scholarly journals A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)

2021 ◽  
Vol 11 (4) ◽  
pp. 1859
Author(s):  
Francisco Carreño-Conde ◽  
Ana Elizabeth Sipols ◽  
Clara Simón de Blas ◽  
David Mostaza-Colado
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Dynamics ◽  
Environmental Changes ◽  
Multiple Scales ◽  
Climatic Factors ◽  
Vegetation Indices ◽  
Arid Zones ◽  
Electromagnetic Spectrum ◽  
Combination Of Forecasts

Vegetation dynamics is very sensitive to environmental changes, particularly in arid zones where climate change is more prominent. Therefore, it is very important to investigate the response of this dynamics to those changes and understand its evolution according to different climatic factors. Remote sensing techniques provide an effective system to monitor vegetation dynamics on multiple scales using vegetation indices (VI), calculated from remote sensing reflectance measurements in the visible and infrared regions of the electromagnetic spectrum. In this study, we use the normalized difference vegetation index (NDVI), provided from the MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor. NDVI is frequent in studies related to vegetation mapping, crop state indicator, biomass estimator, drought monitoring and evapotranspiration. In this paper, we use a combination of forecasts to perform time series models and predict NDVI time series derived from optical remote sensing data. The proposed ensemble is constructed using forecasting models based on time series analysis, such as Double Exponential Smoothing and autoregressive integrated moving average with explanatory variables for a better prediction performance. The method is validated using different maize plots and one olive plot. The results after combining different models show the positive influence of several weather measures, namely, temperature, precipitation, humidity and radiation.

scholarly journals MONITORAMENTO DA COBERTURA VEGETAL POR SENSORIAMENTO REMOTO NO SEMIÁRIDO BRASILEIRO ATRAVÉS DE ÍNDICES DE VEGETAÇÃO

Nativa ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 708
Author(s):  
Caio Victor Santos Silva ◽  
Jhon Lennon Bezerra da Silva ◽  
Geber Barbosa De Albuquerque Moura ◽  
Pabrício Marcos Oliveira Lopes ◽  
Cristina Rodrigues Nascimento ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Environmental Changes ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Digital Processing ◽  
Semiarid Region ◽  
Dry Period ◽  
Biophysical Parameters ◽  
Brazilian Semiarid

São necessárias medidas que visem à proteção e conservação dos recursos hídricos e naturais de forma rápida e eficiente. As técnicas de sensoriamento remoto são essenciais para o monitoramento ambiental dos recursos no semiárido no espaço e no tempo. Objetivou-se monitorar e analisar à dinâmica da cobertura vegetal através da variabilidade espaço-temporal do albedo da superfície e índices de vegetação em região de Caatinga do semiárido brasileiro por sensoriamento remoto. A área de estudo é o município de Arcoverde, localizado no semiárido de Pernambuco. O estudo foi desenvolvido através de seis imagens orbitais do Landsat-5 do sensor TM. O processamento digital dos parâmetros biofísicos foi realizado pelo algoritmo SEBAL. Os resultados foram analisados através da estatística descritiva e quanto a sua variabilidade. Áreas possivelmente degradadas foram identificadas pelos altos valores de albedo e índices de vegetação significativamente menores, localizadas à sudoeste e noroeste da região. Os índices apresentaram comportamento similares, principalmente no período seco, com baixos valores sendo próximos de zero, áreas afetadas pelo período de seca no semiárido. O SAVI apresentou maior precisão, destacando melhor resposta espectral da vegetação. O sensoriamento remoto promoveu monitoramento espaço-temporal adequado, destacando principalmente o período classificado como climaticamente seco através do albedo e índices de vegetação.Palavras-chave: Caatinga; NDVI; SAVI; mudanças ambientais; SEBAL. MONITORING OF VEGETATION COVER BY REMOTE SENSING IN BRAZILIAN SEMIARID THROUGH VEGETATION INDICES ABSTRACT: Measures are needed aimed at the protection and conservation of water and natural resources quickly and efficiently. Remote sensing techniques are essential for the environmental monitoring of resources in the semiarid region in space and time. Aimed to monitor and analyze the dynamics of vegetation cover through the spatial-temporal variability of the surface albedo and indices of vegetation in the Caatinga region of the Brazilian semiarid by remote sensing. The study area is the municipality of Arcoverde, located in the semiarid of Pernambuco. The study was developed through six orbital images of Landsat-5 of the TM sensor. The digital processing of the biophysical parameters was performed by the SEBAL algorithm. The results were analyzed through descriptive statistics and their variability. Possibly degraded areas were identified by high albedo values and significantly lower vegetation indices, located in the southwest and northwest of the region. The indexes showed similar behavior, mainly in the dry period, with low values being close to zero, areas affected by the dry period in the semiarid. The SAVI presented higher accuracy, highlighting better spectral response of the vegetation. Remote sensing promoted adequate space-time monitoring, highlighting mainly the period classified as climatically dry through the albedo and vegetation indexes.Keywords: Caatinga; NDVI; SAVI; environmental changes; SEBAL.

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 INTEGRATION OF HUMAN PARTICIPATORY SENSING AND ARCHIVES OF REMOTE SENSING OBSERVATIONS FOR FIELD LEVEL CROP PHENOLOGY ESTIMATION

2018 ◽  
Vol XLII-4 ◽  
pp. 547-550
Author(s):  
S. A. Sawant ◽  
J. D. Mohite ◽  
S. Pappula
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Indices ◽  
Crop Growth ◽  
Growth Stages ◽  
Growth Monitoring ◽  
Specific Information ◽  
Crop Phenology ◽  
Field Level ◽  
Sentinel 2

<p><strong>Abstract.</strong> The rise in global population has increased food and water demand thereby causing excessive pressure on existing resources. In developing countries with fragmented land holdings there exists constant pressure on available water and land resources. Obtaining field scale crop specific information is challenging task. Advent of open freely available multi-temporal remote sensing observations with improved radiometric resolution the possibilities for near real / real time applications has increased. In this study and an attempt has been made to establish operational model for field level crop growth monitoring using integrated approach of crowd sourcing and time series of remote sensing observations. The time series of Sentinel 2 (A and B) satellite has been used to estimate crop growth related components such as vegetation indices and crop growth stage and crop phenology. In initial stage high valued cereal crop Wheat has been selected. The field level information (i.e. 108 Wheat fields) collected using mobile based agro-advisory platform mKRISHI&amp;reg; has been used to extract time series of Sentinel 2 observations (44 scenes for year 2016 and 2018). The moving average has been used for filling gaps in the time series of vegetation indices. The BFAST and GreenBrown package in R were used for detecting breaks in vegetation index time series and estimating crop growth stages. Analysis shows that the estimated crop phenology parameters were in better agreement with the field observations. In future more crops from different agro-climatic conditions will be considered for providing field level crop management advisory.</p>

Validation of seasonal time series of remote sensing derived LAI for hydrological modelling

2020 ◽  
Author(s):  
Charlotte Wirion ◽  
Boud Verbeiren ◽  
Sindy Sterckx
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Urban Environment ◽  
Vegetation Dynamics ◽  
Heat Waves ◽  
Field Work ◽  
Hydrological Modelling ◽  
Urban Environments ◽  
Urban Vegetation ◽  
Area Index

&lt;p&gt;In urban environments, due to climate change urban heat waves are predicted to occur more frequently. Urban vegetation and the linked evapotranspiration rate can play a mitigating role. However, a major challenge in urban hydrological modelling remains the mapping of vegetation dynamics and its role in hydrological processes in particular interception storage and evapotranspiration. Conventional mapping of vegetation usually implies intensive labor and time consuming field work. We explore the potential of different remote sensing sensors (Proba-V, Landsat, Sentinel2, Apex) to characterize the urban vegetation dynamics for hydrological modelling. The here proposed remote sensing sensors show differences in the spectral and spatial resolutions as well as in their revisit time. However, in the urban environment we need a high spatial and spectral resolution to distinguish the urban landcover and a frequent revisit time to capture seasonal vegetation dynamics. Therefore, we propose a combination of different remote sensing sensors to derive leaf area index (LAI) timeseries in the urban environment. To improve the consistency in time series generated from different remote sensing sources a harmonization of the multi-sensor time series is proposed and validated with a multi-resolution validation approach using ground-truthing LAI (BELHARMONY project). The LAI timeseries, derived from the different remote sensing sensors, are then introduced into the hydrological modelling framework for a location- and time- specific assessment of the interception storage and evapotranspiration component. The effect of the sensor differences to the LAI timeseries on the hydrological response is analyzed.&lt;/p&gt;

scholarly journals Time series and beyond: multifaceted plankton research at a marine Mediterranean LTER site

2019 ◽  
Vol 34 ◽  
pp. 273-310 ◽  
Author(s):  
Adriana Zingone ◽  
Domenico D'Alelio ◽  
Maria Grazia Mazzocchi ◽  
Marina Montresor ◽  
Diana Sarno ◽  
...  
Keyword(s):  
Time Series ◽  
Environmental Changes ◽  
Multiple Scales ◽  
Algal Species ◽  
Taxonomic Diversity ◽  
Life Cycles ◽  
Trophic Relationships ◽  
Primary Role ◽  
Long Time Series ◽  
Long Time

Plankton are a pivotal component of the diversity and functioning of coastal marine ecosystems. A long time-series of observations is the best tool to trace their patterns and variability over multiple scales, ultimately providing a sound foundation for assessing, modelling and predicting the effects of anthropogenic and natural environmental changes on pelagic communities. At the same time, a long time-series constitutes a formidable asset for different kinds of research on specific questions that emerge from the observations, whereby the results of these complementary studies provide precious interpretative tools that augment the informative value of the data collected. In this paper, we review more than 140 studies that have been developed around a Mediterranean plankton time series gathered in the Gulf of Naples at the station LTER-MC since 1984. These studies have addressed different topics concerning marine plankton, which have included: i) seasonal patterns and trends; ii) taxonomic diversity, with a focus on key or harmful algal species and the discovery of many new taxa; iii) molecular diversity of selected species, groups of species or the whole planktonic community; iv) life cycles of several phyto- and zooplankton species; and v) interactions among species through trophic relationships, parasites and viruses. Overall, the products of this research demonstrate the great value of time series besides the record of fluctuations and trends, and highlight their primary role in the development of the scientific knowledge of plankton much beyond the local scale.

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