scholarly journals Predicting Channel Conveyance and Characterizing Planform Using River Bathymetry via Satellite Image Compilation (RiBaSIC) Algorithm for DEM-Based Hydrodynamic Modeling

2020 ◽  
Vol 12 (17) ◽  
pp. 2799
Author(s):  
Md N M Bhuyian ◽  
Alfred Kalyanapu
Keyword(s):  
Mean Squared Error ◽  
Satellite Image ◽  
Shuttle Radar Topography Mission ◽  
Willamette River ◽  
Bathymetric Data ◽  
Digital Elevation ◽  
Channel Slope ◽  
Dem Accuracy ◽  
Landsat Satellite ◽  
Discharge Estimation

Digital Elevation Models (DEMs) are widely used as a proxy for bathymetric data and several studies have attempted to improve DEM accuracy for hydrodynamic (HD) modeling. Most of these studies attempted to quantitatively improve estimates of channel conveyance (assuming a non-braided morphology) rather than accounting for the actual channel planform. Accurate representation of river conveyance and planform in a DEM is critical to HD modeling and can be achieved with a combination of remote sensing (e.g., satellite image) and field data, such as water surface elevation (WSE). Therefore, the objectives of this study are (i) to develop an algorithm for predicting channel conveyance and characterizing planform via satellite images and in situ WSE and (ii) to estimate discharge using the predicted conveyance via an HD model. The algorithm is named River Bathymetry via Satellite Image Compilation (RiBaSIC) and uses Landsat satellite imagery, Shuttle Radar Topography Mission (SRTM) DEM, Multi-Error-Removed Improved-Terrain (MERIT) DEM, and observed WSE. The algorithm is tested on four study areas along the Willamette River, Kushiyara River, Jamuna River, and Solimoes River. Channel slope and predicted hydraulic radius are subsequently estimated for approximating Manning’s roughness factor. Two-dimensional HD models using DEMs modified by the RiBaSIC algorithm and corresponding Manning’s roughness factors are employed for discharge estimation. The proposed algorithm can represent river planform and conveyance in single-channeled, meandering, wandering, and braided river reaches. Additionally, the HD models estimated discharge within 14–19% relative root mean squared error (RRMSE) in simulation of five years period.

scholarly journals Geo-spatial Analysis of Farmland Affected by 2020 Flooding of River Rima, Northwestern Nigeria

2021 ◽  
pp. 20-27
Author(s):  
Abdullahi Muktar ◽  
Sadiq A. Yelwa ◽  
Muhammad Tayyib Bello ◽  
Wali Elekwachi
Keyword(s):  
Satellite Image ◽  
Shuttle Radar Topography Mission ◽  
Policy Makers ◽  
Professional Advice ◽  
Maximum Water Level ◽  
Digital Elevation ◽  
Farm Produce ◽  
Terra Modis ◽  
Elevation Model ◽  
The Impact

The flooding of River Rima is an annual issue affecting farmland located within the floodplains. This phenomena causes loss of farm produce and mass destruction of buildings, including roads and bridges in the area. Estimating the farmland affected by the flood will help the policy makers in decision making on how to mitigate the impact of flooding in the affected areas. The Terra/MODIS satellite image with 7-2-1 bands combination was used to classify the image into four landcover types. The area covered by flood was selected to calculate the flood area using Image Calculator module on QGIS software. The class of water was imposed on Digital Elevation Model that was obtained from Environmental Monitoring Satellite called The Shuttle Radar Topography Mission (SRTM). The result shows that River Rima flood occupies about 17,517 km2, equivalent to 1.7 million hectares of farmland that is below 230 meters (ASL). It was recommended that the local authorities and decision makers may use the flood map to showing flood risk zones so as to deter construction beyond the buffer. Farmers should adhere strictly to NiMet’s advice based on flood predictions. The civil engineers should also take note of the maximum water level during flooding so as to apply professional advice when constructing roads and bridges in the area.

Geoinformation carthography of the landscapes of the Mondy depression

2018 ◽  
Vol 934 (4) ◽  
pp. 23-30 ◽  
Author(s):  
E.A. Istomina ◽  
E.V. Ovchinnikova
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Digital Elevation ◽  
Elevation Model ◽  
Landsat Satellite ◽  
Landscape Map

A method of typological mapping of landscapes with the use of Landsat satellite images and the digital elevation model SRTM, as well as the method of factorial-dynamic classification of landscapes, was developed and a large-scale landscape map of the Mondy basin was created. At the first stage, the image was automatically classified using the neural network classification method, resulting in a picture divided into 11 classes. The resulting classified image was smoothed to remove the mosaic effect and translated into a vector map. For each unit obtained as a result of the classification of the satellite image, the following parameters were calculated by means of spatial analysis in the GIS

scholarly journals European primary forest database v2.0

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Francesco Maria Sabatini ◽  
Hendrik Bluhm ◽  
Zoltan Kun ◽  
Dmitry Aksenov ◽  
José A. Atauri ◽  
...  
Keyword(s):  
Conservation Planning ◽  
Human Impacts ◽  
Satellite Image ◽  
Primary Forest ◽  
Potential Natural Vegetation ◽  
Ecological Studies ◽  
Biogeographical Regions ◽  
Surrounding Landscape ◽  
Landsat Satellite ◽  
Primary Forests

AbstractPrimary forests, defined here as forests where the signs of human impacts, if any, are strongly blurred due to decades without forest management, are scarce in Europe and continue to disappear. Despite these losses, we know little about where these forests occur. Here, we present a comprehensive geodatabase and map of Europe’s known primary forests. Our geodatabase harmonizes 48 different, mostly field-based datasets of primary forests, and contains 18,411 individual patches (41.1 Mha) spread across 33 countries. When available, we provide information on each patch (name, location, naturalness, extent and dominant tree species) and the surrounding landscape (biogeographical regions, protection status, potential natural vegetation, current forest extent). Using Landsat satellite-image time series (1985–2018) we checked each patch for possible disturbance events since primary forests were identified, resulting in 94% of patches free of significant disturbances in the last 30 years. Although knowledge gaps remain, ours is the most comprehensive dataset on primary forests in Europe, and will be useful for ecological studies, and conservation planning to safeguard these unique forests.

scholarly journals Monitoring scrub weed change in the Canterbury region using satellite imagery

2007 ◽  
Vol 60 ◽  
pp. 137-140 ◽  
Author(s):  
J.D. Shepherd ◽  
J.R. Dymond ◽  
J.R.I. Cuff
Keyword(s):  
Satellite Imagery ◽  
Vegetation Cover ◽  
Satellite Image ◽  
Visual Assessment ◽  
Woody Vegetation ◽  
Spatial Change ◽  
Valid Data ◽  
Woody Cover ◽  
Landsat Satellite ◽  
Thematic Data

The spatial change of woody vegetation in the Canterbury region was automatically mapped between 1990 and 2001 using Landsat satellite image mosaics The intersection of valid data from these mosaics gave coverage of 84 of the Canterbury region Changes in woody cover greater than 5 ha were identified Of the 5 ha areas of woody change only those that were likely to have been a scrub change were selected using ancillary thematic data for current vegetation cover (eg afforestation and deforestation were excluded) This resulted in 2466 polygons of potential scrub change These polygons were rapidly checked by visual assessment of the satellite imagery and assigned to exotic or indigenous scrub change categories Between 1990 and 2001 the total scrub weed area in the Canterbury region increased by 3600 400 ha and indigenous scrub increased by 2300 400 ha

scholarly journals ANALYSIS OF INFLUENCE OF TERRAIN RELIEF ROUGHNESS ON DEM ACCURACY GENERATED FROM LIDAR IN THE CZECH REPUBLIC TERRITORY

2016 ◽  
Vol XLI-B4 ◽  
pp. 25-30 ◽  
Author(s):  
M. Hubacek ◽  
V. Kovarik ◽  
V. Kratochvil
Keyword(s):  
Czech Republic ◽  
Laser Scanning ◽  
Vegetation Coverage ◽  
The Czech Republic ◽  
Digital Elevation ◽  
Lower Accuracy ◽  
Check Points ◽  
Elevation Data ◽  
Dem Accuracy ◽  
Elevation Model

Digital elevation models are today a common part of geographic information systems and derived applications. The way of their creation is varied. It depends on the extent of area, required accuracy, delivery time, financial resources and technologies available. The first model covering the whole territory of the Czech Republic was created already in the early 1980's. Currently, the 5th DEM generation is being finished. Data collection for this model was realized using the airborne laser scanning which allowed creating the DEM of a new generation having the precision up to a decimetre. Model of such a precision expands the possibilities of employing the DEM and it also offers new opportunities for the use of elevation data especially in a domain of modelling the phenomena dependent on highly accurate data. The examples are precise modelling of hydrological phenomena, studying micro-relief objects, modelling the vehicle movement, detecting and describing historical changes of a landscape, designing constructions etc. <br><br> Due to a nature of the technology used for collecting data and generating DEM, it is assumed that the resulting model achieves lower accuracy in areas covered by vegetation and in built-up areas. Therefore the verification of model accuracy was carried out in five selected areas in Moravia. The network of check points was established using a total station in each area. To determine the reference heights of check points, the known geodetic points whose heights were defined using levelling were used. Up to several thousands of points were surveyed in each area. Individual points were selected according to a different configuration of relief, different surface types, and different vegetation coverage. The sets of deviations were obtained by comparing the DEM 5G heights with reference heights which was followed by verification of tested elevation model. Results of the analysis showed that the model reaches generally higher precision than the declared one in majority of areas. This applies in particular to areas covered by vegetation. By contrast, the larger deviations occurred in relation to the slope of the terrain, in particular in the micro-relief objects. The results are presented in this article.

scholarly journals Mapeamento e identificação de vetores responsáveis pela supressão do manguezal na Zona Costeira do Baixo Sul da Bahia, Brasil (Mapping and identification of vectors responsible for mangrove suppression in the Southern Bahia Lowlands, Brazil)

2020 ◽  
Vol 12 (7) ◽  
pp. 2503
Author(s):  
Ana Paula Sena de Souza ◽  
Ivonice Sena de Souza ◽  
George Olavo ◽  
Jocimara Souza Britto Lobão ◽  
Rafael Vinícius de São José
Keyword(s):  
Urban Areas ◽  
Satellite Image ◽  
Mangrove Ecosystem ◽  
Shrimp Farming ◽  
Mangrove Forests ◽  
Coverage Area ◽  
Southern Bahia ◽  
Global Accuracy ◽  
Landsat Satellite ◽  
Accuracy Indices

O ecossistema manguezal representa 8% de toda a linha de costa do planeta ocupando uma área total de 181.077 km2. O Brasil é o segundo país em extensão de áreas de manguezal, ficando atrás apenas da Indonésia. O objetivo do presente estudo foi mapear e identificar os principais vetores responsáveis pela supressão da cobertura das áreas de manguezal na região do Baixo Sul da Bahia, Brasil, a partir de imagens de satélite Landsat disponíveis para o período entre 1994 e 2017. Os mapeamentos foram realizados a partir de classificação supervisionada, utilizando o método Maxver. A acurácia da classificação obtida foi verificada através da verdade de campo, de índices de Exatidão Global, e dos coeficientes de concordância kappa e Tau. As classes que apresentaram maior área de cobertura no período analisado foram: vegetação ombrófila densa, agropecuária, solo exposto e manguezal. Foram identificados dois vetores principais responsáveis pela supressão dos bosques de mangue: a expansão desordenada das áreas urbanas (com destaque para o município de Valença) e o avanço da atividade de carcinicultura clandestina, devido a instalação de tanques de cultivo de camarão sem o devido processo de licenciamento ambiental (sobretudo no município de Nilo Peçanha). O uso das geotecnologias, em especial o Sensoriamento Remoto e os Sistemas de Informações Geográficas, foram ferramentas fundamentais na identificação destes vetores responsáveis pela supressão das áreas de manguezal na área de estudo região do Baixo Sul da Bahia.  Mapping and identification of vectors responsible for mangrove suppression in the Southern Bahia Lowlands, BrazilA B S T R A C TThe mangrove ecosystem represents 8% of the entire coastline of the planet and occupies a total area of 181,077 km2. Brazil is the second largest country in terms of mangrove areas, second only to Indonesia. The aim of the present study was to map and identify the main vectors responsible for the suppression of mangrove cover in the Southern Lowlands of Bahia, Brazil, from Landsat satellite images available for the period 1994-2017. based on supervised classification using the Maxver method. The accuracy of the classification obtained was verified through field truth, Global Accuracy indices, and kappa and Tau agreement coefficients. The classes that presented larger coverage area in the analyzed period were: dense ombrophilous vegetation, agriculture, exposed soil and mangrove. Two main vectors responsible for the suppression of mangrove forests were identified: the disorderly expansion of urban areas (especially the municipality of Valença) and the advance of clandestine shrimp farming due to the installation of shrimp farms without due environmental licensing process (mainly in the municipality of Nilo Peçanha). The use of geotechnologies, especially Remote Sensing and Geographic Information Systems, were fundamental tools in the identification of these vectors responsible for the suppression of mangrove areas in the study area of the Southern Bahia Lowlands.Key-words: environmental impacts, satellite image, shrimp farming.

Natural disaster mitigation of Sinabung Mountain by using landsat satellite image

2018 ◽  
Author(s):  
Togi Tampubolon ◽  
Rita Juliani ◽  
Muhammad Ali Thoha Harahap
Keyword(s):  
Natural Disaster ◽  
Satellite Image ◽  
Disaster Mitigation ◽  
Landsat Satellite

scholarly journals Identifikasi Kondisi Hutan Lindung Pulau Sebatik Kabupaten Nunukan Kalimantan Utara

2019 ◽  
Vol 20 (1) ◽  
pp. 1-5
Author(s):  
Husmul Beze ◽  
Suparjo
Keyword(s):  
Forest Cover ◽  
Satellite Image ◽  
Good Condition ◽  
Forest Area ◽  
Water Shortages ◽  
Community Water ◽  
The People ◽  
Drying Up ◽  
Landsat Satellite ◽  
Forest Function

In the last ten years the people on Sebatik Island have experienced water shortages. This happened because the forest which is the source of community water dried up. It is estimated that the drying up of these springs is due to changes in the function of forests as water reserves. This change in forest function occurs as a result of the process of clearing forests for plantations or other development activities. This is why it is necessary to analyze the protected forest cover on Sebatik Island. In this study, analysis of forest cover was carried out based on Landsat satellite imagery. To check the correctness of the analysis results on the satellite image, field checks are carried out. Based on the research results, the forest area on Sebatik Island has an area of ​​2,088.37 ha. The damaged forest is estimated to be 339.97ha, while the protected forest area which is still in good condition has an area of ​​1,748.40 ha.

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