRTM, ASTER, ALOS PALSAR and one made with interpolation of a 1:25,000 contour map to calculate the variables of the relief that have greater impact on erosion. Several geomorphometric parameters, such as slope, aspect, profile and plan curvature, topographic wetness index, stream power index, and sediment transport capacity were computed from the DEM's elevation, some fuzzy logic functions proposed to evaluate the incidence of each parameter on erosion risk in a mountainous area of Colombia. The results showed that the use of DEM data is a relatively easy, uncostly method to identify, in a qualitative way, the risk of erosion and contribute to the enhancement of erosion information that is obtained with conventional general soil surveys.

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A Semi-Automated Object-Based Gully Networks Detection Using Different Machine Learning Models: A Case Study of Bowen Catchment, Queensland, Australia

Sensors ◽

10.3390/s19224893 ◽

2019 ◽

Vol 19 (22) ◽

pp. 4893 ◽

Cited By ~ 22

Author(s):

Hejar Shahabi ◽

Ben Jarihani ◽

Sepideh Tavakkoli Piralilou ◽

David Chittleborough ◽

Mohammadtaghi Avand ◽

...

Keyword(s):

Machine Learning ◽

Image Segmentation ◽

Vegetation Index ◽

Scale Parameter ◽

Slope Aspect ◽

Support Vector ◽

Topographic Wetness Index ◽

Slope Length ◽

Optimal Scale ◽

Object Based

Gully erosion is a dominant source of sediment and particulates to the Great Barrier Reef (GBR) World Heritage area. We selected the Bowen catchment, a tributary of the Burdekin Basin, as our area of study; the region is associated with a high density of gully networks. We aimed to use a semi-automated object-based gully networks detection process using a combination of multi-source and multi-scale remote sensing and ground-based data. An advanced approach was employed by integrating geographic object-based image analysis (GEOBIA) with current machine learning (ML) models. These included artificial neural networks (ANN), support vector machines (SVM), and random forests (RF), and an ensemble ML model of stacking to deal with the spatial scaling problem in gully networks detection. Spectral indices such as the normalized difference vegetation index (NDVI) and topographic conditioning factors, such as elevation, slope, aspect, topographic wetness index (TWI), slope length (SL), and curvature, were generated from Sentinel 2A images and the ALOS 12-m digital elevation model (DEM), respectively. For image segmentation, the ESP2 tool was used to obtain three optimal scale factors. On using object pureness index (OPI), object matching index (OMI), and object fitness index (OFI), the accuracy of each scale in image segmentation was evaluated. The scale parameter of 45 with OFI of 0.94, which is a combination of OPI and OMI indices, proved to be the optimal scale parameter for image segmentation. Furthermore, segmented objects based on scale 45 were overlaid with 70% and 30% of a prepared gully inventory map to select the ML models’ training and testing objects, respectively. The quantitative accuracy assessment methods of Precision, Recall, and an F1 measure were used to evaluate the model’s performance. Integration of GEOBIA with the stacking model using a scale of 45 resulted in the highest accuracy in detection of gully networks with an F1 measure value of 0.89. Here, we conclude that the adoption of optimal scale object definition in the GEOBIA and application of the ensemble stacking of ML models resulted in higher accuracy in the detection of gully networks.

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ON GENERATING DIGITAL ELEVATION MODELS FROM LIDAR DATA – RESOLUTION VERSUS ACCURACY AND TOPOGRAPHIC WETNESS INDEX INDICES IN NORTHERN PEATLANDS

Geodesy and Cartography ◽

10.3846/20296991.2012.702983 ◽

2012 ◽

Vol 38 (2) ◽

pp. 57-69 ◽

Cited By ~ 12

Author(s):

Abdulghani Hasan ◽

Petter Pilesjö ◽

Andreas Persson

Keyword(s):

Large Scale ◽

Drainage Area ◽

Data Sets ◽

Topographic Wetness Index ◽

Absolute Deviation ◽

Digital Elevation ◽

Elevation Data ◽

Scale Modelling ◽

Data Points ◽

Emission Modelling

Global change and GHG emission modelling are dependent on accurate wetness estimations for predictions of e.g. methane emissions. This study aims to quantify how the slope, drainage area and the TWI vary with the resolution of DEMs for a flat peatland area. Six DEMs with spatial resolutions from 0.5 to 90 m were interpolated with four different search radiuses. The relationship between accuracy of the DEM and the slope was tested. The LiDAR elevation data was divided into two data sets. The number of data points facilitated an evaluation dataset with data points not more than 10 mm away from the cell centre points in the interpolation dataset. The DEM was evaluated using a quantile-quantile test and the normalized median absolute deviation. It showed independence of the resolution when using the same search radius. The accuracy of the estimated elevation for different slopes was tested using the 0.5 meter DEM and it showed a higher deviation from evaluation data for steep areas. The slope estimations between resolutions showed differences with values that exceeded 50%. Drainage areas were tested for three resolutions, with coinciding evaluation points. The model ability to generate drainage area at each resolution was tested by pair wise comparison of three data subsets and showed differences of more than 50% in 25% of the evaluated points. The results show that consideration of DEM resolution is a necessity for the use of slope, drainage area and TWI data in large scale modelling.

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Water slope and discharge in the Amazon River estimated using the shuttle radar topography mission digital elevation model

Geophysical Research Letters ◽

10.1029/2005gl023836 ◽

2005 ◽

Vol 32 (17) ◽

Cited By ~ 110

Author(s):

Gina LeFavour ◽

Doug Alsdorf

Keyword(s):

Digital Elevation Model ◽

Shuttle Radar Topography Mission ◽

Amazon River ◽

Digital Elevation ◽

Elevation Model

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Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10050315 ◽

2021 ◽

Vol 10 (5) ◽

pp. 315

Author(s):

Hilal Ahmad ◽

Chen Ningsheng ◽

Mahfuzur Rahman ◽

Md Monirul Islam ◽

Hamid Reza Pourghasemi ◽

...

Keyword(s):

Machine Learning ◽

Land Cover ◽

Debris Flows ◽

Regional Scale ◽

Indus Basin ◽

Annual Rainfall ◽

Slope Aspect ◽

Topographic Wetness Index ◽

Susceptibility Maps ◽

Landslides And Debris Flows

The China–Pakistan Economic Corridor (CPEC) project passes through the Karakoram Highway in northern Pakistan, which is one of the most hazardous regions of the world. The most common hazards in this region are landslides and debris flows, which result in loss of life and severe infrastructure damage every year. This study assessed geohazards (landslides and debris flows) and developed susceptibility maps by considering four standalone machine-learning and statistical approaches, namely, Logistic Regression (LR), Shannon Entropy (SE), Weights-of-Evidence (WoE), and Frequency Ratio (FR) models. To this end, geohazard inventories were prepared using remote sensing techniques with field observations and historical hazard datasets. The spatial relationship of thirteen conditioning factors, namely, slope (degree), distance to faults, geology, elevation, distance to rivers, slope aspect, distance to road, annual mean rainfall, normalized difference vegetation index, profile curvature, stream power index, topographic wetness index, and land cover, with hazard distribution was analyzed. The results showed that faults, slope angles, elevation, lithology, land cover, and mean annual rainfall play a key role in controlling the spatial distribution of geohazards in the study area. The final susceptibility maps were validated against ground truth points and by plotting Area Under the Receiver Operating Characteristic (AUROC) curves. According to the AUROC curves, the success rates of the LR, WoE, FR, and SE models were 85.30%, 76.00, 74.60%, and 71.40%, and their prediction rates were 83.10%, 75.00%, 73.50%, and 70.10%, respectively; these values show higher performance of LR over the other three models. Furthermore, 11.19%, 9.24%, 10.18%, 39.14%, and 30.25% of the areas corresponded to classes of very-high, high, moderate, low, and very-low susceptibility, respectively. The developed geohazard susceptibility map can be used by relevant government officials for the smooth implementation of the CPEC project at the regional scale.

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A New Approach to the Quality Control of Slope and Aspect Classes Derived from Digital Elevation Models

Remote Sensing ◽

10.3390/rs13112069 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2069

Author(s):

M. V. Alba-Fernández ◽

F. J. Ariza-López ◽

M. D. Jiménez-Gamero

Keyword(s):

Quality Control ◽

Hypothesis Test ◽

Simulated Data ◽

Slope Aspect ◽

New Approach ◽

Reference Set ◽

Digital Elevation ◽

Using Data ◽

Elevation Model ◽

Degree Of Similarity

The usefulness of the parameters (e.g., slope, aspect) derived from a Digital Elevation Model (DEM) is limited by its accuracy. In this paper, a thematic-like quality control (class-based) of aspect and slope classes is proposed. A product can be compared against a reference dataset, which provides the quality requirements to be achieved, by comparing the product proportions of each class with those of the reference set. If a distance between the product proportions and the reference proportions is smaller than a small enough positive tolerance, which is fixed by the user, it will be considered that the degree of similarity between the product and the reference set is acceptable, and hence that its quality meets the requirements. A formal statistical procedure, based on a hypothesis test, is developed and its performance is analyzed using simulated data. It uses the Hellinger distance between the proportions. The application to the slope and aspect is illustrated using data derived from a 2×2 m DEM (reference) and 5×5 m DEM in Allo (province of Navarra, Spain).

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Testing PRISMA hyperspectral satellite imagery in predicting soil carbon content based on synthetized LUCAS spectral data

10.5194/egusphere-egu21-15450 ◽

2021 ◽

Author(s):

Zsófia Adrienn Kovács ◽

János Mészáros ◽

Mátyás Árvai ◽

Annamária Laborczi ◽

Gábor Szatmári ◽

...

Keyword(s):

Land Use ◽

Spectral Data ◽

Hyperspectral Imagery ◽

Slope Aspect ◽

Topographic Wetness Index ◽

Soil Carbon Content ◽

Space Agency ◽

Italian Space Agency ◽

Agricultural Areas ◽

Imagery Data

The estimation of the soil organic carbon (SOC) content plays an important role for carbon sequestration in the context of climate change and soil degradation. Reflectance spectroscopy has proven to be promising technique for SOC quantification in the laboratory and increasingly from air and spaceborne platforms, where hyperspectral imagery provides great potential for mapping SOC on larger scales.The PRISMA (PRecursore IperSpettrale della Missione Applicativa) is an earth-observation satellite with a medium spatial resolution hyperspectral radiometer onboard, developed and maintained by the Italian Space Agency.The Pan-European Land Use/ Land Cover Area Frame Survey (LUCAS) topsoil database contains soil physical, chemical and spectral data for most European countries. Based on the LUCAS points located in Hungary, a synthetized spectral dataset was created and matched to the spectral characteristic of PRISMA sensor, later used for building up machine learning based models (random forest, artificial neural network). SOC levels for the sample area was predicted using generated models and mainly PRISMA imagery.Our sample imagery data was generated from five consecutive, cloud-free PRISMA images covering 4500 km2 in the central part of the Great Plain in Hungary, which is one of the most important agricultural areas of the country, used mainly for crops on arable lands. The images were recorded in 2020 February when most croplands are not covered by vegetation therefore our tests were implemented on bare soils.We tested the prediction accuracy of hyperspectral imagery data supplemented by various environmental datasets as additional predictor variables in four scenarios: (i) using solely hyperspectral imagery data (ii) spectral imagery data, elevation and its derived parameters (e.g. slope, aspect, topographic wetness index etc.) (iii) spectral imagery data and land-use information and (iv) all aforementioned data in fusion.For validation two types of datasets were used: (i) measured data at the observation sites of the Hungarian Soil Information and Monitoring System and (ii) the recently compiled national SOC maps., which provides a suitable and formerly tested spatial representation of the carbon stock of the Hungarian soils.&#160;Acknowledgment: Our research was supported by the Cooperative Doctoral Programme for Doctoral Scholarships (1015642) and by the OTKA thematic research projects K-131820 and K-124290 of the Hungarian National Research, Development and Innovation Office and by the Scholarship of Human Resource Supporter (NTP-NFT&#214;-20-B-0022). Our project carried out using PRISMA Products, &#169; of the Italian Space Agency (ASI), delivered under an ASI License to use.

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Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms

Applied Sciences ◽

10.3390/app8081369 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1369 ◽

Cited By ~ 52

Author(s):

Alireza Arabameri ◽

Biswajeet Pradhan ◽

Hamid Reza Pourghasemi ◽

Khalil Rezaei ◽

Norman Kerle

Keyword(s):

Data Mining ◽

Spatial Relationship ◽

Area Under The Curve ◽

Regression Tree ◽

Drainage Density ◽

Gully Erosion ◽

Slope Aspect ◽

Topographic Wetness Index ◽

Boosted Regression Tree ◽

Area Index

Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region.

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