scholarly journals Procjena opsega poplave daljinskim istraživanjima

2021 ◽  
Vol 24 (1) ◽  
pp. 119-132
Author(s):  
Tea Butković ◽  
Andrea Maretić ◽  
Bojana Horvat ◽  
Nino Krvavica
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Water Index ◽  
Normalized Difference Water Index

U radu su, na primjeru poplave koja je u svibnju 2014. godine zadesila istočnu Hrvatsku, uspoređene tri metode kartiranja i procjene opsega poplavljenog područja: metoda analize refleksije s površine u blisko infracrvenom (IC) dijelu spektra (jednokanalna metoda) te metode vegetacijskog indeksa NDVI (Normalized Difference Vegetation Index) i vodenog indeksa NDWI (Normalized Difference Water Index). Metode kao ulazne podatke koriste snimke snimljene pasivnim senzorom ugrađenim na satelitsku platformu Landsat 8. Analizirane su četiri snimke; snimljene su prije (jedna snimka), tijekom (jedna snimka) i nakon poplave (dvije snimke). Procjena temeljena na jednokanalnoj metodi rezultirala je površinom manjom od površina procijenjenih primjenom višekanalnim metodama. Rezultati se mogu objasniti kompleksnošću spektralnog potpisa plitkih poplavnih voda s visokim udjelom suspendiranog nanosa koji će utjecati na refleksiju takvih površina u blisko IC dijelu spektra i klasificirati ih kao nevodene površine. S druge strane, kombiniranjem različitih spektralnih kanala u višekanalnim metodama kompenzira se utjecaj suspendiranog nanosa na refleksiju takvih voda te je klasifikacija na vodene i nevodene površine preciznija.

scholarly journals COVERAGE CHANGES DETECTION AT CIÉNAGA GRANDE, SANTA MARTA – COLOMBIA USING AUTOMATIC CLASSIFICATION

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 195-200
Author(s):  
J. S. Vinasco ◽  
D. A. Rodríguez ◽  
S. Velásquez ◽  
D. F. Quintero ◽  
L. R. Livni ◽  
...  
Keyword(s):  
Random Forest ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Confusion Matrix ◽  
Automatic Classification ◽  
Detection Methods ◽  
Landsat 8 ◽  
High Resolution Data ◽  
Water Index ◽  
Normalized Difference Water Index

Abstract. The Ciénaga Grande, Santa Marta is the largest and most diverse ecosystem of its kind in Colombia. Its primary function is acting as a filter for the organic carbon cycle. Recently, this place has been suffering disruptions due to the anthropic activities taking place in its surroundings. The present study, the changes in the surface of Ciénaga Grande, Santa Marta, Magdalena, Colombia between 2013 and 2018 were determined using semiautomatic detection methods with high resolution data from remote sensors (Landsat 8). The zone of studies was classified in six kinds of surfaces: 1) artificial territories, 2) agricultural territories, 3) forests and semi-natural areas, 4) wet areas, 5) deep water surfaces & 6) wich is related to clouds as a masking method. Random Forest classifiers were utilized and the Feed For Ward multilayer perceptron neuronal network (ANN) was simultaneously assessed. The training stage for both methods was performed with 300 samples, distributed in equal quantities, over each coverage class. The semi-automatic classification was carried out with an annual frequency, but the monitoring was carried out throughout the analysis period through the performance of three indicators Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI). It was found from the confusion matrix that the Random Forest method more accurately classified four classes while Neural Networks Analysis (NNA) just three. Finally, taking the Random Forest results into account, it was found that the agricultural expansion increased from 7% to 9% and the urban zone increased from 20% to 30% of the total area. As well as a decrease of damp areas from 27% to 12% and forests from 4% to 3% of the total area of study.

scholarly journals Diseño de una interfaz para la segmentación de agua, vegetación y ciudad en imágenes multiespectrales utilizando SVM (TOLTECA)

2019 ◽  
pp. 8-14
Author(s):  
Andrea González-Ramírez ◽  
Israel Yañez-Vargas ◽  
Jayro Santiago-Paz ◽  
Deni Torres-Román ◽  
Ramón Parra-Michel
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Support Vector ◽  
Landsat 8 ◽  
K Nearest Neighbors ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Vegetation Water ◽  
The Government ◽  
Human Losses

Floodings in Mexico generated economic and human losses in recent years, so it is necessary to use all possible tools that can help the government to reduce all these disasters, especially human losses. Therefore, a Graphical User Interface (GUI) was developed in Matlab for the segmentation and classification of vegetation, water and city in multispectral images obtained from the Landsat 8 satellite with the intention of detecting floods and vulnerable zones of flooding. The interface performs a feature extraction, segmentation, classification, validation and visualization of the final results obtained through basic segmentation algorithms such as the Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), in addition to performing the segmentation with one of the artificial intelligence methodologies most used in the state of the art: support vector machine (SVM) and the proposal of SVM with the k-nearest neighbors as an improvement to the algorithm.

scholarly journals Analysis on Thermal Islands Effectors in Ramadi City, Iraq Using Multi-temporal Landsat Images

2020 ◽  
Vol 1 (135) ◽  
pp. 67-78
Author(s):  
Ismael Abbas Hurat
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Landsat Images ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Landsat 7 ◽  
Multi Temporal

This paper analyzes the effects of urban density, vegetation cover, and water body on thermal islands measured by land surface temperature in Al Anbar province, Iraq using multi-temporal Landsat images. Images from Landsat 7 ETM and Landsat 8 OLI for the years 2000, 2014, and 2018 were collected, pre-processed, and anal yzed. The results suggested that the strongest correlation was found between the Normalized Difference Built-up Index (NDBI) and the surface temperature. The correlation between the Normalized Difference Vegetation Index (NDVI) and the surface temperature was slightly weaker compared to that of NDBI. However, the weakest correlation was found between the Normalized Difference Water Index (NDWI) and the temperature. The results obtained in this research may help the decision makers to take actions to reduce the effects of thermal islands by looking at the details in the produced maps and the analyzed values of these spectral indices.

scholarly journals Utilização de Índices Físicos a partir de Imagens OLI – TIRS para o Mapeamento de Uso e Cobertura da Terra no entorno do Aeroporto Internacional do Recife/Guararapes – Gilberto Freire

2019 ◽  
Vol 12 (3) ◽  
pp. 1039
Author(s):  
Claudianne Brainer De Souza Oliveira
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat 8 ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Land Cover Map ◽  
Area Use ◽  
Water Vegetation

Atualmente o uso de índices físicos NDVI (Normalized Difference Vegetacion Index), NDBI (Normalized Difference Built-up Index) e NDWI (Normalized Difference Water Index) vêm sendo muito utilizados como suporte para o mapeamento e monitoramento de uso e ocupação da terra. A área de estudo abrange o Aeroporto Internacional do Recife/Guararapes – Gilberto Freyre e o seu entorno, uma região na qual estão inseridos os municípios de Jaboatão dos Guararapes e Recife, ambos no Estado de Pernambuco. Utilizando imagens do satélite LANDSAT-8, sensor OLI de 18-06-2016, orbita-ponto 214-066, aplicou-se a técnica de fusão RGB-IHS para se obter uma melhor resolução espacial, logo após foram calculados os índices físicos, com o objetivo de avaliar o uso e ocupação do solo da área em questão. Como resultado final, obteve-se um mapa de uso e cobertura da terra, contendo quatro classes (solo exposto, água, vegetação e área construída), na escala de 1:50.000, no sistema de referência geodésico WGS84.  Physical indexes from OLI - TIRS images as tools for land use and coverage mapping around the airport International Recife / Guararapes - Gilberto Freire A B S T R A C TCurrently the use of NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-up Index) and NDWI (Normalized Difference Water Index) have been widely used as support for mapping and monitoring land use and occupation. The study area covers the Recife / Guararapes - Gilberto Freyre International Airport and its surroundings, a region in which the municipalities of Jaboatão dos Guararapes and Recife are located, both in the State of Pernambuco. Using images from the LANDSAT-8 satellite, OLI sensor of 06-06-2016, orbit-point 214-066, the RGB-IHS fusion technique was applied to obtain a better spatial resolution, after the physical indexes were calculated, with the objective of evaluating the land use and occupation of the area in question. As a final result, a land use and land cover map was obtained, containing four classes (exposed soil, water, vegetation and built area), in the 1: 50.000 scale, in the WGS84 geodetic reference system.Keywords: physical indexes, remote sensing, urban area, use and land cover.

scholarly journals Evaluando el desempeño de índices espectrales para identificar humedales alto andinos

2019 ◽  
pp. 59
Author(s):  
J. Aponte-Saravia ◽  
J. E. Ospina-Noreña
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Life Forms ◽  
High Mountain ◽  
Landsat 8 ◽  
Water Index ◽  
Tasseled Cap ◽  
Normalized Difference Water Index ◽  
Humidity Index ◽  
Index Index

<p>High Andean wetlands are habitats critical to life forms that have adapted to these extreme high mountain ecosystems, and for living beings that inhabit the lower parts of the basin; they are spaces that contain high diversity of flora and fauna characteristic of these places and are strongly associated with the water component. There lies the importance of identifying and monitoring ecosystems, using easy applicable methods and allowing results every two weeks approximately, they are inexpensive and highly reliable. Methods of monitoring in short periods, they are economically profitable and provide reliable information, they correspond to the evaluations by satellite images, specifically applying the methods of spectral indices. Thereby, the objective of the research was to evaluate the performance of six indices, considered to be the most used to identify high Andean wetlands (humidity index at surface level, normalized difference water index, normalized difference vegetation index, enhanced vegetation index, index of vegetation to the surface and tasseled CAP vegetation), in periods of low precipitation, using imagery Landsat 8 OLI. Comparing the performance of those indexes in the identification of wetlands through cross-validation and bootstrap statistical learning, the index that showed better performance was tasseled CAP vegetation, revealing the lowest value of the average of the mean square error of iterations between the test failure rate and training. The index tasseled CAP vegetation, shows greater reliability to identify and evaluate high Andean wetlands.</p>

scholarly journals DETECTION LAND COVER CHANGES OF THE BAQUBA CITY FOR THE PERIOD 2014-2019 USING SPECTRAL INDICES

2020 ◽  
Vol 51 (3) ◽  
pp. 805-815
Author(s):  
Khalaf & Al-Jibouri
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Landsat 8 ◽  
Fast Method ◽  
Barren Land ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Land Control ◽  
The City

This study was conducted on the Land coverings of the city of Baquba and its outskirts in Diyala province, central Iraq, between latitudes 44º 42ʹ 31.78ʺ ــ  44º33ʹ 14.99ʺ  and 33º41ʹ 46.66ʺ ــ  33º 48ʹ 23.18ʺ an area of 180,835 km2. In order to classify the earth covers, it was relied on the field survey to determine the grounding points. Also used two satellite data from Landsat 8, the first one on 23/3/2014, the second on 21/3/2019, and the production of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Differences Built- up the Index (NDBI) maps. The results of the survey was showed five varieties are vegetation cover, agricultural land, water, buildings and barren land. They were identified and compared with the 75 land control points, The accuracy of the classification was calculated using Kappa It was 89% , and purely concluded that the use of manuals NDVI, NDWI and NDBI was useful for classifying Land coverings and detecting changes as they are considered an easy and fast method.

Developing land-state maps of Tram Chim National Park in Vietnam based on monitoring results

2020 ◽  
Vol 963 (9) ◽  
pp. 53-64
Author(s):  
V.F. Kovyazin ◽  
Thi Lan Anh Dang ◽  
Viet Hung Dang
Keyword(s):  
National Park ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Likelihood Method ◽  
Wetland Area ◽  
Water Index ◽  
Effi Ciency ◽  
Normalized Difference Water Index ◽  
Specially Protected Natural Areas ◽  
Protected Natural Areas

Tram Chim National Park in Southern Vietnam is a wetland area included in the system of specially protected natural areas (SPNA). For the purposes of land monitoring, we studied Landsat-5 and Sentinel-2B images obtained in 1991, 2006 and 2019. The methods of normalized difference vegetation index (NDVI) and water objects – normalized difference water index (NDWI) were used to estimate the vegetation in National Park. The allocated land is classifi ed by the maximum likelihood method in ENVI 5.3 into categories. For each image, a statistical analysis of the land after classifi cation was performed. Between 1991 and 2019, land changes occurred in about 57 % of the Tram Chim National Park total area. As a result, the wetland area has signifi cantly reduced there due to climate change. However, the area of Melaleuca forests in Tram Chim National Park has increased due to the effi ciency of reforestation in protected areas. Melaleuca forests are also being restored.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Monitoring of Inland Surface Water Quality Using Remote Sensing on the Example of Wigry Lake

2018 ◽  
Vol 63 ◽  
pp. 00017
Author(s):  
Michał Lupa ◽  
Katarzyna Adamek ◽  
Renata Stypień ◽  
Wojciech Sarlej
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Surface Water Quality ◽  
Lake Area ◽  
Landsat Images ◽  
Secchi Disc ◽  
Water Index ◽  
Normalized Difference Water Index

The study examines how LANDSAT images can be used to monitor inland surface water quality effectively by using correlations between various indicators. Wigry lake (area 21.7 km2) was selected for the study as an example. The study uses images acquired in the years 1990–2016. Analysis was performed on data from 35 months and seven water condition indicators were analyzed: turbidity, Secchi disc depth, Dissolved Organic Material (DOM), chlorophyll-a, Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The analysis of results also took into consideration the main relationships described by the water circulation cycle. Based on the analysis of all indicators, clear trends describing a systematic improvement of water quality in Lake Wigry were observed.

scholarly journals Avaliação da precisão temática de composições de NDBI, NDVI, NDWI

2016 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Júlio Cezar Cotrim Moreira Filho ◽  
João Rodrigues Tavares Junior
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water Index ◽  
Normalized Difference Water Index

Este artigo trata de experimentos usando composições de índices físicos NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), NDBI (Normalized Difference Built-Up Index), para avaliar a precisão temática do classificador Máxima Verossimilhança (MAXVER) usando exatidão global, índice kappa e teste Z. A área de estudo foi o entorno da Lagoa Olho D’Água localizada em Jaboatão dos Guararapes-PE. Foram usadas duas cenas TM LANDSAT-5, órbita-ponto 214-066, de 17/03/20111 e 29/09/2011, do DGI-INPE (Divisão de Geração de Imagens do Instituto Nacional de Pesquisas Espaciais), e todo o processamento realizado no SPRING 5.0.6. Os resultados indicam que apenas usando índices físicos substituindo composições RGB, a acurácia temática é muito degradada. A classificação MAXVER da composição NDBI-TM4-TM-3 e IHS obtiveram bons resultados em acurácia temática, demonstrando que o método de combinações de índices físicos e composições RGB podem ser usados para melhorar os resultados da classificação MAXVER.

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