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

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.

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

Irriga ◽

10.15809/irriga.2017v22n2p330-342 ◽

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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TEMPERATURA DE SUPERFÍCIE E A RELAÇÃO COM O ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) NA MICROBACIA DO RIO DA BATATEIRAS, CRATO-CE

Revista da Casa da Geografia de Sobral (RCGS) ◽

10.35701/rcgs.v21n2.548 ◽

2019 ◽

Vol 21 (2) ◽

pp. 1310-1320

Author(s):

Cícera Celiane Januário da Silva ◽

Vinicius Ferreira Luna ◽

Joyce Ferreira Gomes ◽

Juliana Maria Oliveira Silva

Keyword(s):

Remote Sensing ◽

Surface Temperature ◽

Temperature Measurement ◽

Rainy Season ◽

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landsat 8 ◽

Low Density ◽

Difference Map

O objetivo do presente trabalho é fazer uma comparação entre a temperatura de superfície e o Índice de Vegetação por Diferença Normalizada (NDVI) na microbacia do rio da Batateiras/Crato-CE em dois períodos do ano de 2017, um chuvoso (abril) e um seco (setembro) como também analisar o mapa de diferença de temperatura nesses dois referidos períodos. Foram utilizadas imagens de satélite LANDSAT 8 (banda 10) para mensuração de temperatura e a banda 4 e 5 para geração do NDVI. As análises demonstram que no mês de abril a temperatura da superfície variou aproximadamente entre 23.2ºC e 31.06ºC, enquanto no mês correspondente a setembro, os valores variaram de 25°C e 40.5°C, sendo que as maiores temperaturas foram encontradas em locais com baixa densidade de vegetação, de acordo com a carta de NDVI desses dois meses. A maior diferença de temperatura desses dois meses foi de 14.2°C indicando que ocorre um aumento da temperatura proporcionado pelo período que corresponde a um dos mais secos da região, diferentemente de abril que está no período de chuvas e tem uma maior umidade, presença de vegetação e corpos d’água que amenizam a temperatura.Palavras-chave: Sensoriamento Remoto; Vegetação; Microbacia. ABSTRACTThe objective of the present work is to compare the surface temperature and the Normalized Difference Vegetation Index (NDVI) in the Batateiras / Crato-CE river basin in two periods of 2017, one rainy (April) and one (September) and to analyze the temperature difference map in these two periods. LANDSAT 8 (band 10) satellite images were used for temperature measurement and band 4 and 5 for NDVI generation. The analyzes show that in April the surface temperature varied approximately between 23.2ºC and 31.06ºC, while in the month corresponding to September, the values ranged from 25ºC and 40.5ºC, and the highest temperatures were found in locations with low density of vegetation, according to the NDVI letter of these two months. The highest difference in temperature for these two months was 14.2 ° C, indicating that there is an increase in temperature provided by the period that corresponds to one of the driest in the region, unlike April that is in the rainy season and has a higher humidity, presence of vegetation and water bodies that soften the temperature.Key-words: Remote sensing; Vegetation; Microbasin.RESUMENEl objetivo del presente trabajo es hacer una comparación entre la temperatura de la superficie y el Índice de Vegetación de Diferencia Normalizada (NDVI) en la cuenca Batateiras / Crato-CE en dos períodos de 2017, uno lluvioso (abril) y uno (Septiembre), así como analizar el mapa de diferencia de temperatura en estos dos períodos. Las imágenes de satélite LANDSAT 8 (banda 10) se utilizaron para la medición de temperatura y las bandas 4 y 5 para la generación de NDVI. Los análisis muestran que en abril la temperatura de la superficie varió aproximadamente entre 23.2ºC y 31.06ºC, mientras que en el mes correspondiente a septiembre, los valores oscilaron entre 25 ° C y 40.5 ° C, y las temperaturas más altas se encontraron en lugares con baja densidad de vegetación, según el gráfico NDVI de estos dos meses. La mayor diferencia de temperatura de estos dos meses fue de 14.2 ° C, lo que indica que hay un aumento en la temperatura proporcionada por el período que corresponde a uno de los más secos de la región, a diferencia de abril que está en la temporada de lluvias y tiene una mayor humedad, presencia de vegetación y cuerpos de agua que suavizan la temperatura.Palabras clave: Detección remota; vegetación; Cuenca.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040257 ◽

2020 ◽

Vol 9 (4) ◽

pp. 257 ◽

Cited By ~ 3

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.

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CHARACTERIZING LANDSCAPE SPATIAL HETEROGENEITY USING SEMIVARIOGRAM PARAMETERS DERIVED FROM NDVI IMAGES

CERNE ◽

10.1590/01047760201723042370 ◽

2017 ◽

Vol 23 (4) ◽

pp. 413-422 ◽

Cited By ~ 1

Author(s):

Eduarda Martiniano de Oliveira Silveira ◽

José Márcio de Mello ◽

Fausto Weimar Acerbi Júnior ◽

Aliny Aparecida dos Reis ◽

Kieran Daniel Withey ◽

...

Keyword(s):

Remote Sensing ◽

Spatial Heterogeneity ◽

Spatial Resolution ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landscape Heterogeneity ◽

Landsat 8 ◽

Remotely Sensed Data ◽

Object Based ◽

Geostatistical Techniques

ABSTRACT Assuming a relationship between landscape heterogeneity and measures of spatial dependence by using remotely sensed data, the aim of this work was to evaluate the potential of semivariogram parameters, derived from satellite images with different spatial resolutions, to characterize landscape spatial heterogeneity of forested and human modified areas. The NDVI (Normalized Difference Vegetation Index) was generated in an area of Brazilian amazon tropical forest (1,000 km²). We selected samples (1 x 1 km) from forested and human modified areas distributed throughout the study area, to generate the semivariogram and extract the sill (σ²-overall spatial variability of the surface property) and range (φ-the length scale of the spatial structures of objects) parameters. The analysis revealed that image spatial resolution influenced the sill and range parameters. The average sill and range values increase from forested to human modified areas and the greatest between-class variation was found for LANDSAT 8 imagery, indicating that this image spatial resolution is the most appropriate for deriving sill and range parameters with the intention of describing landscape spatial heterogeneity. By combining remote sensing and geostatistical techniques, we have shown that the sill and range parameters of semivariograms derived from NDVI images are a simple indicator of landscape heterogeneity and can be used to provide landscape heterogeneity maps to enable researchers to design appropriate sampling regimes. In the future, more applications combining remote sensing and geostatistical features should be further investigated and developed, such as change detection and image classification using object-based image analysis (OBIA) approaches.

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Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

HAYATI Journal of Biosciences ◽

10.4308/hjb.26.3.117 ◽

2019 ◽

Vol 26 (3) ◽

pp. 117

Author(s):

Tri Muji Susantoro ◽

Ketut Wikantika ◽

Agung Budi Harto ◽

Deni Suwardi

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Landsat 8 ◽

Gas Field ◽

Research Results ◽

The Third ◽

Third Phase ◽

The Impact

This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.

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Drought Risk Assessment in the Khushab Region of Pakistan Using Satellite Remote Sensing and Geospatial Methods

International Journal of Economic and Environmental Geology ◽

10.46660/ijeeg.vol10.iss1.2019.217 ◽

2019 ◽

Vol 10 (1) ◽

pp. 48-56 ◽

Cited By ~ 4

Author(s):

Muhammad Khubaib Abuzar ◽

Muhammad Shafiq ◽

Syed Amer Mahmood ◽

Muhammad Irfan ◽

Tayyaba Khalil ◽

...

Keyword(s):

Remote Sensing ◽

Social Life ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Standardized Precipitation Index ◽

Temporal Analysis ◽

Agricultural Drought ◽

Natural Phenomenon ◽

Drought Risk ◽

Gis And Remote Sensing

Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.

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

Irriga ◽

10.15809/irriga.2015v1n2p30 ◽

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.

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The NDVI-CV Method for Mapping Evergreen Trees in Complex Urban Areas Using Reconstructed Landsat 8 Time-Series Data

Forests ◽

10.3390/f10020139 ◽

2019 ◽

Vol 10 (2) ◽

pp. 139 ◽

Cited By ~ 4

Author(s):

Yingying Yang ◽

Taixia Wu ◽

Shudong Wang ◽

Jing Li ◽

Farhan Muhanmmad

Keyword(s):

Remote Sensing ◽

Time Series ◽

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Series Data ◽

Series Data ◽

Distribution Data ◽

Landsat 8 ◽

Oxygen Balance

Evergreen trees play a significant role in urban ecological services, such as air purification, carbon and oxygen balance, and temperature and moisture regulation. Remote sensing represents an essential technology for obtaining spatiotemporal distribution data for evergreen trees in cities. However, highly developed subtropical cities, such as Nanjing, China, have serious land fragmentation problems, which greatly increase the difficulty of extracting evergreen trees information and reduce the extraction precision of remote-sensing methods. This paper introduces a normalized difference vegetation index coefficient of variation (NDVI-CV) method to extract evergreen trees from remote-sensing data by combining the annual minimum normalized difference vegetation index (NDVIann-min) with the CV of a Landsat 8 time-series NDVI. To obtain an intra-annual, high-resolution time-series dataset, Landsat 8 cloud-free and partially cloud-free images over a three-year period were collected and reconstructed for the study area. Considering that the characteristic growth of evergreen trees remained nearly unchanged during the phenology cycle, NDVIann-min is the optimal phenological node to separate this information from that of other vegetation types. Furthermore, the CV of time-series NDVI considers all of the phenologically critical phases; therefore, the NDVI-CV method had higher extraction accuracy. As such, the approach presented herein represents a more practical and promising method based on reasonable NDVIann-min and CV thresholds to obtain spatial distribution data for evergreen trees. The experimental verification results indicated a comparable performance since the extraction accuracy of the model was over 85%, which met the classification accuracy requirements. In a cross-validation comparison with other evergreen trees’ extraction methods, the NDVI-CV method showed higher sensitivity and stability.

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Effect of Locust Invasion and Mitigation Using Remote Sensing Techniques: A Case Study of North Sindh Pakistan

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.21-00025r2 ◽

2022 ◽

Vol 88 (1) ◽

pp. 47-53

Author(s):

Muhammad Nasar Ahmad ◽

Zhenfeng Shao ◽

Orhan Altan

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Google Earth ◽

Landsat 8 ◽

Abrupt Decrease ◽

Computing Platform ◽

Remote Sensing Techniques ◽

Processing Techniques

This study comprises the identification of the locust outbreak that happened in February 2020. It is not possible to conduct ground-based surveys to monitor such huge disasters in a timely and adequate manner. Therefore, we used a combination of automatic and manual remote sensing data processing techniques to find out the aftereffects of locust attack effectively. We processed MODIS -normalized difference vegetation index (NDVI ) manually on ENVI and Landsat 8 NDVI using the Google Earth Engine (GEE ) cloud computing platform. We found from the results that, (a) NDVI computation on GEE is more effective, prompt, and reliable compared with the results of manual NDVI computations; (b) there is a high effect of locust disasters in the northern part of Sindh, Thul, Ghari Khairo, Garhi Yaseen, Jacobabad, and Ubauro, which are more vulnerable; and (c) NDVI value suddenly decreased to 0.68 from 0.92 in 2020 using Landsat NDVI and from 0.81 to 0.65 using MODIS satellite imagery. Results clearly indicate an abrupt decrease in vegetation in 2020 due to a locust disaster. That is a big threat to crop yield and food production because it provides a major portion of food chain and gross domestic product for Sindh, Pakistan.

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Spatio-Temporal Analysis of Satellite Imagery (NDVI) to Identify Terroir and Vineyard Yeast Differences According to Appellation of Origin (AOP) and Biogeographic Origin

J ◽

10.3390/j4030020 ◽

2021 ◽

Vol 4 (3) ◽

pp. 244-256

Author(s):

Sergio Vélez ◽

Enrique Barajas ◽

Pilar Blanco ◽

José Antonio Rubio ◽

David Castrillo

Keyword(s):

Remote Sensing ◽

Satellite Imagery ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Analysis ◽

Landsat 8 ◽

Appellation Of Origin ◽

Core Concepts ◽

Spatio Temporal ◽

Plant Habitat

Terroir is one of the core concepts associated with wine and presumes that the land from which the grapes are grown, the plant habitat, imparts a unique quality that is specific to that growing site. Additionally, numerous factors can influence yeast diversity, and terroir is among the most relevant. Therefore, it can be interesting to use Remote Sensing tools that help identify and give helpful information about the terroir and key characteristics that define the AOP (Appellation of Origin). In this study, the NDVI (Normalized Difference Vegetation Index) calculated from Landsat 8 imagery was used to perform a spatio-temporal analysis during 2013, 2014, and 2015 of several vineyards belonging to four different AOP in Galicia (Spain). This work shows that it is possible to use Remote Sensing for AOP delimitation. Results suggest: (i) satellite imagery can establish differences in terroir, (ii) the higher the NDVI, the higher the yeast species richness, (iii) the relationship between NDVI, terroir, and yeasts shows a stable trend over the years (Pearson’s r = 0.3894, p = 0.0119).

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