scholarly journals Validation of aerosol estimation in atmospheric correction algorithm ATCOR

2015 ◽  
Vol XL-7/W3 ◽  
pp. 677-683 ◽  
Author(s):  
B. Pflug ◽  
M. Main-Knorn ◽  
A. Makarau ◽  
R. Richter
Keyword(s):  
Satellite Images ◽  
Landsat Imagery ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Biomass Estimation ◽  
Spatial And Temporal Variability ◽  
Landsat 8 ◽  
Vertical Column ◽  
Sun Photometer ◽  
Better Than

Atmospheric correction of satellite images is necessary for many applications of remote sensing, i.e. computation of vegetation indices and biomass estimation. The first step in atmospheric correction is estimation of the actual aerosol properties. Due to the spatial and temporal variability of aerosol amount and type, this step becomes crucial for an accurate correction of satellite data. Consequently, the validation of aerosol estimation contributes to the validation of atmospheric correction algorithms. In this study we present the validation of aerosol estimation using own sun photometer measurements in Central Europe and measurements of AERONET-stations at different locations in the world. Our ground-based sun photometer measurements of vertical column aerosoloptical thickness (AOT) spectra are performed synchronously to overpasses of the satellites RapidEye, Landsat 5, Landsat 7 and Landsat 8. Selected AERONET data are collocated to Landsat 8 overflights. The validation of the aerosol retrieval is conducted by a direct comparison of ground-measured AOT with satellite derived AOT using the ATCOR tool for the selected satellite images. The mean uncertainty found in our experiments is ΔAOT550nm ≈ 0.03±0.02 for cloudless conditions with cloud+haze fraction below 1%. This AOT uncertainty approximately corresponds to an uncertainty in surface albedo of Δρ ≈ 0.003. Inclusion of cloudy and hazy satellite images into the analysis results in mean ΔAOT550nm ≈ 0.04±0.03 for both RapidEye and Landsat imagery. About ⅓ of samples perform with the AOT uncertainty better than 0.02 and about ⅔ perform with AOT uncertainty better than 0.05.

scholarly journals A Comparative Study of Water Indexes and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

2019 ◽  
Author(s):  
Feifei Pan ◽  
Xiaohuan Xi ◽  
Cheng Wang
Keyword(s):  
Image Classification ◽  
Landsat Imagery ◽  
Red Light ◽  
Computational Cost ◽  
Google Earth ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Water Index ◽  
Otsu Method ◽  
Better Than

To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEIns, and AWEIs) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEIns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEIns and AWEIs are better than NDWI, MNDWI and MNDWI2. AWEIns works better than AWEIs under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEIns.

scholarly journals BIOMASS AND CARBON STOCK ESTIMATION USING IN-SITU OBSERVATIONS AND GIS IN GILGIT BALTISTAN, PAKISTAN

2021 ◽  
Vol XLIV-M-3-2021 ◽  
pp. 49-55
Author(s):  
E. Fatima ◽  
S. S. Ali
Keyword(s):  
Carbon Stock ◽  
Anthropogenic Activities ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Accurate Method ◽  
Carbon Accounting ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Inventory Data ◽  
Gilgit Baltistan

Abstract. Carbon dioxide (CO2) emission and other greenhouse gases are rising day by day due to anthropogenic activities which lead to global warming and cause natural disasters. Thus REDD+ comes up with an initiative to reduce emissions from deforestation through Carbon accounting, in which the under developing countries Measure, Report, and Verify (MRV) the sum of Above Ground Biomass (AGB)/carbon stored in a particular forest. Nonetheless, the major challenge for REDD+ is to find an accurate method for biomass estimation. The purpose of this study was to model and map the AGB and carbon stock of Gilgit-Baltistan, Pakistan. For this purpose, we linked Landsat 8 and forest inventory data to assess the potential of Vegetation Indices (Vis) derived AGB estimation. Inventory data consisted of the tree measurements from 480 plots that data was collected in the year (June–Oct) 2016 in a 72,971 km2 (28,174 sq mi) study area, in Gilgit-Baltistan. Out of these plots, 287 was used in Calibration and 191 is used for Validation. This paper provides a regression equation between the reflection values from the Landsat-8 satellite image and sample areas where terrestrial aboveground biomass (AGB) was calculated by direct measurement method. As a result of the calculations made, a positive linear correlation between AGB and NDVI was relatively high compared to other vegetation indices i.e 0.59 in the year 2016 or for the year 2013.

scholarly journals ESTIMATING ABOVEGROUND BIOMASS OF BAMBOO AND MIXED BAMBOO FOREST IN THUA THIEN-HUE PROVINCE, VIET NAM USING PALSAR-2 AND LANDSAT OLI DATA

2019 ◽  
Vol XLII-4/W20 ◽  
pp. 11-19
Author(s):  
T. T. Cat Tuong ◽  
H. Tani ◽  
X. F. Wang ◽  
V.-M. Pham
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Landsat Data ◽  
Bamboo Forest ◽  
Landsat 8 Oli ◽  
L Band

Abstract. In this study, above-ground biomass (AGB) performance was evaluated by PALSAR-2 L-band and Landsat data for bamboo and mixed bamboo forest. The linear regression model was chosen and validated for forest biomass estimation in A Luoi district, Thua Thien Hue province, Vietnam. A Landsat 8 OLI image and a dual-polarized ALOS/PALSAR-2 L-band (HH, HV polarizations) were used. In addition, 11 diferrent vegetation indices were extracted to test the performance of Landsat data in estimating forest AGB Total of 54 plots were collected in the bamboo and mixed bamboo forest in 2016. The linear regression is used to evaluate the sensitivity of biomass to the obtained parameters, including radar polarization, optical properties, and some vegetation indices which are extracted from Landsat data. The best-fit linear regression is selected by using the Bayesian Model Average for biomass estimation. Leave-one-out cross-validation (LOOCV) was employed to test the robustness of the model through the coefficient of determination (R squared – R2) and Root Mean Squared Error (RMSE). The results show that Landsat 8 OLI data has a slightly better potential for biomass estimation than PALSAR-2 in the bamboo and mixed bamboo forest. Besides, the combination of PALSAR-2 and Landsat 8 OLI data also has a no significant improvement (R2 of 0.60) over the performance of models using only SAR (R2 of 0.49) and only Landsat data (R2 of 0.58–0.59). The univariate model was selected to estimate AGB in the bamboo and mixed bamboo forest. The model showed good accuracy with an R2 of 0.59 and an RMSE of 29.66 tons ha−1. The comparison between two approaches using the entire dataset and LOOCV demonstrates no significant difference in R (0.59 and 0.56) and RMSE (29.66 and 30.06 tons ha−1). This study performs the utilization of remote sensing data for biomass estimation in bamboo and mixed bamboo forest, which is a lack of up-to-date information in forest inventory. This study highlights the utilization of the linear regression model for estimating AGB of the bamboo forest with a limited number of field survey samples. However, future research should include a comparison with non-linear and non-parametric models.

scholarly journals Detection of changes of the soil use and vegetation in the low San Francisco

2019 ◽  
Vol 9 (5) ◽  
pp. 310
Author(s):  
Douglas Alberto De Oliveira Silva ◽  
Frederico abraão Costa Lins ◽  
Jhon Lennon Bezerra da Silva ◽  
Landson Carlos da Silva ◽  
Geber Barbosa De Albuquerque Moura ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Environmental Monitoring ◽  
Satellite Images ◽  
Environmental Changes ◽  
Vegetation Indices ◽  
Operating Cost ◽  
Semiarid Region ◽  
Landsat 8 ◽  
Biophysical Parameters

The quantification and spatialization of environmental degradation is an essential element in the planning of agricultural activities and in the management of the water and natural resources in the semiarid. Thus, the detection of changing land use conditions is necessary for understand with more accurately the dynamics of the different types of soil coverage. Remote sensing techniques make it possible to evaluate this type of environmental monitoring in a practical and efficient manner, and low operating cost in a short time. The objective of this study was to monitor and evaluate the environmental changes caused about the Caatinga vegetation coverage by remote sensing using satellite images in the municipality of Petrolina, semiarid region of Pernambuco state. The study was developed using two Landsat-8 satellite images, processed using SEBAL algorithm steps, in the development of thematic maps of the surface biophysical parameters. The maps expressed the spatial distribution of the albedo parameters and surface temperature, and of the NDVI and SAVI vegetation indices, which served for highlight the dynamics of environmental changes in the Caatinga natural environment of semiarid region. The results showed increased of the albedo and surface temperature when there was a decrease in vegetation indices. This behavior was mainly favored by the region's dry season, which coincides with the satellite's days of passage. The biophysical parameters are effective in the spatial monitoring of semiarid regions, highlighting the spatial variability of the soil uses, identifying possibly degraded areas. Remote sensing environmental monitoring is a viable alternative for mitigate environmental changes caused by anthropogenic actions and drought events. 

scholarly journals MANGROVE ABOVE GROUND BIOMASS ESTIMATION USING COMBINATION OF LANDSAT 8 AND ALOS PALSAR DATA

2017 ◽  
Vol 12 (2) ◽  
pp. 85 ◽  
Author(s):  
Gathot Winarso ◽  
Yenni Vetrita ◽  
Anang D. Purwanto ◽  
Nanin Anggraini ◽  
Soni Darmawan ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Vegetation Indices ◽  
Emission Trading ◽  
Remote Sensing Data ◽  
Tree Height ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Alos Palsar ◽  
Sar Data ◽  
Estimation Models

Mangrove ecosystem is important coastal ecosystem, both ecologically and economically. Mangrove provides rich-carbon stock, most carbon-rich forest among ecosystems of tropical forest. It is very important for the country to have a large mangrove area in the context of global community of climate change policy related to emission trading in the Kyoto Protocol. Estimation of mangrove carbon-stock using remote sensing data plays an important role in emission trading in the future. Estimation models of above ground mangrove biomass are still limited and based on common forest biomass estimation models that already have been developed. Vegetation indices are commonly used in the biomass estimation models, but they have low correlation results according to several studies. Synthetic Aperture Radar (SAR) data with capability in detecting volume scattering has potential applications for biomass estimation with better correlation. This paper describes a new model which was developed using a combination of optical and SAR data. Biomass is volume dimension related to canopy and height of the trees. Vegetation indices could provide two dimensional information on biomass by recording the vegetation canopy density and could be well estimated using optical remote sensing data. One more dimension to be 3 dimensional feature is height of three which could be provided from SAR data. Vegetation Indices used in this research was NDVI extracted from Landsat 8 data and height of tree estimated from ALOS PALSAR data. Calculation of field biomass data was done using non-decstructive allometric based on biomass estimation at 2 different locations that are Segara Anakan Cilacap and Alas Purwo Banyuwangi, Indonesia. Correlation between vegetation indices and field biomass with ALOS PALSAR-based biomass estimation was low. However, multiplication of NDVI and tree height with field biomass correlation resulted R2 0.815 at Alas Purwo and R2 0.081 at Segara Anakan.  Low correlation at Segara anakan was due to failed estimation of tree height. It seems that ALOS PALSAR height was not accurate for determination of areas dominated by relative short trees as we found at Segara Anakan Cilacap, but the result was quite good for areas dominated by high trees. To improve the accuracy of tree height estimation, this method still needs validation using more data.

scholarly journals Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images

2018 ◽  
Vol 51 (1) ◽  
pp. 932-944 ◽  
Author(s):  
Fabrício L. Macedo ◽  
Adélia M. O. Sousa ◽  
Ana Cristina Gonçalves ◽  
José R. Marques da Silva ◽  
Paulo A. Mesquita ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Quercus Rotundifolia

scholarly journals ANÁLISE DO IMPACTO DA CORREÇÃO ATMOSFÉRICA NO CÁLCULO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA A PARTIR DE IMAGEM LANDSAT 8/OLI

2020 ◽  
Vol 13 (1) ◽  
pp. 076
Author(s):  
Cristiane Nunes Francisco ◽  
Paulo Roberto da Silva Ruiz ◽  
Cláudia Maria de Almeida ◽  
Nina Cardoso Gruber ◽  
Camila Souza dos Anjos
Keyword(s):  
Rio De Janeiro ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Topographic Effects ◽  
Landsat 8 Oli ◽  
Arithmetic Operations

As operações aritméticas efetuadas entre bandas espectrais de imagens de sensoriamento remoto necessitam de correção atmosférica para eliminar os efeitos atmosféricos na resposta espectral dos alvos, pois os números digitais não apresentam escala equivalente em todas as bandas. Índices de vegetação, calculados com base em operações aritméticas, além de caracterizarem a vegetação, minimizam os efeitos da iluminação da cena causados pela topografia. Com o objetivo de analisar a eficácia da correção atmosférica no cálculo de índices de vegetação, este trabalho comparou os Índices de Vegetação por Diferença Normalizada (Normalized Difference Vegetation Index - NDVI), calculados com base em imagens corrigidas e não corrigidas de um recorte de uma cena Landsat 8/OLI situado na cidade do Rio de Janeiro, Brasil. Os resultados mostraram que o NDVI calculado pela reflectância, ou seja, imagem corrigida, apresentou o melhor resultado, devido ao maior discriminação das classes de vegetação e de corpos d'água na imagem, bem como à minimização do efeito topográfico nos valores dos índices de vegetação.  Analysis of the atmospheric correction impact on the assessment of the Normalized Difference Vegetation Index for a Landsat 8 oli image A B S T R A C TThe image arithmetic operations must be executed on previously atmospherically corrected bands, since the digital numbers do not present equivalent scales in all bands. Vegetation indices, calculated by means of arithmetic operations, are meant for both targets characterization and the minimization of illumination effects caused by the topography. With the purpose to analyze the efficacy of atmospheric correction in the calculation of vegetation indices with respect to the mitigation of atmospheric and topographic effects on the targets spectral response, this paper compared the NDVI (Normalized Difference Vegetation Index) calculated using corrected and uncorrected images related to an inset of a Landsat 8 OLI scene from Rio de Janeiro, Brazil. The result showed that NDVI calculated from reflectance values, i.e, corrected images, presented the best results due to a greater number of vegetation patches and water bodies classes that could be discriminated in the image, as well the mitigation of the topographic effect in the vegetation indices values.Keywords: remote sensing, urban forest, atmospheric correction.

scholarly journals Urban climate and registration of thermal anomalies of territories using satellite imagery

2021 ◽  
Vol 266 ◽  
pp. 08008
Author(s):  
A.D. Biryukov ◽  
V.D. Olenkov ◽  
A.О. Kolmogorova
Keyword(s):  
Satellite Images ◽  
Urban Climate ◽  
Atmospheric Correction ◽  
Optimization Methods ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Before And After ◽  
Landsat 7 ◽  
Automated Processing ◽  
Urban Heat

This paper describes a simplified method of mapping of the ur-ban environment surface to obtain a map of the thermal anomalies distribu-tion and study the structure of the urban heat island. Those maps allow evaluating or planning the urban microclimate optimization methods and studying the effect of land cover type on the site temperature. The article discusses the processing of five satellite images for the summer and winter from 2002 to 2019. We propose a simpler and more automated processing of thermal images for Landsat 7 and Landsat 8. The stages of automatic atmospheric correction according to the DOS1 method and calculation of the emissivity with surface classification are considered. Image processing was carried out in the QGIS software package using the Semiautomatic Classification Plugin extension. As a result, thermal anomalies in Chelya-binsk were localized and a comparison of the thermal map for the specific region before and after urbanization was made.

scholarly journals Using Satellite Images to Measure Crop Productivity: Long-Term Impact Assessment of a Randomized Technology Adoption Program in the Dominican Republic

2021 ◽  
Author(s):  
Lina Salazar ◽  
Ana Claudia Palacios ◽  
Michael Selvaraj ◽  
Frank Montenegro
Keyword(s):  
Remote Sensing ◽  
Dominican Republic ◽  
Event Study ◽  
Satellite Images ◽  
Vegetation Indices ◽  
Agricultural Productivity ◽  
Spillover Effects ◽  
Crop Productivity ◽  
Landsat 8

This study combines three rounds of surveys with remote sensing to measure long-term impacts of a randomized irrigation program in the Dominican Republic. Specifically, Landsat 7 and Landsat 8 satellite images are used to measure the causal effects of the program on agricultural productivity, measured through vegetation indices (NDVI and OSAVI). To this end, 377 plots were analyzed (129 treated and 248 controls) for the period from 2011 to 2019. Following a Differencein-Differences (DD) and Event study methodology, the results confirmed that program beneficiaries have higher vegetation indices, and therefore experienced a higher productivity throughout the post-treatment period. Also, there is some evidence of spillover effects to neighboring farmers. Furthermore, the Event Study model shows that productivity impacts are obtained in the third year after the adoption takes place. These findings suggest that adoption of irrigation technologies can be a long and complex process that requires time to generate productivity impacts. In a more general sense, this study reveals the great potential that exists in combining field data with remote sensing information to assess long-term impacts of agricultural programs on agricultural productivity.

A Landsat 8 OLI Satellite Data-Based Assessment of Spatio-Temporal Variations of Lake Sevan Phytoplankton Biomass

2017 ◽  
Vol 17 (1) ◽  
pp. 83-89 ◽  
Author(s):  
Garegin Tepanosayn ◽  
Vahagn Muradyan ◽  
Azatuhi Hovsepyan ◽  
Lilit Minasyan ◽  
Shushanik Asmaryan
Keyword(s):  
Water Quality ◽  
Phytoplankton Biomass ◽  
Atmospheric Correction ◽  
In Situ Measurements ◽  
Biomass Estimation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Phytoplankton Communities ◽  
Spatio Temporal

Abstract The Sevan is one of the world’s largest highland lakes and the largest drinking water reservoir to the South Caucasus. An intensive drop in the level of the lake that occurred over the last decades of the 20th century has brought to eutrophication. The 2000s were marked by an increase in the level of the lake and development of fish farming. To assess possible effect of these processes on water quality, creating a state-ofthe- art water quality monitoring system is required. Traditional approaches to monitoring aquatic systems are often time-consuming, expensive and non-continuous. Thus, remote sensing technologies are crucial in quantitatively monitoring the status of water quality due to the rapidity, cyclicity, large-scale and low-cost. The aim of this work was to evaluate potential applications of the Landsat 8 Operational Land Imager (OLI) to study the spatio-temporal phytoplankton biomass changes. In this study phytoplankton biomasses are used as a water quality indicator, because phytoplankton communities are sensitive to changes in their environment and directly correlated with eutrophication. We used Landsat 8 OLI (30 m spatial resolution, May, Aug, Sep 2016) images converted to the bottom of atmosphere (BOA) reflectance by performing standard preprocessing steps (radiometric and atmospheric correction, sun glint removal etc.). The nonlinear regression model was developed using Landsat 8 (May 2016) coastal blue, blue, green, red, NIR bands, their ratios (blue/red, red/green, red/blue etc.) and in situ measurements (R2=0.7, p<0.05) performed by the Scientific Center of Zoology and Hydroecology of NAS RA in May 2016. Model was applied to the OLI images received for August and September 2016. The data obtained through the model shows that in May the quantity of phytoplankton mostly varies from 0.2 to 0.6g/m3. In August vs. May a sharp increase in the quantity of phytoplankton around 1-5 g/m3 is observable. In September, very high contents of phytoplankton are observed for almost entire surface of the lake. Preliminary collation between data generated with help of the model and in-situ measurements allows to conclude that the RS model for phytoplankton biomass estimation showed reasonable results, but further validation is necessary.

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