Using Satellite Data to Estimate Risk of Mercury Exposure in the Amazonian Wayana Language Territory between Suriname, French Guiana, and Brazil

2021 ◽  
Author(s):  
Daniel Peplow
Keyword(s):  
Remote Sensing ◽  
Economic Development ◽  
Indigenous People ◽  
Vegetation Index ◽  
Regional Scale ◽  
Test Area ◽  
Mercury Contamination ◽  
System Level ◽  
Health Issues ◽  
Development Policies

Abstract BackgroundThere is a need for methods that measure the public and environmental health risks of mercury from small-scale gold mines (SSGMs) at a regional scale in tropical forests. Mercury is poisonous, with mercury toxicity in humans most commonly affecting the neurologic, gastrointestinal and renal organ systems at the individual, community and population scale. Economic development policies and projects responsible for SSGM in regions held by indigenous people are developed at the regional scale.MethodsThe synoptic regional-scale perspective of overhead remote imaging technology was used to supplement previous ground-level community risk and health assessment studies. The objective was to evaluate the usefulness of remote sensing as a method for measuring mercury impacts over large areas and test whether regional-level vegetation index values are lower in a test area where mercury contamination from SSGMs are known to impact human health compared to index values in a pristine reference area.ResultsLow vegetation index values were obtained in the test area compared to the high index values at the pristine reference location where vegetation stress is low. Public policy solutions to system-level causes of indigenous health issues caused by natural resource extraction projects are limited in this region where policymakers and economists perform cost-benefit analyses, ostensibly to develop rational economic development policies, that are built on the legal principle terra nullius codified in Western civil law as the Doctrine of Discovery. This principle designates the land and resources held by indigenous people as vacant and empty thus rendering the value of the lives of indigenous people equal to zero. Conclusions Vegetation index values were lower in the test area where there was mercury contamination from SSGMs when compared to a pristine reference area. These results suggest that remote sensing methods can be useful for measuring mercury contamination at scales that support the supranational policy development processes and address health issues caused by factors that lie outside the health sector and are economically formed. At this scale, the ratio comparing the cost of prevention to the benefits is revealed to be an irrational number when the benefit value of the lives saved is set at zero.

scholarly journals Improving the Evapotranspiration Estimation under Cloudy Condition by Extending the Ts-VI Triangle Model

2021 ◽  
Vol 13 (8) ◽  
pp. 1516
Author(s):  
Boyang Li ◽  
Yaokui Cui ◽  
Xiaozhuang Geng ◽  
Huan Li
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Energy Exchange ◽  
Vegetation Index ◽  
Regional Scale ◽  
Reconstruction Algorithm ◽  
Heihe River ◽  
Main Process ◽  
In Situ Data

Evapotranspiration (ET) of soil-vegetation system is the main process of the water and energy exchange between the atmosphere and the land surface. Spatio-temporal continuous ET is vitally important to agriculture and ecological applications. Surface temperature and vegetation index (Ts-VI) triangle ET model based on remote sensing land surface temperature (LST) is widely used to monitor the land surface ET. However, a large number of missing data caused by the presence of clouds always reduces the availability of the main parameter LST, thus making the remote sensing-based ET estimation unavailable. In this paper, a method to improve the availability of ET estimates from Ts-VI model is proposed. Firstly, continuous LST product of the time series is obtained using a reconstruction algorithm, and then, the reconstructed LST is applied to the estimate ET using the Ts-VI model. The validation in the Heihe River Basin from 2009 to 2011 showed that the availability of ET estimates is improved from 25 days per year (d/yr) to 141 d/yr. Compared with the in situ data, a very good performance of the estimated ET is found with RMSE 1.23 mm/day and R2 0.6257 at point scale and RMSE 0.32 mm/day and R2 0.8556 at regional scale. This will improve the understanding of the water and energy exchange between the atmosphere and the land surface, especially under cloudy conditions.

scholarly journals Mapeamento e Quantificação da Cobertura Vegetal no Agreste Central de Pernambuco Utilizando Técnicas de Empilhamento e o NDVI (Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique)

2011 ◽  
Vol 3 (3) ◽  
pp. 157
Author(s):  
Daniel Rodrigues Lira ◽  
Maria do Socorro Bezerra de Araújo ◽  
Everardo Valadares De Sá Barretto Sampaio ◽  
Hewerton Alves da Silva
Keyword(s):  
Remote Sensing ◽  
Vegetation Cover ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Regional Scale ◽  
Digital Processing ◽  
Sparse Vegetation ◽  
New Sensors

O mapeamento e monitoramento da cobertura vegetal receberam consideráveis impulsos nas últimas décadas, com o advento do sensoriamento remoto, processamento digital de imagens e políticas de combate ao desmatamento, além dos avanços nas pesquisas e gerações de novos sensores orbitais e sua distribuição de forma mais acessível aos usuários, tornam as imagens de satélite um dos produtos do sensoriamento remoto mais utilizado para análises da cobertura vegetal das terras. Os índices de cobertura vegetal deste trabalho foram obtidos usando o NDVI - Normalized Difference Vegetation Index para o Agreste central de Pernambuco indicou 39,7% de vegetação densa, 13,6% de vegetação esparsa, 14,3% de vegetação rala e 10,5% de solo exposto. O NDVI apresentou uma caracterização satisfatória para a classificação do estado da vegetação do ano de 2007 para o Agreste Central pernambucano, porém ocorreu uma confusão com os índices de nuvens, sombras e solos exposto, necessitando de uma adaptação na técnica para um melhor aprimoramento da diferenciação desses elementos, constituindo numa recombinação de bandas após a elaboração e calculo do NDVI.Palavras-chave: Geoprocessamento; sensoriamento remoto; índice de vegetação. Mapping and Quantification of Vegetation Cover from Central Agreste Region of Pernambuco State Using NDVI Technique ABSTRACTIn recent decades, advanced techniques for mapping and monitoring vegetation cover have been developed with the advent of remote sensing. New tools for digital processing, the generation of new sensors and their orbital distribution more accessible have facilitated the acquisition and use of satellite images, making them one of the products of remote sensing more used for analysis of the vegetation cover. The aim of this study was to assess the vegetation cover from Central Agreste region of Pernambuco State, using satellite images TM / LANDSAT-5. The images were processed using the NDVI (Normalized Difference Vegetation Index) technique, generating indexes used for classification of vegetation in dense, sparse and scattered. There was a proportion of 39.7% of dense vegetation, 13.6% of sparse vegetation, 14.3% of scattered vegetation and 10.5% of exposed soil. NDVI technique has been used as a useful tool in the classification of vegetation on a regional scale, however, needs improvement to a more precise differentiation among levels of clouds, shadow, exposed soils and vegetation. Keywords: Geoprocessing, remote sensing, vegetation index

scholarly journals CLASSIFYING AND MAPPING OF VEGETATED AREA IN AL- BAHA REGION, SAUDI ARABIA USING REMOTE SENSING. I. EXTENT AND DISTRIBUTION OF GROUND VEGETATED COVER CATEGORIES.

2020 ◽  
pp. 75-80
Author(s):  
Abdullah Saleh Al-Ghamdi
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Type ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Vegetation Mapping ◽  
Primary Objective ◽  
High Density ◽  
Vegetation Density

Classifying and mapping vegetation is an important technical task for managing natural resources; the primary objective of the vegetation-mapping inventory is to produce high quality, standardized maps and associated data sets of vegetation. Satellite remote sensing has proven to be effective technology for mapping forest vegetation at the landscape to regional scale. In the remote sensing technique, vegetation density can be directly indicated by vegetation indices. Although there are several vegetation indices, the most widely used is the Normalized Difference Vegetation Index (NDVI), formulated by transforming raw satellite data into NDVI values, ranging from -1 to 1. NDVI enables the creation of images and other products that provide a rough measure of vegetation type, amount, and condition on land surfaces. The results show that medium to high density vegetation is mostly found in the central part of Al-Baha region separating the highlands and lowlands. The relationship study between NDVI and vegetation cover percentage in this study depicts an NDVI value of only 0.20–1.00, which indicates that vegetation covers over 60% of Al-Baha. This is probably because vegetation here may not only comprise trees but also other plant forms such as herbs and shrubs. However, only 862.5 km2 (7.7%) of Al-Baha is covered with medium-high density vegetation, found mainly at the 6 –15km width horizontal central belt (in the Al-Mandaq, Al-Baha, and south Baljurashi districts) along a high, foggy mountainous plateau. Conversely, about 65% of Al-Baha region has very low to no vegetation density; vegetation is found extensively in the Tihama low plain towards the Red Sea and in the north-eastern desert plain. This study has provided a comprehensive report on vegetation mapping in the Al-Baha region.

scholarly journals Characterising Rice (Oryza indica) Ecology in East and Southern Eastern Plateau of Odisha using Multi Source Remote Sensing

2021 ◽  
pp. 37-47
Author(s):  
M. Satya Swarupa Rani ◽  
Anima Biswal ◽  
B. S. Rath
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Ecological Condition ◽  
Water Index ◽  
Normalised Difference Vegetation Index ◽  
Spectral Vegetation Indices ◽  
Block Level

Rice is the most important crop of Odisha occupying 41.24% of net sown area in Kharif season and contributing 65.85 % of total food grain production of Odisha state and this is being cultivated in various types environmental and ecological condition. Assessment of rice phenology is prime for management and yield prediction. In view of characterizing rice ecology in East and South Eastern Plateau from 2008 – 2018 to know the time series analysis , remote sensing tools were used . MODIS can0 acquire data over a wide area with high spatial and temporal resolutions easily providing regional scale information .In order to study the seasonal /annual as well as spatial variability of kharif rice vigour and wetness spectral vegetation indices like NDVI(Normalised Difference Vegetation Index),LSWI(Land surface water index) derived from 15 day composite 250 m data were analysed at block level for Odisha state. For studying the start of season variability, SASI index was used. The season maximum NDVI, LSWI were computed for the year 2008-2018 for kharif rice in East and Southern eastern coastal plain zone of Odisha and graphs were generated which shows the variability of the kharif rice vigour and wetness.

scholarly journals Remote Sensing Vegetation Indices in Viticulture: A Critical Review

Agriculture ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 457
Author(s):  
Rigas Giovos ◽  
Dimitrios Tassopoulos ◽  
Dionissios Kalivas ◽  
Nestor Lougkos ◽  
Anastasia Priovolou
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Proximal Sensing ◽  
Spectral Bands ◽  
Sensing Platforms ◽  
Precision Viticulture ◽  
Remote Sensing Methods

One factor of precision agriculture is remote sensing, through which we can monitor vegetation health and condition. Much research has been conducted in the field of remote sensing and agriculture analyzing the applications, while the reviews gather the research on this field and examine different scientific methodologies. This work aims to gather the existing vegetation indices used in viticulture, which were calculated from imagery acquired by remote sensing platforms such as satellites, airplanes and UAVs. In this review we present the vegetation indices, the applications of these and the spatial distribution of the research on viticulture from the early 2000s. A total of 143 publications on viticulture were reviewed; 113 of them had used remote sensing methods to calculate vegetation indices, while the rejected ones have used proximal sensing methods. The findings show that the most used vegetation index is NDVI, while the most frequently appearing applications are monitoring and estimating vines water stress and delineation of management zones. More than half of the publications use multitemporal analysis and UAVs as the most used among remote sensing platforms. Spain and Italy are the countries with the most publications on viticulture with one-third of the publications referring to regional scale whereas the others to site-specific/vineyard scale. This paper reviews more than 90 vegetation indices that are used in viticulture in various applications and research topics, and categorized them depending on their application and the spectral bands that they are using. To summarize, this review is a guide for the applications of remote sensing and vegetation indices in precision viticulture and vineyard assessment.

scholarly journals The GA-BPNN-Based Evaluation of Cultivated Land Quality in the PSR Framework Using Gaofen-1 Satellite Data

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5127 ◽  
Author(s):  
Liu ◽  
Peng ◽  
Xia ◽  
Hu ◽  
Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Linear Model ◽  
Vegetation Index ◽  
Regional Scale ◽  
Cultivated Land ◽  
Land Quality ◽  
Remote Sensing Technology ◽  
Evaluation Approach ◽  
Sensing Technology ◽  
State Index

Rapid and efficient assessment of cultivated land quality (CLQ) using remote sensing technology is of great significance for protecting cultivated land. However, it is difficult to obtain accurate CLQ estimates using the current satellite-driven approaches in the pressure-state-response (PSR) framework, owing to the limitations of linear models and CLQ spectral indices. In order to improve the estimation accuracy of CLQ, this study used four evaluation models (the traditional linear model; partial least squares regression, PLSR; back propagation neural network, BPNN; and BPNN with genetic algorithm optimization, GA-BPNN) to evaluate CLQ for determining the accurate evaluation model. In addition, the optimal satellite-derived indicator in the land state index was selected among five vegetation indices (the normalized vegetation index, NDVI; enhanced vegetation index, EVI; modified soil-adjusted vegetation index, MSAVI; perpendicular vegetation index, PVI; and soil-adjusted vegetation index, SAVI) to improve the prediction accuracy of CLQ. This study was conducted in Conghua District of Guangzhou, Guangdong Province, China, based on Gaofen-1 (GF-1) data. The prediction accuracies from the traditional linear model, PLSR, BPNN, and GA-BPNN were compared using observations. The results demonstrated that (1) compared with other models (the traditional linear model: R2 = 0.14 and RMSE = 91.53; PLSR: R2 = 0.33 and RMSE = 74.58; BPNN: R2 = 0.50 and RMSE = 61.75), the GA-BPNN model based on EVI in the land state index provided the most accurate estimates of CLQ, with the R2 of 0.59 and root mean square error (RMSE) of 56.87, indicating a nonlinear relationship between CLQ and the prediction indicator; and (2) the GA-BPNN-based evaluation approach of CLQ in the PSR framework was driven to map CLQ of the study area using the GF-1 data, leading to an RMSE of 61.44 at the regional scale, implying that the GA-BPNN-based evaluation approach has the potential to map CLQ over large areas. This study provides an important reference for the high-accuracy prediction of CLQ based on remote sensing technology.

scholarly journals Regional-scale drought monitor using synthesized index based on remote sensing in northeast China

2020 ◽  
Vol 12 (1) ◽  
pp. 163-173 ◽  
Author(s):  
Xiaofang Sun ◽  
Meng Wang ◽  
Guicai Li ◽  
Yuanyuan Wang
Keyword(s):  
Remote Sensing ◽  
Northeast China ◽  
Vegetation Index ◽  
Potential Evapotranspiration ◽  
Regional Scale ◽  
Growth Period ◽  
Stress Index ◽  
Drought Indices ◽  
Drought Severity ◽  
Yield Data

AbstractDrought has a significant impact on agricultural, ecological, and socioeconomic spheres. Although many drought indices have been proposed until now, the detection of droughts at regional scales still needs to be further studied. The Standardized Vegetation Index (SVI) that represents vegetation growing condition, the Standardized Water Index (SWI) that represents canopy water content, and the Evaporative Stress Index (ESI) that quantifies anomalies in the ratio of actual to potential evapotranspiration were calculated based on the Moderate-resolution Imaging Spectroradiometer (MODIS) data. A new remote sensing-based Vegetation Drought Monitor Synthesized Index (VDSI) was proposed by integrating the SVI, SWI, and ESI in the northeast China. When tested against the in situ Standardized Precipitation Evapotranspiration Index (SPEI), VDSI with proper weights of three variables outperformed individual remote sensing drought indices. The county-level yields of the main crops in the study area from 2001 to 2010 were also used to validate the VDSI. The correlation analysis between the yield data and the VDSI data during the crop growing season was performed, and its results showed that VDSI during the crop reproductive growth period was strongly correlated with the variation of crop yield. It was proved that this index is a potential indicator for assessment of the spatial pattern of drought severity in northeast China.

scholarly journals Spatio-temporal dynamics of vegetation cover in North-West Algeria using remote sensing data

2019 ◽  
Vol 51 (3) ◽  
pp. 117-127
Author(s):  
Si Tayeb Tayeb ◽  
Benabdeli Kheloufi
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Human Activities ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Anthropogenic Impacts ◽  
Regional Scale ◽  
Anthropogenic Factors ◽  
Spatio Temporal

Abstract Land cover change is the result of complex interactions between social and environmental systems which change over time. While climatic and biophysics phenomena were for a long time the principal factor of land transformations, human activities are today the origin of the major part of land transformation which affects natural ecosystems. Quantification of natural and anthropogenic impacts on vegetation cover is often hampered by logistical issues, including (1) the difficulty of systematically monitoring the effects over large areas and (2) the lack of comparison sites needed to evaluate the effect of the factors. The effective procedure for measuring the degree of environmental change due to natural factors and human activities is the multitemporal study of vegetation cover. For this purpose, the aim of this work is the analysis of the evolution of land cover using remote sensing techniques, in order to better understand the respective role of natural and anthropogenic factors controlling this evolution. A spatio-temporal land cover dynamics study on a regional scale in Oranie, using Landsat data for two periods (1984–2000) and (2000–2011) was conducted. The images of the vegetation index were classified into three classes based on Normalized Difference Vegetation Index (NDVI) values and analysed using image difference approach. The result shows that the vegetation cover was changed. An intensive regression of the woody vegetation and forest land resulted in -22.5% of the area being lost between 1984 and 2000, 1,271 km2 was converted into scrub formations and 306 km2 into bare soil. On the other hand, this class increased by around 45% between 2000 and 2011, these evolutions resulting from the development of scrub groups with an area of 1,875.7 km2.

A model of the analysis of the dynamics and structure of socio-economic development (an example of the set of the largest Polish cities in the years 1998–2015)

2019 ◽  
Vol 63 (1) ◽  
pp. 25-37
Author(s):  
Lidia Mierzejewska ◽  
Jerzy Parysek
Keyword(s):  
Principal Component Analysis ◽  
Economic Development ◽  
Development Process ◽  
Principal Component ◽  
The State ◽  
Geographical Research ◽  
Development Policies ◽  
Socio Economic Development ◽  
Development Processes ◽  
State Of Affairs

Abstract The complexity of the reality studied by geographical research requires applying such methods which allow describing the state of affairs and ongoing changes in the best possible way. This study aims to present a model of research on selected aspects of the dynamics and structure of socio-economic development. The idea was to determine whether we deal with the process of reducing or widening the differences in terms of individual features. The article primarily pursues a methodological goal, and to a lesser extent an empirical one. The methodological objective of the paper was to propose and verify a multi-aspect approach to the study of development processes. The analyses carried out reveal that in terms of the features taken into account in the set of 24 of the largest Polish cities the dominating processes are those increasing differences between cities, which are unfavourable in the context of the adopted development policies aiming at reducing the existing disparities. In relation to the methodological objective, the results of the conducted research confirm the rationale of the application of the measures of dynamics and the feature variance to determine the character (dynamics and structure) of the socio-economic development process of cities. Comparatively less effective, especially for interpretation, is the application of principal component analysis and a multivariate classification, which is mainly the result of differences in the variance of particular features.

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