scholarly journals IMAGENS DO LANDSAT- 8 NO MAPEAMENTO DE SUPERFÍCIES EM ÁREA IRRIGADA

Irriga ◽  
2015 ◽  
Vol 1 (2) ◽  
pp. 30-36
Author(s):  
JANNAYLTON EVERTON OLIVIERA SANTOS ◽  
Donizeti Aparecido Pastori Nicolete ◽  
Roberto Filgueiras ◽  
Victor Costa Leda ◽  
Célia Regina Lopes Zimback
Keyword(s):  
Vegetation Index ◽  
Atmospheric Correction ◽  
Growth Stages ◽  
Landsat 8 ◽  
Land Uses ◽  
Radiometric Calibration ◽  
Estimation Of Parameters ◽  
Irrigated Area ◽  
Dark Object ◽  
Center Pivot

IMAGENS DO LANDSAT- 8 NO MAPEAMENTO DE SUPERFÍCIES EM ÁREA IRRIGADA  JANNAYLTON ÉVERTON OLIVEIRA SANTOS¹; DONIZETI APARECIDO PASTORI NICOLETE¹; ROBERTO FILGUEIRAS¹; VICTOR COSTA LEDA² E CÉLIA REGINA LOPES ZIMBACK¹ [1] Departamento de Ciência do Solo e Recursos Ambientais da UNESP - campus Botucatu – SP,Programa de Irrigação e Drenagem UNESP/FCA. Email:[email protected], [email protected], [email protected], [email protected] Departamento de Ciência do Solo e Recursos Ambientais da UNESP - campus Botucatu – SP, Programa de Energia na agricultura UNESP/FCA. Email: [email protected]  1 RESUMO O trabalho tem como objetivo analisar os parâmetros NDVI (Normalized Difference Vegetation Index) e SAVI (Soil Adjusted Vegetation Index) para dois períodos, chuvoso e seco, em área irrigada. A área de estudo apresenta constante expansão na irrigação por pivô central, sendo localizada nas proximidades do município de Paranapanema – SP. As imagens foram processadas utilizando o programa QGIS 2.2. Para a obtenção dos índices realizou-se a calibração radiométrica, que consiste na transformação dos números digitais para correspondentes físicos, radiância e reflectância, e correção atmosférica por meio do método DOS 1 (Dark Object Substraction). Após os processamentos computou-se os índices de vegetação, os quais deram subsídio para o monitoramento das culturas agrícolas nos diferentes manejos (irrigado e sequeiro) e épocas de análise (chuvoso e seco). Como auxílio para o monitoramento das áreas, fusionou-se uma composição RGB 432, com a banda pancromática, o que permitiu uma pré-análise das condições e dos tipos de uso do solo na área de estudo. As cartas obtidas de NDVI e SAVI permitiram inferir sobre as condições fisiológicas e estádios fenológicos da vegetação nos diferentes usos do solo. No período de estiagem os índices médios obtiveram valores inferiores ao do período chuvoso, tendo isto ocorrido, principalmente, devido as condições de estresse hídrico característico da época. Desse modo, o cômputo dos parâmetros para a área de estudo foram de extrema valia na análise das condições da vegetação nos diferentes cenários, pois por meio desses foi possível inferir sobre as diferenças encontradas nos períodos e nos diferentes usos do solo, o que auxilia os agricultores em tomadas de decisão com relação ao manejo de suas áreas, no que tange as questões relacionadas a necessidades hídrica das culturas.Palavras-chave: Sensoriamento remoto, monitoramento agrícola, pivô central.  SANTOS, J. E. O.; NICOLETE, D. A. P.; FILGUEIRAS, R.; LEDA, V. C.; ZIMBACK, C. R. L.IMAGES OF LANDSAT-8 TO MONITOR THE SURFACES ON IRRIGATED AREA    2 ABSTRACT The study aims to analyze NDVI (Difference Vegetation Index Normalized) and SAVI (Soil Adjusted Vegetation Index) for two periods (rainy and dry) on irrigated area. The study area has constant expansion on irrigation center pivot, it is located near the Paranapanema ­- SP county. For this study we used two images of Landsat ­8 orbital platform. The images were processed using QGIS 2.2 program. To obtain the indexes, it was held radiometric calibration, which is the transformation of digital numbers in corresponding physical, radiance and reflectance, and atmospheric correction using the DOS method (Dark Object Substraction). These procedures were performed on semi automatic classification plugin. After appropriate calibrations and corrections, it were computed the vegetation indexes. These gave allowance for monitoring agricultural crops in different management systems (irrigated and rainfed) and analysis of seasons (wet and dry). As an aid for monitoring areas, we merged a RGB ­432 composition, with a panchromatic band. This product allowed a pre - analysis of conditions and types of land use in the study area. The maps obtained from NDVI and SAVI, allowed to infer about the physiological conditions and growth stages vegetation in different land uses. During the dry season, we found average rates which has lower values than the rainy season. This occurred, mainly, due to water stress conditions, which is characteristic of that season. Thus, the estimation of parameters for the study area were extremely valuable in analysis of vegetation conditions, on different scenarios, because through these, became possible to infer about the differences in seasons analized and different land uses. Then, these analisys served as an aid for farmers in decision­ making, regard the management of their areas, which is related to water requirements of crops. Keywords: Remote sensing, agriculture monitoring, center pivot.

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.

scholarly journals Estudio de bofedales en los Andes ecuatorianos a través de la comparación de imágenes Landsat-8 y Sentinel-2

2019 ◽  
pp. 45
Author(s):  
C. Jara ◽  
J. Delegido ◽  
J. Ayala ◽  
P. Lozano ◽  
A. Armas ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Dynamic Range ◽  
Atmospheric Correction ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Water Index ◽  
Red Edge ◽  
Standard Difference ◽  
Digital Levels ◽  
Sentinel 2

<p>The objective of the present study was to compare the Landsat-8 and Sentinel-2 images to calculate the wetland´s extension, distribution and degree of conservation, in Reserva de Producción de Fauna Chinborazo (RPFCH) protected area located in the Andean region of Ecuador. This process was developed with in situ work in 16 wetlands, distributed in different conservation levels. The Landsat-8 and Sentinel-2 images were processed through a radiometric calibration (restoration of lost lines or píxels and correction of the stripe of the image) and an atmospheric correction (conversion of the digital levels to radiance values), to later calculate the Vegetation spectral indexes: NDVI, SAVI (L = 0.5) where L is a constant of the soil brightness component, EVI2 (improved vegetation index 2), NDWI (standard difference water index), WDRI (wide dynamic range vegetation index) and the Red Edge model that only this one has in Sentinel-2 in this study. Making a classification of the Bofedal ecosystem in satellite images by applying Random Forest, the most important variables with Landsat-8 were EVI2 (37.72%) and SAVI with L = 0.5 (30.97%), while with Sentinel-2 the most important variables correspond to the Red Edge (38.54%) and WDRI (27.06%). With the indices calculated, two categories of analysis were determined: a) wetland integrated by the levels: intervened [1], moderately conserved [2] and conserved [3] and b) other than wetland [4] integrated by areas that do not correspond to this ecosystem. Landsat-8 shows that the percentage of correct classifications of píxels belonging to the wetland category corresponds to: [1] 72.76%, [2] 58.38%, [3] 68.42%, while for the category other [4] were correct 95.15%. With Sentinel-2, the percentage of correct classifications corresponds to [1] 95.00%, [2] 82.60%, [3] 96.25%, while for the category other [4] the correct answers were 98.13%. In this way with Landsat-8 the wetland corresponds to 21.708,54 ha (41.21%), while with Sentinel-2 the wetland represents a total of 20,518 ha (38.95%), of the 52,560 ha that belong to the RPFCH, concluding that Sentinel-2, due to its better spatial resolution, and the incorporation of its new bands in Red Edge, obtains better results in image classification.</p>

scholarly journals Assessment of the Forest Health Through Remote Sensing Techniques in Valea Roșie Natura 2000 Site, Bihor County, Romania

2019 ◽  
Vol 9 (2) ◽  
pp. 207-215
Author(s):  
L. Blaga ◽  
Ioana Josan ◽  
G. V. Herman ◽  
V. Grama ◽  
S. Nistor ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Atmospheric Correction ◽  
Standardized Precipitation Index ◽  
Forest Health ◽  
Natura 2000 ◽  
Landsat 8 ◽  
Radiometric Calibration ◽  
Short Period

Abstract The present study deals with the estimation of the evolution tendency of the environmental stage of a protected habitat with predominant forest vegetation, during a short period of time, using techniques specific to remote sensing. Therefore, two important spectral indexes were tested while assessing the health of the forest ecosystems: i.e. the Normalized Difference Vegetation Index (NDVI) and the Structure Insensitive Pigment Index (SIPI). The period of time taken into consideration for the study was, 2013 - 2019, having used medium resolution satellite photos, Landsat 8 OLI, having initially undergone standard pre-processing operations (resize data, radiometric calibration, atmospheric correction). The satellite images modified according to the Top of Atmosphere Reflectance and corrected topographically resulted into getting values for the two before mentioned indexes. The quantity-spatial results obtained, correlated to the monthly values of the precipitations processed in order to obtain the SPI (Standardized Precipitation Index), mostly reveal, in what SIPI and also NDVI are concerned, a slight decrease in the quality of the forest on the analysed area in the sense that the vegetation stress is increased under meteorological factors, expressed differently depending on the morphometric and pedological parameters of the habitat.

scholarly journals Mapping of Cotton Fields Within-Season Using Phenology-Based Metrics Derived from a Time Series of Landsat Imagery

2020 ◽  
Vol 12 (18) ◽  
pp. 3038
Author(s):  
Dhahi Al-Shammari ◽  
Ignacio Fuentes ◽  
Brett M. Whelan ◽  
Patrick Filippi ◽  
Thomas F. A. Bishop
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Landsat Imagery ◽  
Google Earth ◽  
Growth Stages ◽  
Landsat 8 ◽  
Crop Type ◽  
Eastern Australia ◽  
Normalised Difference Vegetation Index ◽  
Cotton Fields

A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.

scholarly journals Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

2020 ◽  
Vol 9 (4) ◽  
pp. 257 ◽  
Author(s):  
Kiwon Lee ◽  
Kwangseob Kim ◽  
Sun-Gu Lee ◽  
Yongseung Kim
Keyword(s):  
Open Source ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Atmospheric Correction ◽  
Urban Forestry ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

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 COMPARISON OF LANDSAT-8 AND SENTINEL-2 DATA FOR CLASSIFICATION OF RABI CROPS OVER KARNATAKA, INDIA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 579-584
Author(s):  
S. Paul ◽  
D. N. Kumar
Keyword(s):  
Vegetation Index ◽  
Support Vector ◽  
Growth Stages ◽  
Landsat 8 ◽  
Large Spatial Scale ◽  
Spectral Bands ◽  
Sentinel 2A ◽  
Rabi Crops ◽  
Sentinel 2

<p><strong>Abstract.</strong> Classification of crops is very important to study different growth stages and forecast yield. Remote sensing data plays a significant role in crop identification and condition assessment over a large spatial scale. Importance of Normalized Difference Indices (NDIs) along with surface reflectances of remotely sensed spectral bands have been evaluated for classification of eight types of Rabi crops utilizing the Landsat-8 and Sentinel-2 datasets and performances of both the satellites are compared. Landsat-8 and Sentinel-2A images are acquired for the location of crops and seven and nine spectral bands are utilized respectively for the classification. Experiments are carried out considering the different combinations of surface reflectances of spectral bands and optimal NDIs as features in support vector machine classifier. Optimal NDIs are selected from the set of <sup>7</sup>C<sub>2</sub> and <sup>9</sup>C<sub>2</sub> NDIs of Landsat-8 and Sentinel-2A datasets respectively using the partial informational correlation measure, a nonparametric feature selection approach. Few important vegetation indices (e.g. enhanced vegetation index) are also experimented in combination with the surface reflectances and NDIs to perform the crop classification. It has been observed that combination of surface reflectances and optimal NDIs can classify the crops more efficiently. The average overall accuracy of 80.96% and 88.16% are achieved using the Landsat-8 and Sentinel-2A datasets respectively. It has been observed that all the crop classes except Paddy and Cotton achieve producer accuracy and user accuracy of more than 75% and 85% respectively. This technique can be implemented for crop identification with adequate accessibility of crop information.</p>

scholarly journals O CLIMA URBANO DE FEIRA DE SANTANA –BA ATRAVÉS DA TEMPERATURA DA SUPERFÍCIE E DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA -NDVI

2018 ◽  
Vol 10 (3) ◽  
pp. 94-98
Author(s):  
Jovana Mariano Damasceno ◽  
Margarete Cristiane de Costa Trindade Amorim
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Surface Temperature ◽  
Vegetation Index ◽  
Thermal Structure ◽  
Urban Climate ◽  
Landsat 8 ◽  
Land Uses ◽  
Remote Sensing Techniques ◽  
Climate Studies

The use of remote sensing techniques for urban climate studies has advanced over recent years. In this sense, the objective of this study was to identify the influence exerted by the different land uses and coverages in the thermal structure of the urban surface in Feira de Santana-BA. For elaboration of the map of the surface temperature were used calculations for conversion of digital values of the image of Landsat 8 satellite to temperature in degrees Celsius (°C) in the software Idrisi. The vegetation mapping was prepared by the calculation of the vegetation index of normalized difference (NDVI), on the same software. Analyzing the results,itwas possible to perceive that the highestsurface temperature aredirectlyrelated to land use,and thatthe vegetation is fundamental to decrease those temperatures. Thereby, remotesensingtechniques are very useful for urban climate studies.

scholarly journals Mapping of active and empty aquaponds using spectral indices in coastal region of Guntur District, Andhra Pradesh, India

2021 ◽  
Vol 42 (5) ◽  
pp. 1338-1346
Author(s):  
P. Prasuna Rani ◽  
◽  
M. Sunil Kumar ◽  
P.V. Geetha Sireesha ◽  
◽  
...  
Keyword(s):  
Maximum Likelihood ◽  
Andhra Pradesh ◽  
Vegetation Index ◽  
Confusion Matrix ◽  
Coastal Region ◽  
Landsat 8 ◽  
Land Uses ◽  
Spectral Indices ◽  
Maximum Likelihood Classifier ◽  
Guntur District

Aim: To evaluate spectral indices as tools for separation of active aquaponds filled with water and engaged in shrimp/fish production from empty aquaponds using Landsat -8 data in coastal region of Guntur district, Andhra Pradesh. Methodology: The active and empty aquaponds were demarcated with Landsat satellite (Landsat-8) Operational Land Imager’s (OLI) multispectral images using maximum likelihood classifier (MLC) algorithm and spectral indices like Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Water Ratio Index (WRI) and Automated Water Extraction Index (AWEInsh) by means of thresholds. Results: The supervised classification using maximum likelyhood classifier recorded the highest active aquapond area whereas; NDWI, combination of indices and WRI resulted in lower but almost similar extents. Evaluation of confusion matrix using validation points revealed that NDWI, WRI and combination of indices resulted in all most perfect agreement with a kappa value of more than 0.9. Maximum likelihood classifier, NDVI and MNDWI could separate active ponds and empty ponds from other land uses with strong agreement, while AWEInsh could separate different land uses only with moderate agreement. Interpretation: The study indicates that spectral indices like NDWI, WRI and combination of indices are able to delineate aquaponds that were cultured for shrimp/fish and kept empty at a given time with noticeably high accuracy using satellite data for better managing of resources in coastal ecosystem.

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