scholarly journals Avaliação da regeneração natural e da recuperação ambiental em áreas de depósitos de expurgos no âmbito do Projeto de Integração do São Francisco com as Bacias do Nordeste Setentrional – PISF (Evaluation of natural regeneration and environmental...)

2017 ◽  
Vol 10 (4) ◽  
pp. 1199
Author(s):  
Getúlio Ezequiel da Costa Peixoto Filho ◽  
Edilson De Souza Bias
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Natural Regeneration ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geographic Information ◽  
Erosion Processes ◽  
Main Instrument ◽  
High Degree

O Projeto de Integração da Bacia do São Francisco com as Bacias do Nordeste Setentrional – PISF é dotado de diversos tipos de obras e intervenções, tais como aquedutos, estações elevatórias, barragens, canais, formando – em seu conjunto – uma obra com alto grau de complexidade, produzindo diversos impactos ambientais e degradando muitas áreas. Dentre os 38 programas ambientais no âmbito do Projeto Básico Ambiental – PBA do PISF, há o PBA 09 “Programa de Recuperação de Áreas Degradadas” que tem por objetivo evitar o agravamento de processos erosivos e o comprometimento dos canais de água, assim como possibilitar a retomada do uso original ou alternativo das áreas onde houver intervenção. A proposta do presente estudo surgiu no sentido de complementar as ações adotados no âmbito do PBA 09. Nesse, avaliou-se a regeneração/ recuperação em áreas de depósitos de expurgos a partir de ferramentas de sensoriamento remoto, Sistemas de Informações Geográficas – SIG e dados de campo. Para tanto, o principal instrumento utilizado para avaliar a regeneração/ recuperação foi o Índice de Vegetação por Diferença Normalizada. A partir da obtenção do índice de vegetação e estabelecimento de classes de cobertura do solo através do Software Envi 5.1@, realizou-se a integração e análise com o uso do software ArcGis 10.1@. Os dados de campo utilizados para avaliar a regeneração/ recuperação ambiental foram as áreas onde receberam depósitos de expurgos. De acordo com os métodos adotados neste trabalho pode-se perceber que as ações de regeneração/ recuperação em áreas de depósitos de expurgos adotadas foram pouco expressivas.   A B S T R A C TThe Project for the Integration of the São Francisco Basin with the Northern Northeast Basins - PISF is endowed with many types of constructions and interventions, such as aqueducts, elevation stations, dams, canals, forming – together – a work with a high degree of complexity, producing several environmental impacts and degrading many areas. Among the 38 programs in the scope of the Basic Environmental Project - BEP of the PISF, there is the BEP 09 ";Program for the Recovery of Degraded Areas";, whose objective is to avoid the worsening of erosion processes and the impairment of water channels, as well as to enable a resumption of usage of original or alternate areas of intervention. The proposal of the present study emerged in the sense to complement the actions adopted in the scope of PBA 09. In this study the regeneration / recovery in areas of waste disposal sites was evaluated using remote sensing tools, Geographic Information Systems - GIS and in field data. For this, the main instrument used to evaluate the regeneration / recovery was the Normalized Difference Vegetation Index. From the collect of the vegetation index and the establishment of coverage classes of the soil through Envi 5.1® software, and integrations and analysis with the ArcGis 10.1® program. The field data used to evaluate the environmental regeneration / recovery were the areas that received deposits of purges. According to the methods adopted in this study it can be seen that the regeneration / recovery actions in areas of purge deposits were not very significant.Keywords: Natural Regeneration; Recovery of Degraded Areas; Vegetation Index; Remote Sensing; Geographic Information System. 

scholarly journals Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

2013 ◽  
Vol 10 (10) ◽  
pp. 6279-6307 ◽  
Author(s):  
E. Boegh ◽  
R. Houborg ◽  
J. Bienkowski ◽  
C. F. Braban ◽  
T. Dalgaard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Field Measurements ◽  
Leaf Nitrogen ◽  
Agricultural Landscapes ◽  
Nitrogen Pools ◽  
Spectral Vegetation Indices

Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and play a significant role in the global cycles of carbon, nitrogen and water. The purpose of this study is to use field-based and satellite remote-sensing-based methods to assess leaf nitrogen pools in five diverse European agricultural landscapes located in Denmark, Scotland (United Kingdom), Poland, the Netherlands and Italy. REGFLEC (REGularized canopy reFLECtance) is an advanced image-based inverse canopy radiative transfer modelling system which has shown proficiency for regional mapping of leaf area index (LAI) and leaf chlorophyll (CHLl) using remote sensing data. In this study, high spatial resolution (10–20 m) remote sensing images acquired from the multispectral sensors aboard the SPOT (Satellite For Observation of Earth) satellites were used to assess the capability of REGFLEC for mapping spatial variations in LAI, CHLland the relation to leaf nitrogen (Nl) data in five diverse European agricultural landscapes. REGFLEC is based on physical laws and includes an automatic model parameterization scheme which makes the tool independent of field data for model calibration. In this study, REGFLEC performance was evaluated using LAI measurements and non-destructive measurements (using a SPAD meter) of leaf-scale CHLl and Nl concentrations in 93 fields representing crop- and grasslands of the five landscapes. Furthermore, empirical relationships between field measurements (LAI, CHLl and Nl and five spectral vegetation indices (the Normalized Difference Vegetation Index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green chlorophyll index) were used to assess field data coherence and to serve as a comparison basis for assessing REGFLEC model performance. The field measurements showed strong vertical CHLl gradient profiles in 26% of fields which affected REGFLEC performance as well as the relationships between spectral vegetation indices (SVIs) and field measurements. When the range of surface types increased, the REGFLEC results were in better agreement with field data than the empirical SVI regression models. Selecting only homogeneous canopies with uniform CHLl distributions as reference data for evaluation, REGFLEC was able to explain 69% of LAI observations (rmse = 0.76), 46% of measured canopy chlorophyll contents (rmse = 719 mg m−2) and 51% of measured canopy nitrogen contents (rmse = 2.7 g m−2). Better results were obtained for individual landscapes, except for Italy, where REGFLEC performed poorly due to a lack of dense vegetation canopies at the time of satellite recording. Presence of vegetation is needed to parameterize the REGFLEC model. Combining REGFLEC- and SVI-based model results to minimize errors for a "snap-shot" assessment of total leaf nitrogen pools in the five landscapes, results varied from 0.6 to 4.0 t km−2. Differences in leaf nitrogen pools between landscapes are attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. In order to facilitate a substantial assessment of variations in Nl pools and their relation to landscape based nitrogen and carbon cycling processes, time series of satellite data are needed. The upcoming Sentinel-2 satellite mission will provide new multiple narrowband data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing capabilities for mapping LAI, CHLl and Nl.

scholarly journals Land use and Land cover Mapping Based on Normalized Difference Vegetation Index using Remote Sensing and Geographical Information System in Banjar River Watershed of Narmada Basin

2017 ◽  
Vol 12 (3) ◽  
pp. 678-684
Author(s):  
Jagriti Tiwari ◽  
S.K. Sharma ◽  
R.J. Patil
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Information System ◽  
Land Cover ◽  
Geographical Information System ◽  
Water Body ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geographical Information ◽  
River Watershed

The spatial analysis of land use and land cover (LULC) dynamics is necessary for sustainable utilization and management of the land resources of an area. Remote sensing along with Geographical Information System emerged as an effective technique for mapping the LU/LC categories of an area in an efficient and cost-effective manner. The present study was conducted in Banjar river watershed located in Balaghat and Mandla district of Madhya Pradesh, India. The Normalized Difference Vegetation Index (NDVI) approach was adopted for LU/LC classification of study area. The Landsat-8 satellite data of year 2013 was selected for the classification purpose. The NDVI values were generated in ERDAS Imagine 2011 software and LU/LC map was prepared in ARC GIS environment. On the basis of NDVI values five LU/LC classes were recognized in the study area namely river & water body, waste land & habitation, forest, agriculture/other vegetation, open land/fallow land/barren land. The forest cover was found to be highly distributed in the study area with an extent of 115811 ha and least area was found to be covered under river and water body (4057.28 ha). This research work will be helpful for the policy makers for proper formulation and implementation of watershed developmental plans.

scholarly journals Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

2012 ◽  
Vol 9 (8) ◽  
pp. 10149-10205 ◽  
Author(s):  
E. Boegh ◽  
R. Houborg ◽  
J. Bienkowski ◽  
C. F. Braban ◽  
T. Dalgaard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Nitrogen Content ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Leaf Nitrogen ◽  
Nitrogen Pools ◽  
Canopy Nitrogen ◽  
Use Categories

Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and they play a significant role in the global cycles of carbon, nitrogen and water. Remote sensing data from satellites can be used to estimate leaf area index (LAI), leaf chlorophyll (CHLl) and leaf nitrogen density (Nl). However, methods are often developed using plot scale data and not verified over extended regions that represent a variety of soil spectral properties and canopy structures. In this paper, field measurements and high spatial resolution (10–20 m) remote sensing images acquired from the HRG and HRVIR sensors aboard the SPOT satellites were used to assess the predictability of LAI, CHLl and Nl. Five spectral vegetation indices (SVIs) were used (the Normalized Difference Vegetation index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green Chlorophyll Index) together with the image-based inverse canopy radiative transfer modelling system, REGFLEC (REGularized canopy reFLECtance). While the SVIs require field data for empirical model building, REGFLEC can be applied without calibration. Field data measured in 93 fields within crop- and grasslands of five European landscapes showed strong vertical CHLl gradient profiles in 20% of fields. This affected the predictability of SVIs and REGFLEC. However, selecting only homogeneous canopies with uniform CHLl distributions as reference data for statistical evaluation, significant (p < 0.05) predictions were achieved for all landscapes, by all methods. The best performance was achieved by REGFLEC for LAI (r2=0.7; rmse = 0.73), canopy chlorophyll content (r2=0.51; rmse = 439 mg m−2) and canopy nitrogen content (r2 = 0.53; rmse = 2.21 g m−2). Predictabilities of SVIs and REGFLEC simulations generally improved when constrained to single land use categories (wheat, maize, barley, grass) across the European landscapes, reflecting sensitivity to canopy structures. Predictability further improved when constrained to local (10 × 10 km2) landscapes, thereby reflecting sensitivity to local environmental conditions. All methods showed different predictabilities for land use categories and landscapes. Combining the best methods, LAI, canopy chlorophyll content (CHLc) and canopy nitrogen content (CHLc) for the five landscapes could be predicted with improved accuracy (LAI rmse = 0.59; CHLc rmse = 346 g m−2; Ncrmse = 1.49 g m−2). Remote sensing-based results showed that the vegetation nitrogen pools of the five agricultural landscapes varied from 0.6 to 4.0 t km−2. Differences in nitrogen pools were attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. Information on Nl and total Nc pools within the landscapes is important for the spatial evaluation of nitrogen and carbon cycling processes. The upcoming Sentinel-2 satellite mission will provide new multiple narrow-band data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing predictabilities of LAI, CHLl and Nl.

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

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.

scholarly journals A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods

2021 ◽  
Vol 13 (9) ◽  
pp. 1818
Author(s):  
Lisha Ding ◽  
Lei Ma ◽  
Longguo Li ◽  
Chao Liu ◽  
Naiwen Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Flash Flood ◽  
Flash Floods ◽  
Geographic Information ◽  
Flood Disaster ◽  
Future Research ◽  
Time Zone ◽  
Gis Technologies

Flash floods are among the most dangerous natural disasters. As climate change and urbanization advance, an increasing number of people are at risk of flash floods. The application of remote sensing and geographic information system (GIS) technologies in the study of flash floods has increased significantly over the last 20 years. In this paper, more than 200 articles published in the last 20 years are summarized and analyzed. First, a visualization analysis of the literature is performed, including a keyword co-occurrence analysis, time zone chart analysis, keyword burst analysis, and literature co-citation analysis. Then, the application of remote sensing and GIS technologies to flash flood disasters is analyzed in terms of aspects such as flash flood forecasting, flash flood disaster impact assessments, flash flood susceptibility analyses, flash flood risk assessments, and the identification of flash flood disaster risk areas. Finally, the current research status is summarized, and the orientation of future research is also discussed.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals Remote Sensing for Plant Water Content Monitoring: A Review

2021 ◽  
Vol 13 (11) ◽  
pp. 2088
Author(s):  
Carlos Quemada ◽  
José M. Pérez-Escudero ◽  
Ramón Gonzalo ◽  
Iñigo Ederra ◽  
Luis G. Santesteban ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Continuous Wave ◽  
Technological Development ◽  
Water Status ◽  
Irrigation Management ◽  
Coefficient Of Determination ◽  
Plant Water ◽  
Monitoring Methods

This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R2) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%).

scholarly journals Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 286
Author(s):  
Sang-Jin Park ◽  
Seung-Gyu Jeong ◽  
Yong Park ◽  
Sang-hyuk Kim ◽  
Dong-kun Lee ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Data Fusion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Study Region ◽  
Modis Imagery ◽  
Increasing Temperature

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

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