scholarly journals CHANGE DETECTION OF LAKE ABA SAMUEL IN ETHIOPIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 339-341
Author(s):  
R. Kaczynski ◽  
A. Rylko
Keyword(s):  
Supervised Classification ◽  
Local Population ◽  
Atmospheric Correction ◽  
Ground Truth ◽  
Landsat Tm ◽  
Aerial Photographs ◽  
Radiometric Calibration ◽  
Topographic Map ◽  
Ground Truth Data ◽  
Color Composite

Old topographic map published in 1975 elaborated from aerial photographs taken in 1972, Landsat TM data acquired in May 1986 and Landsat ETM+ from June 2002 have been used to assess the changes of the lake Aba Samuel in Ethiopia. First map of the lake has been done in the framework of UNDP project running in 1988-90 in the Ethiopian Mapping Authority. The second classification map has been done as M.Sc. thesis in the MUT in 2015. Supervised classification methods with the use of ground truth data have been used for elaboration of the Landsat TM data. From the year 1972 up to 1986 the area of the lake has decreased by 23%. From 1986 up to 2002 the area of the lake has decreased by 20%. Therefore, after 30 years the lake was smaller by 43%. This have had very bad influence on the lives of the local population. From other recent data in the period from 2002-2015 the lake has practically disappeared and now it is only a small part of the river Akaki. ENVI 5.2 and ERDAS IMAGINE 9.2 have been used for Radiometric Calibration, Quick Atmospheric Correction (QUAC) and supervised classification of Landsat ETM+ data. The Optimum Index Factor shows the best combination of Landsat TM and ETM+ bands for color composite as 1,4,5 in the color filters: B, G, R for the signature development. Methodology and final maps are enclosed in the paper.

scholarly journals CHANGE DETECTION OF LAKE ABA SAMUEL IN ETHIOPIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 339-341
Author(s):  
R. Kaczynski ◽  
A. Rylko
Keyword(s):  
Supervised Classification ◽  
Local Population ◽  
Atmospheric Correction ◽  
Ground Truth ◽  
Landsat Tm ◽  
Aerial Photographs ◽  
Radiometric Calibration ◽  
Topographic Map ◽  
Ground Truth Data ◽  
Color Composite

Old topographic map published in 1975 elaborated from aerial photographs taken in 1972, Landsat TM data acquired in May 1986 and Landsat ETM+ from June 2002 have been used to assess the changes of the lake Aba Samuel in Ethiopia. First map of the lake has been done in the framework of UNDP project running in 1988-90 in the Ethiopian Mapping Authority. The second classification map has been done as M.Sc. thesis in the MUT in 2015. Supervised classification methods with the use of ground truth data have been used for elaboration of the Landsat TM data. From the year 1972 up to 1986 the area of the lake has decreased by 23%. From 1986 up to 2002 the area of the lake has decreased by 20%. Therefore, after 30 years the lake was smaller by 43%. This have had very bad influence on the lives of the local population. From other recent data in the period from 2002-2015 the lake has practically disappeared and now it is only a small part of the river Akaki. ENVI 5.2 and ERDAS IMAGINE 9.2 have been used for Radiometric Calibration, Quick Atmospheric Correction (QUAC) and supervised classification of Landsat ETM+ data. The Optimum Index Factor shows the best combination of Landsat TM and ETM+ bands for color composite as 1,4,5 in the color filters: B, G, R for the signature development. Methodology and final maps are enclosed in the paper.

scholarly journals Practical example for use of the supervised vicarious calibration (SVC) method on multisource hyperspectral imagery data – ValCalHyp airborne hyperspectral campaign under the EUFAR framework

2014 ◽  
Vol XL-7 ◽  
pp. 51-53
Author(s):  
A. Brook ◽  
E. Ben Dor
Keyword(s):  
Atmospheric Correction ◽  
Ground Truth ◽  
Critical Issue ◽  
Radiometric Calibration ◽  
Vicarious Calibration ◽  
Flight Direction ◽  
Novel Approach ◽  
Novel Method ◽  
General Goal ◽  
Cross Calibration

A novel approach for radiometric calibration and atmospheric correction of airborne hyperspectral (HRS) data, termed supervised vicarious calibration (SVC) was proposed by Brook and Ben-Dor in 2010. The present study was aimed at validating this SVC approach by simultaneously using several different airborne HSR sensors that acquired HSR data over several selected sites at the same time. The general goal of this study was to apply a cross-calibration approach to examine the capability and stability of the SVC method and to examine its validity. This paper reports the result of the multi sensors campaign took place over Salon de Provenance, France on behalf of the ValCalHyp project took place in 2011. The SVC method enabled the rectification of the radiometric drift of each sensor and improves their performance significantly. The flight direction of the SVC targets was found to be a critical issue for such correction and recommendations have been set for future utilization of this novel method. The results of the SVC method were examined by comparing ground-truth spectra of several selected validation targets with the image spectra as well as by comparing the classified water quality images generated from all sensors over selected water bodies.

scholarly journals The Hyper-Angular Cube Concept for Improving the Spatial and Acoustic Resolution of MBES Backscatter Angular Response Analysis

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 446 ◽  
Author(s):  
Evangelos Alevizos ◽  
Jens Greinert
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Structure ◽  
Supervised Classification ◽  
Incidence Angle ◽  
Ground Truth ◽  
Response Analysis ◽  
Ground Truth Data ◽  
Angular Response ◽  
Artificial Neural

This study presents a novel approach, based on high-dimensionality hydro-acoustic data, for improving the performance of angular response analysis (ARA) on multibeam backscatter data in terms of acoustic class separation and spatial resolution. This approach is based on the hyper-angular cube (HAC) data structure which offers the possibility to extract one angular response from each cell of the cube. The HAC consists of a finite number of backscatter layers, each representing backscatter values corresponding to single-incidence angle ensonifications. The construction of the HAC layers can be achieved either by interpolating dense soundings from highly overlapping multibeam echo-sounder (MBES) surveys (interpolated HAC, iHAC) or by producing several backscatter mosaics, each being normalized at a different incidence angle (synthetic HAC, sHAC). The latter approach can be applied to multibeam data with standard overlap, thus minimizing the cost for data acquisition. The sHAC is as efficient as the iHAC produced by actual soundings, providing distinct angular responses for each seafloor type. The HAC data structure increases acoustic class separability between different acoustic features. Moreover, the results of angular response analysis are applied on a fine spatial scale (cell dimensions) offering more detailed acoustic maps of the seafloor. Considering that angular information is expressed through high-dimensional backscatter layers, we further applied three machine learning algorithms (random forest, support vector machine, and artificial neural network) and one pattern recognition method (sum of absolute differences) for supervised classification of the HAC, using a limited amount of ground truth data (one sample per seafloor type). Results from supervised classification were compared with results from an unsupervised method for inter-comparison of the supervised algorithms. It was found that all algorithms (regarding both the iHAC and the sHAC) produced very similar results with good agreement (>0.5 kappa) with the unsupervised classification. Only the artificial neural network required the total amount of ground truth data for producing comparable results with the remaining algorithms.

scholarly journals A study on landuse and landcover classification using microwave data in Joida taluk of Uttara Kannada district, Karnataka

2020 ◽  
Vol 12 (1) ◽  
pp. 9-12
Author(s):  
Arjun G. Koppad ◽  
Syeda Sarfin ◽  
Anup Kumar Das
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Data Sets ◽  
Radiometric Calibration ◽  
Ground Truth Data ◽  
Dense Forest ◽  
Sar Data

The study has been conducted for land use and land cover classification by using SAR data. The study included examining of ALOS 2 PALSAR L- band quad pol (HH, HV, VH and VV) SAR data for LULC classification. The SAR data was pre-processed first which included multilook, radiometric calibration, geometric correction, speckle filtering, SAR Polarimetry and decomposition. For land use land cover classification of ALOS-2-PALSAR data sets, the supervised Random forest classifier was used. Training samples were selected with the help of ground truth data. The area was classified under 7 different classes such as dense forest, moderate dense forest, scrub/sparse forest, plantation, agriculture, water body, and settlements. Among them the highest area was covered by dense forest (108647ha) followed by horticulture plantation (57822 ha) and scrub/Sparse forest (49238 ha) and lowest area was covered by moderate dense forest (11589 ha).   Accuracy assessment was performed after classification. The overall accuracy of SAR data was 80.36% and Kappa Coefficient was 0.76.  Based on SAR backscatter reflectance such as single, double, and volumetric scattering mechanism different land use classes were identified.

scholarly journals Forest and Industrial Plantation Mapping with Multi-Temporal Dual Polarization by ALOS PALSAR Data In Riau Province

2018 ◽  
Vol 2 (1) ◽  
pp. 35
Author(s):  
Elis Molidena ◽  
Takahiro Osawa ◽  
Putu Gede Ardhana ◽  
Abd. Rahman As-syakur
Keyword(s):  
Land Use ◽  
Ground Truth ◽  
Temporal Analysis ◽  
Alos Palsar ◽  
Ground Truth Data ◽  
Multi Temporal ◽  
Industrial Plantation ◽  
Changes Detection ◽  
Accuracy Result ◽  
Color Composite

Backscattering characteristics of land use has been analyzed using ALOS PALSAR data. The purpose of this research are mapping of land use by five categories such as forest, acacia, oil palm, open area and water, and to identify the changes of environmental. Analysis Pixel-by-pixel average of ALOS PALSAR level 1.5 backscattering used from five of category land use was to estimate the spectral characteristic of each object in difference HH and HV polarization. Ground truth data was taken from 169 locations which used for classification, 119 locations and 50 locations used for validation. Two different times of ALOS PALSAR level 1.0 2009 and 2010 data, was used for changes detection by multi temporal color composite combination. The accuracy result for classification map shows 62% of ground truth database, and multi temporal analysis showed the possibility of changes.

scholarly journals Methods for Earth-Observing Satellite Surface Reflectance Validation

2019 ◽  
Vol 11 (13) ◽  
pp. 1543
Author(s):  
Badawi ◽  
Helder ◽  
Leigh ◽  
Jing
Keyword(s):  
Solar Spectrum ◽  
Ground Truth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Radiometric Calibration ◽  
Ground Truth Data ◽  
Initial Validation ◽  
Reliable Performance ◽  
The Mean ◽  
The U.S

In this study an initial validation of the Landsat 8 (L8) Operational Land Imager (OLI) Surface Reflectance (SR) product was performed. The OLI SR product is derived from the L8 Top-of-Atmosphere product via the Landsat Surface Reflectance Code (LaSRC) software and generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center. The goal of this study is to develop and evaluate proper validation methodology for the OLI L2 SR product. Validation was performed using near-simultaneous ground truth SR measurements during Landsat 8 overpasses at 13 sites located in the U.S., Brazil, Chile and France. The ground truth measurements consisted of field spectrometer measurements, automated hyperspectral ground measurements operated by the Radiometric Calibration Network (RadCalNet) and derived SR measurements from Airborne Observation Platforms (AOP) operated by the National Ecological Observatory Network (NEON). The 13 sites cover a broad range of 0–0.5 surface reflectance units across the reflective solar spectrum. Results show that the mean reflectance difference between OLI L2 SR products and ground truth measurements for the 13 validation sites and all bands was under 2.5%. The largest uncertainties of 11% and 8% were found in the CA and Blue bands, respectively; whereas, the longer wavelength bands were within 4% or less. Results consistently indicated similarity between the OLI L2 SR product and ground truth data, especially in longer wavelengths over dark and bright targets, while less reliable performance was observed in shorter wavelengths and sparsely vegetated targets.

scholarly journals The application of FORMOSAT-2 high-temporal- and high-spatial resolution imagery for monitoring open straw burning and carbon emission detection

2013 ◽  
Vol 13 (3) ◽  
pp. 575-582 ◽  
Author(s):  
C.-C. Liu ◽  
P.-Y. Tseng ◽  
C.-Y. Chen
Keyword(s):  
Carbon Emissions ◽  
Supervised Classification ◽  
Carbon Emission ◽  
Global Climate ◽  
Ground Truth ◽  
Ground Truth Data ◽  
Study Results ◽  
Quantitative Results ◽  
Straw Burning ◽  
Emission Detection

Abstract. Rice is produced in more than 95 countries worldwide and is a staple food for over half of the world's population. Rice is also a major food crop of Taiwan. There are numerous rice crops planted on the western plains of Taiwan, and, after the harvest season, the left-over straw is often burned on-site. The air pollutants from the burning emissions include CO2, CO, CH4 and other suspended particles, most of these being the greenhouse gases which cause global climate change. In this study FORMOSAT-2 satellite images and ground-truth data from 2008 and 2009 are used to conduct supervised classification and calculate the extent of the straw burning areas. It was found that 10% of the paddies in the study area were burned after harvest during this 2-yr period. On this pro rata basis, we calculated the overall carbon emissions from the burning of the straw. The findings showed that these few farmers produced up to 34 000 tons of carbon emissions in 2008, and 40 000 tons in 2009. The study results indicate that remotely sensed images can be used to efficiently evaluate the important characteristics for carbon emission detection. It also provides quantitative results that are relevant to tracking sources of transport pollution, postharvest burning, and Asian dust in Taiwan.

scholarly journals Observing and Predicting Coastal Erosion at the Langue de Barbarie Sand Spit around Saint Louis (Senegal, West Africa) through Satellite-Derived Digital Elevation Model and Shoreline

2021 ◽  
Vol 13 (13) ◽  
pp. 2454
Author(s):  
Adélaïde Taveneau ◽  
Rafaël Almar ◽  
Erwin W. J. Bergsma ◽  
Boubou Aldiouma Sy ◽  
Abdoulaye Ndour ◽  
...  
Keyword(s):  
West Africa ◽  
Coastal Erosion ◽  
Local Population ◽  
Ground Truth ◽  
Beach Erosion ◽  
Wave Regime ◽  
Ground Truth Data ◽  
Saint Louis ◽  
Digital Elevation ◽  
Sand Spit

Coastal erosion at Saint Louis in Senegal is affecting the local population that consists of primarily fishermen communities in their housing and their access to the sea. This paper aims at quantifying urban beach erosion at Saint Louis, Senegal, West Africa which is located on the northern end of the 13 km long Langue de Barbarie sand spit. The coastal evolution is examined quantitatively over a yearly period using Pleiades sub-metric satellite imagery that allows for stereogrammetry to derive Digital Elevation Models (DEMs). The comparison with ground truth data shows sub-metric differences to the satellite DEMs. Despite its interest in remote areas and developing countries that cannot count on regular surveys, the accuracy of the satellite-derived topography is in the same order as the coastal change itself, which emphasizes its current limitations. These 3D data are combined with decades-long regular Landsat and Sentinel-2 imagery derived shorelines. These observations reveal that the sand spit is stretching, narrowing at its Northern part while it is lengthening downdrift Southward, independently from climatological changes in the wave regime. A parametric model based on a stochastic cyclic sand spit behaviour allows for predicting the next northern opening of a breach and the urban erosion at Saint Louis.

scholarly journals SENSORIAMENTO REMOTO APLICADO AO MONITORAMENTO DE MACRÓFITAS AQUÁTICAS NO RESERVATÓRIO DE BARRA BONITA, SP

Irriga ◽  
2017 ◽  
Vol 22 (2) ◽  
pp. 330-342
Author(s):  
Renata Teixeira de Almeida Minhoni ◽  
Mírian Paula Medeiros André Pinheiro ◽  
Roberto Filgueiras ◽  
Celia Regina Lopes Zimback
Keyword(s):  
Supervised Classification ◽  
Aquatic Macrophytes ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Landsat 8 Oli ◽  
Boundary Definition ◽  
E Mail

SENSORIAMENTO REMOTO APLICADO AO MONITORAMENTO DE MACRÓFITAS AQUÁTICAS NO RESERVATÓRIO DE BARRA BONITA, SP  RENATA TEIXEIRA DE ALMEIDA MINHONI1; MÍRIAN PAULA MEDEIROS ANDRÉ PINHEIRO2; ROBERTO FILGUEIRAS3 E CÉLIA REGINA LOPES ZIMBACK4 1 Eng. Ambiental, Doutoranda em Agronomia (Irrigação e Drenagem) – FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected] Eng. Agrônoma, Doutoranda em Agronomia (Irrigação e Drenagem) – FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected] Eng. Agrícola e Ambiental, Doutorando em Engenharia Agrícola – UFV. Avenida Peter Henry Rolfs, s/n - Campus Universitário, CEP 36570-900, Viçosa - MG, e-mail: [email protected] Eng. Agrônoma, Professora. Doutora do Departamento de Solos e Recursos Ambientais - FCA/UNESP. Rua José Barbosa de Barros, 1780, CEP 18610-307, Botucatu – SP, e-mail: [email protected]  1 RESUMO Macrófitas aquáticas são organismos fotossintéticos, com tamanho suficiente para serem vistos a olho nu, que crescem submersas, flutuando ou sobre a superfície da água. A ação antrópica no represamento de corpos hídricos tem ocasionado a eutrofização dos recursos hídricos, e dentre os desequilíbrios que esta ação gera no meio aquático está à elevada proliferação de macrófitas. Devido a esse fato, essa pesquisa foi desenvolvida com o objetivo de realizar uma estimativa da área ocupada por macrófitas aquáticas no reservatório da Usina Hidrelétrica de Barra Bonita (SP), nos anos de 2013, 2014 e 2015. O estudo foi realizado na estação seca (mês de agosto), por meio do uso do NDVI (Normalized Difference Vegetation Index) e classificação supervisionada MAXVER (Máxima Verossimilhança). Para obtenção dos mapas e gráficos, foram realizadas as seguintes ações: seleção das imagens do satélite LANDSAT-8/OLI, calibração radiométrica, correção atmosférica, reprojeção, definição do limite, recorte da área, NDVI e classificação supervisionada. Os mapas obtidos por meio da classificação supervisionada, auxiliada pelos mapas de NDVI, apontaram para um aumento de aproximadamente 50% na área ocupada por macrófitas aquáticas de 2013 a 2015. Palavras-chave: classificação supervisionada, eutrofização, índice NDVI, landsat-8.  MINHONI, R. T. A.; PINHEIRO, M. P. M. A.; FILGUEIRAS, R.; ZIMBACK, C. R. L.REMOTE SENSING APPLIED TO THE MONITORING OF AQUATIC MACROPHYTES AT BARRA BONITA RESERVOIR, SP  2 ABSTRACT Aquatic macrophytes are photosynthetic organisms, large enough to be seen with naked eye, which grow submerged, floating or on the surface of the water. The anthropic action in the damming of water bodies has caused eutrophication of water resources, and among the imbalances that this action generates in the aquatic environment is the high proliferation of macrophytes. Due to this fact, this research was developed with the aim of estimating the area occupied by aquatic macrophytes in the reservoir of Barra Bonita Hydroelectric Power Plant (SP), in the years of 2013, 2014 and 2015. The study was carried out in the dry season (August), through the use of NDVI (Normalized Difference Vegetation Index) and supervised classification MAXVER (Maximum Likelihood). To obtain the maps and graphs, the following actions were taken: selection of LANDSAT-8 / OLI satellite images, radiometric calibration, atmospheric correction, reprojection, boundary definition, NDVI and supervised classification. The maps obtained through supervised classification, aided by NDVI maps, pointed to an increase of approximately 50% in the area occupied by aquatic macrophytes from 2013 to 2015. Keywords: supervised classification, eutrophication, NDVI index, landsat-8.

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