scholarly journals Soil use and occupation in Caracol Settlement: a multitemporal assessment using remote sensing and geoprocessing techniques

2020 ◽  
Vol 9 (12) ◽  
pp. e30891211029
Author(s):  
Odemir Coelho da Costa ◽  
José Francisco dos Reis Neto ◽  
Ana Paula Garcia Oliveira
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Cover ◽  
The United States ◽  
United States Geological Survey ◽  
Vegetation Landscape ◽  
Digital Elevation ◽  
Elevation Model ◽  
Gis Software

This study focused on the application of remote sensing and geoprocessing techniques to quantify the agroecological use of Caracol settlement area in order to quantify the vegetated areas, as well as the use and occupation of the soil in the years 2000, 2010 and 2020, in the months of May of each year. To achieve the objectives, computational tools (Quantum GIS software) were used, as well as data from Landsat 5 and 8 satellites, bands 3 and 4, 4 and 5 respectively. Vector data from the database of the Brazilian Institute of Geography and Statistics (IBGE), a Digital Elevation Model (DEM), from the United States Geological Survey (USGS/NASA) for evaluation of the watersheds were also used. For vegetation analysis, as well as temporal evolution, the Normalized Difference Vegetation Index (NDVI) was used, with this it was possible to evaluate by means of thematic maps and tables containing the quantification and classification of vegetation and soil cover. It was evident in the present study that there were significant changes in the vegetation landscape over two decades, through anthropic activity by settled families, that were responsible for such changes in the use and soil cover of Caracol settlement.

scholarly journals Spatial Downscaling of GPM Annual and Monthly Precipitation Using Regression-Based Algorithms in a Mountainous Area

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chesheng Zhan ◽  
Jian Han ◽  
Shi Hu ◽  
Liangmeizi Liu ◽  
Yuxuan Dong
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mountainous Area ◽  
Monthly Precipitation ◽  
Practical Applications ◽  
Precipitation Measurement ◽  
Digital Elevation ◽  
Elevation Model ◽  
Global Precipitation ◽  
Spatial Downscaling

As a fundamental component in material and energy circulation, precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. In this study, we investigated two downscaling algorithms based on the Multiple Linear Regression (MLR) and the Geographically Weighted Regression (GWR), respectively. They were employed to downscale annual and monthly precipitation obtained from the Global Precipitation Measurement (GPM) Mission in Hengduan Mountains, Southwestern China, from 10 km × 10 km to 1 km × 1 km. Ground observations were then used to validate the accuracy of downscaled precipitation. The results showed that (1) GWR performed much better than MLR to regress precipitation on Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM); (2) coefficients of GWR models showed strong spatial nonstationarity, but the spatial mean standardized coefficients were very similar to standardized coefficients of MLR in terms of intra-annual patterns: generally NDVI was positively related to precipitation when monthly precipitation was under 166 mm; DEM was negatively related to precipitation, especially in wet months like July and August; contribution of DEM to precipitation was greater than that of NDVI; (3) residuals’ correction was indispensable for the MLR-based algorithm but should be removed from the GWR-based algorithm; (4) the GWR-based algorithm rather than the MLR-based algorithm produced more accurate precipitation than original GPM precipitation. These results indicated that GWR is a promising method in satellite precipitation downscaling researches and needed to be further studied.

scholarly journals Identifying and Predicting the Geographical Distribution Patterns of Oncomelania hupensis

2019 ◽  
Vol 16 (12) ◽  
pp. 2206 ◽  
Author(s):  
Niu ◽  
Li ◽  
Qiu ◽  
Xu ◽  
Huang ◽  
...  
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Distribution Patterns ◽  
Oncomelania Hupensis ◽  
Spatial And Temporal Patterns ◽  
Jianghan Plain ◽  
Coverage Area ◽  
Digital Elevation ◽  
Elevation Model

Schistosomiasis is a snail-borne parasitic disease endemic to the tropics and subtropics, whose distribution depends on snail prevalence as determined by climatic and environmental factors. Here, dynamic spatial and temporal patterns of Oncomelania hupensis distributions were quantified using general statistics, global Moran’s I, and standard deviation ellipses, with Maxent modeling used to predict the distribution of habitat areas suitable for this snail in Gong’an County, a severely affected region of Jianghan Plain, China, based on annual average temperature, humidity of the climate, soil type, normalized difference vegetation index, land use, ditch density, land surface temperature, and digital elevation model variables; each variable’s contribution was tested using the jackknife method. Several key results emerged. First, coverage area of O. hupensis had changed little from 2007 to 2012, with some cities, counties, and districts alternately increasing and decreasing, with ditch and bottomland being the main habitat types. Second, although it showed a weak spatial autocorrelation, changing negligibly, there was a significant east–west gradient in the O. hupensis habitat area. Third, 21.9% of Gong’an County’s area was at high risk of snail presence; and ditch density, temperature, elevation, and wetting index contributed most to their occurrence. Our findings and methods provide valuable and timely insight for the control, monitoring, and management of schistosomiasis in China.

UAV Remote Sensing Assessment of Crop Growth

2021 ◽  
Vol 87 (12) ◽  
pp. 891-899
Author(s):  
Freda Elikem Dorbu ◽  
Leila Hashemi-Beni ◽  
Ali Karimoddini ◽  
Abolghasem Shahbazi
Keyword(s):  
Remote Sensing ◽  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Crop Growth ◽  
Infrared Sensors ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Growth Indicators

The introduction of unmanned-aerial-vehicle remote sensing for collecting high-spatial- and temporal-resolution imagery to derive crop-growth indicators and analyze and present timely results could potentially improve the management of agricultural businesses and enable farmers to apply appropriate solution, leading to a better food-security framework. This study aimed to analyze crop-growth indicators such as the normalized difference vegetation index (NDVI), crop height, and vegetated surface roughness to determine the growth of corn crops from planting to harvest. Digital elevation models and orthophotos generated from the data captured using multispectral, red/green/blue, and near-infrared sensors mounted on an unmanned aerial vehicle were processed and analyzed to calculate the various crop-growth indicators. The results suggest that remote sensing-based growth indicators can effectively determine crop growth over time, and that there are similarities and correlations between the indicators.

A APLICAÇÃO DO ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA (NDVI) PARA ANÁLISE DA DEGRADAÇÃO AMBIENTAL DA ÁREA DE INFLUÊNCIA DIRETA DO AÇUDE CASTANHÃO

2019 ◽  
Vol 21 (2) ◽  
pp. 674-685
Author(s):  
Amanda Menezes De Albuquerque ◽  
José Robério Cabral Ribeiro ◽  
Marta Celina Linhares Sales
Keyword(s):  
Remote Sensing ◽  
Environmental Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Cover ◽  
Spatial Location ◽  
Biophysical Parameters ◽  
Spectral Image ◽  
Vegetation Indexes ◽  
The Relationship

O aumento da degradação ambiental de terras secas vem conduzindo à erosão dos solos e desertificação, o uso intenso e predatório dos recursos naturais nessas áreas acaba impossibilitando a sobrevivência das comunidades que vivem nessas regiões. O estado do Ceará tem cerca de 92% de seu território inserido no semiárido, a pesquisa foi desenvolvida na Área de Influência Direta do Açude Castanhão – AIC. A através do registro de imagens, tornou-se possível às análises de relacionamento entre localização espacial de alvos do meio ambiente, variação espectral da imagem e variação da cobertura vegetal dos solos. A utilização do sensoriamento remoto e de índices de vegetação como o Índice de Vegetação da Diferença Normalizada (NDVI), facilita a obtenção e modelagem de parâmetros biofísicos das plantas, como a área foliar, biomassa e porcentagem de cobertura do solo, fornecendo importantes informações sobre a Degradação Ambiental da área.Palavras-chave: Degradação; Sensoriamento Remoto; Cobertura Vegetal. ABSTRACTThe increased environmental degradation of dry lands has led to soil erosion and desertification, the intense and predatory use of natural resources in these areas makes it impossible to survive the communities living in these regions. The state of Ceará has about 92% of its territory inserted in the semi-arid, the research was developed in the Area of Direct Influence of Castanhão - AIC. A through image registration, it became possible to analyze the relationship between spatial location of environmental targets, spectral image variation and variation of soil cover. The use of remote sensing and vegetation indexes such as the Normalized Difference Vegetation Index (NDVI) facilitates the obtaining and modeling of plant biophysical parameters such as leaf area, biomass and percentage of soil cover, providing important information on the Environmental Degradation of the area.Keywords:Degradation; Remote Sensing; Vegetal Cover.

scholarly journals Predicting Urban Waterlogging Risks by Regression Models and Internet Open-Data Sources

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 879
Author(s):  
Ducthien Tran ◽  
Dawei Xu ◽  
Vanha Dang ◽  
Abdulfattah.A.Q. Alwah
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Open Data ◽  
Data Sources ◽  
Road Density ◽  
Rapid Urbanization ◽  
Spatial Factors ◽  
Digital Elevation ◽  
Important Means ◽  
Elevation Model

In the context of climate change and rapid urbanization, urban waterlogging risks due to rainstorms are becoming more frequent and serious in developing countries. One of the most important means of solving this problem lies in elucidating the roles played by the spatial factors of urban surfaces that cause urban waterlogging, as well as in predicting urban waterlogging risks. We applied a regression model in ArcGIS with internet open-data sources to predict the probabilities of urban waterlogging risks in Hanoi, Vietnam, during the period 2012–2018 by considering six spatial factors of urban surfaces: population density (POP-Dens), road density (Road-Dens), distances from water bodies (DW-Dist), impervious surface percentage (ISP), normalized difference vegetation index (NDVI), and digital elevation model (DEM). The results show that the frequency of urban waterlogging occurrences is positively related to the first four factors but negatively related to NDVI, and DEM is not an important explanatory factor in the study area. The model achieved a good modeling effect and was able to explain the urban waterlogging risk with a confidence level of 67.6%. These results represent an important analytic step for urban development strategic planners in optimizing the spatial factors of urban surfaces to prevent and control urban waterlogging.

scholarly journals Normalized difference vegetation index analysis of the first part impacted with the breaking of the Fundão Dam in Mariana-MG

2020 ◽  
Vol 24 ◽  
pp. e27
Author(s):  
Renan Valério Eduvirgem ◽  
Claudemir Rodrigues Soares ◽  
Elissandro Voigt Beier
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The United States ◽  
Local Economy ◽  
Vegetation Coverage ◽  
United States Geological Survey ◽  
Landsat 8 ◽  
Data Set ◽  
Mutual Cooperation ◽  
The Impact

This paper addresses the exploitation of mineral resources and suggests that an environmental management that meets a set of measures and mutual cooperation between public and private managers, civil society, and mining companies that exploit natural, renewable, and non-renewable resources is needed. Cooperation between managers and joint safety measures can prevent present and future accidents like the one that occurred in Mariana City in Minas Gerais State (MG). The questioning presented puts into discussion the disaster that occurred in Mariana City due to the rupture of the ore tailings dam (Fundão dam) in November 2015. With an estimated population of 60,000 inhabitants, Mariana City has a local economy directly linked to mining activities. Due to the impact caused by the rupture of the Fundão dam, both city and vegetation were destroyed, among other factors observed along the path followed by the tailings. However, what is discussed in this article with greater emphasis is the loss of vegetation in the watershed. The methodology compared the degree of vegetation coverage in the basin area through the analysis of the Normalized Difference Vegetation Index – NDVI for 2013, 2016, 2017, 2018, and 2019 in different months. Some images refer to August and other samples are from September, complementing the process through the use of Landsat 8 satellite images - OLI sensor, acquired from the United States Geological Survey (USGS) repository. 299 points were distributed in the quadrant to perform the analyses (n = 299). The level of significance was set at 5% with a 95% confidence, to ascertain and verify whether the data distribution is in an acceptable condition (dense or semi-dense vegetation cover). Regarding vegetation analysis, the Kolmogorov-Smirnov and Shapiro-Wilk tests were used. Both tests indicated a non-normal distribution for the NDVI data set, which indicates the absence of a vegetation index that was covered by the tailings, resulting in an area with large spaces without the coverage previously registered in 2013. We conclude that the vegetation suffered a drastic alteration provoked by the rupture of the Fundão dam which also led to homeless residents, negative impacts on the livelihood of the small farmers and fishermen, silting up of rivers and streams, death of several animal and plant species, and also affected the ecosystem and the local and regional biodiversity. 

scholarly journals Remote sensing vegetation index methods to evaluate changes in greenness and evapotranspiration in riparian vegetation in response to the Minute 319 environmental pulse flow to Mexico

2018 ◽  
Vol 380 ◽  
pp. 45-54 ◽  
Author(s):  
Pamela L. Nagler ◽  
Christopher J. Jarchow ◽  
Edward P. Glenn
Keyword(s):  
Remote Sensing ◽  
Gulf Of California ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Colorado River ◽  
Environmental Flows ◽  
The United States ◽  
Landsat 8 ◽  
Pulse Flow ◽  
The Us

Abstract. During the spring of 2014, 130 million m3 of water were released from the United States' Morelos Dam on the lower Colorado River to Mexico, allowing water to reach the Gulf of California for the first time in 13 years. Our study assessed the effects of water transfer or ecological environmental flows from one nation to another, using remote sensing. Spatial applications for water resource evaluation are important for binational, integrated water resources management and planning for the Colorado River, which includes seven basin states in the US plus two states in Mexico. Our study examined the effects of the historic binational experiment (the Minute 319 agreement) on vegetative response along the riparian corridor. We used 250 m Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and 30 m Landsat 8 satellite imagery to track evapotranspiration (ET) and the normalized difference vegetation index (NDVI). Our analysis showed an overall increase in NDVI and evapotranspiration (ET) in the year following the 2014 pulse, which reversed a decline in those metrics since the last major flood in 2000. NDVI and ET levels decreased in 2015, but were still significantly higher (P < 0.001) than pre-pulse (2013) levels. Preliminary findings show that the decline in 2015 persisted into 2016 and 2017. We continue to analyse results for 2018 in comparison to short-term (2013–2018) and long-term (2000–2018) trends. Our results support the conclusion that these environmental flows from the US to Mexico via the Minute 319 “pulse” had a positive, but short-lived (1 year), impact on vegetation growth in the delta.

scholarly journals Quantifying Efficiency of Sliding-Window Based Aggregation Technique by Using Predictive Modeling on Landform Attributes Derived from DEM and NDVI

2019 ◽  
Vol 8 (4) ◽  
pp. 196 ◽  
Author(s):  
Rahul Gomes ◽  
Anne Denton ◽  
David Franzen
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Predictive Ability ◽  
Window Size ◽  
Slope Aspect ◽  
Topographic Features ◽  
Digital Elevation ◽  
Random Forest Models ◽  
Aggregation Technique ◽  
Elevation Model

Topographic features impact biomass and other agriculturally relevant observables. However, conventional tools for processing digital elevation model (DEM) data in geographic information systems have severe limitations. Typically, 3-by-3 window sizes are used for evaluating the slope, aspect and curvature. As a consequence, high resolution DEMs have to be resampled to match the size of typical topographic features, resulting in low accuracy and limiting the predictive ability of any model using such features. In this paper, we examined the usefulness of DEM-derived topographic features within Random Forest models that predict biomass. Our model utilized the derived topographic features and achieved 95.31% accuracy in predicting Normalized Difference Vegetation Index (NDVI) compared to a 51.89% accuracy obtained for window size 3-by-3 in the traditional resampling model. The efficacy of partial dependency plots (PDP) in terms of interpretability was also assessed.

scholarly journals Relationship between landform classification and vegetation (case study: southwest of Fars province, Iran)

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Marzieh Mokarram ◽  
Dinesh Sathyamoorthy
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tree Height ◽  
Shuttle Radar Topography Mission ◽  
Fars Province ◽  
Visual Interpretation ◽  
Landform Classification ◽  
Digital Elevation ◽  
Position Index ◽  
Elevation Model

AbstractThis study is aimed at investigating the relationship between landform classification and vegetation in the southwest of Fars province, Iran. First, topographic position index (TPI) is used to perform landform classification using a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) with resolution of 30 m. The classification has ten classes; high ridges, midslope ridges, upland drainage, upper slopes, open slopes, plains, valleys, local ridges, midslope drainage and streams. Visual interpretation indicates that for the local, midslope and high ridge landforms, normalized difference vegetation index (NDVI) values and tree heights are higher as compared to the other landforms. In addition, it is found that there are positive and significant correlations betweenNDVI and tree height (

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