Climatic and Anthropic Influence on the Geodiversity of the Maranhão Amazon Floodplain

The Maranhense Amazon floodplain shelters a Ramsar site established by the United Nations for the protection of wetland biodiversity. Despite its protected ecological status, the impacts from deforestation, burning, the agricultural and livestock industries, are on the rise. Knowledge of the spatial distribution and temporal dynamics of these impacts are important to improve the understanding of how this region is affected. Data on increasing deforestation and hot pixels were used to evaluate the anthropogenic pressure under the geodiversity of the region, relating them to the environmental variables (rainfall, Normalized Difference Vegetation Index and Deforestation annual deforestation rate) measured through the rainfall data and the Normalized Difference Vegetation Index (NDVI). In this study, the potential of remote sensing and geographic information system. The time series were used from 2001 to 2016 for all variables. We observed a strong negative and significant correlation between hot pixels and NDVI, while hot pixels increase, the vegetation indexes tend to decrease. In 2006 an abrupt fall in the NDVI occurred due to the marked increase in the deforested area. In 2010, the NDVI reached its highest levels, because the vegetation responded to the highest rainfall observed in the period in 2009. Unit 4 presented the highest pixels number in the period evaluated (2,978 pixels; 55% of the total). There is a significant correlation between NDVI and rainfall.

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Estudo da dinâmica espaço-temporal do NDVI no Município de Sorriso-MT

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v13.2.p834-841 ◽

2020 ◽

Vol 13 (2) ◽

pp. 834

Author(s):

Juliana Soares ◽

Victor Hugo De Morais Danelichen ◽

Osvaldo Alves Pereira ◽

André Luiz Martins

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Agricultural Expansion ◽

Native Forest ◽

Mato Grosso ◽

Python Language ◽

Vegetation Indexes ◽

Region Temperature ◽

Landsat 5 Tm

A cidade de Sorriso-MT é considerada a capital do agronegócio no Brasil e maior produtora individual de soja do mundo. A expansão agrícola no Município de Sorriso no Estado de Mato Grosso se deu nos anos 70 com o favorecimento do governo federal estimulando a chegada de famílias provenientes do Sul do País. Diante disso, o objetivo desse trabalho foi avaliar a dinâmica espaço-temporal do NDVI no Município por meio de sensoriamento remoto. Utilizou-se imagens do satélite Landsat-5 (TM), entre os anos de 1984 e 2011. O processamento dessas imagens foi realizado por meio do programa ArcGIS 10.3 e linguagem Python. A estimativa da expansão das áreas de cultivo e quantidade de áreas de mata nativa do Município foi calculado através do índice de vegetação da diferença normalizada - NDVI. Nossos resultados sugerem que as possíveis mudanças ocorridas no solo do Município podem estar afetando as variáveis climatológicas como a temperatura da região. O estudou demonstrou também que a expansão agrícola se intensificou após a emancipação do Município, com grande crescimento das áreas de cultivo e urbanização, tendo como consequência a diminuição das áreas de mata nativa.Palavra-chave: Processamento de imagens, Satélites, Índices de vegetação. Study of NDVI spatiotemporal dynamics in the city of Sorriso-MT A B S T R A C TThe Sorriso-MT city is considered the agribusiness capital in Brazil and the largest single soy producer in the world. The agricultural expansion of Sorriso in the state of Mato Grosso occurred in the 70's with the favor of the federal government encouraging the arrival of families from the south of the country. Therefore, the objective of this work was to evaluate the spatial-temporal dynamics of NDVI in the Municipality through remote sensing. Landsat-5 (TM) satellite images were used between 1984 and 2011. These images were processed using the ArcGIS 10.3 program and Python language. The estimate the expansion of the cultivated areas and the amount of native forest areas of the Municipality was calculated through the normalized difference vegetation index - NDVI. Our results suggest that possible changes in the soil of the municipality may be affecting climatic variables such as the region temperature. The study also showed that the agricultural expansion intensified after the emancipation of the Municipality, with great growth of cultivation and urbanization areas, resulting in the reduction of native forest areas.Keywords: Image processing, Satellites, Vegetation indexes

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Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

Remote Sensing ◽

10.3390/rs13050956 ◽

2021 ◽

Vol 13 (5) ◽

pp. 956

Author(s):

Florian Mouret ◽

Mohanad Albughdadi ◽

Sylvie Duthoit ◽

Denis Kouamé ◽

Guillaume Rieu ◽

...

Keyword(s):

Outlier Detection ◽

Experimental Validation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Multispectral Images ◽

Vegetation Indexes ◽

Crop Development ◽

Different Types ◽

Selection Of ◽

Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

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AEROSPACE MONITORING OF THE FOREST ECOSYSTEM DYNAMICS WITHIN THE TERRITORY OF VRACHANSKI BALKAN NATURE PARK

10.32006/eeep.2018.2.4146 ◽

2018 ◽

pp. 41-46

Author(s):

Adlin Dancheva

Keyword(s):

Remote Sensing ◽

Forest Ecosystem ◽

Vegetation Index ◽

Forest Ecosystems ◽

Normalized Difference Vegetation Index ◽

Remote Sensing And Gis ◽

Ecosystem Dynamics ◽

Anthropogenic Pressure ◽

Water Index ◽

Nature Park

In this paper the application of Remote Sensing and GIS as a means of performing aero – space monitoring of forest ecosystems dynamics is being considered. The purpose of this work is to create a model for monitoring the dynamic of forest ecosystems, based on Remote Sensing and GIS. The results of eco-monitoring can be used to update plans and policies for forest ecosystem management. The territory of Vrachanski Balkan Nature park was chosen as the subject of research as there is a certain anthropogenic pressure there. The results presented are obtained by spatial-time analysis of certain aerospace data indices. To carry out the study optical satellite images were used, on the basics of which three indices were calculated: Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Greenness Index (NDGI). A comparative analysis has been created and results of the degree of correlation between the different indices are presented, as well as indicators from the different test areas and related changes in the individual points in time. The results of the survey aim to assess the dynamics and condition of the forest vegetation on the territory of Vrachanski Balkan Nature park and can be utilised in activities related to monitoring, mapping and forest management.

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Surveillance along the Rio Grande during the 2020 Vesicular Stomatitis Outbreak Reveals Spatio-Temporal Dynamics of and Viral RNA Detection in Black Flies

Pathogens ◽

10.3390/pathogens10101264 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1264

Author(s):

Katherine I. Young ◽

Federico Valdez ◽

Christina Vaquera ◽

Carlos Campos ◽

Lawrence Zhou ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Rio Grande ◽

Maximum Temperature ◽

Black Flies ◽

Southern Mexico ◽

Black Fly ◽

The Usa ◽

Vesicular Stomatitis

Vesicular stomatitis virus (VSV) emerges periodically from its focus of endemic transmission in southern Mexico to cause epizootics in livestock in the US. The ecology of VSV involves a diverse, but largely undefined, repertoire of potential reservoir hosts and invertebrate vectors. As part of a larger program to decipher VSV transmission, we conducted a study of the spatiotemporal dynamics of Simulium black flies, a known vector of VSV, along the Rio Grande in southern New Mexico, USA from March to December 2020. Serendipitously, the index case of VSV-Indiana (VSIV) in the USA in 2020 occurred at a central point of our study. Black flies appeared soon after the release of the Rio Grande’s water from an upstream dam in March 2020. Two-month and one-year lagged precipitation, maximum temperature, and vegetation greenness, measured as Normalized Difference Vegetation Index (NDVI), were associated with increased black fly abundance. We detected VSIV RNA in 11 pools comprising five black fly species using rRT-PCR; five pools yielded a VSIV sequence. To our knowledge, this is the first detection of VSV in the western US from vectors that were not collected on premises with infected domestic animals.

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Hydrological Condition Constrains Vegetation Dynamics for Wintering Waterfowl in China’s East Dongting Lake Wetland

Sustainability ◽

10.3390/su11184936 ◽

2019 ◽

Vol 11 (18) ◽

pp. 4936 ◽

Cited By ~ 2

Author(s):

Min Wang ◽

Qing Gu ◽

Guihua Liu ◽

Jingwei Shen ◽

Xuguang Tang

Keyword(s):

Vegetation Dynamics ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Global Climate ◽

Dongting Lake ◽

Plant Resources ◽

Data Set ◽

Wintering Waterfowl ◽

The Government

As an internationally important wintering region for waterfowls on the East Asian–Australasian Flyway, the national reserve of China’s East Dongting Lake wetland is abundant in animal and plant resources during winter. The hydrological regimes, as well as vegetation dynamics, in the wetland have experienced substantial changes due to global climate change and anthropogenic disturbances, such as the construction of hydroelectric dams. However, few studies have investigated how the wetland vegetation has changed over time, particularly during the wintering season, and how this has directly affected habitat suitability for migratory waterfowl. Thus, it is necessary to monitor the spatio-temporal dynamics of vegetation in the protected wetland and explore the potential factors that alter it. In this study, the data set of time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) from 2000 to 2018 was used to analyze the seasonal dynamics and interannual trends of vegetation over the wintering period from October to January. The results showed that the average NDVI exhibited an overall increasing trend, with the trend rising slowly in recent years. The largest monthly mean NDVI generally occurred in November, which is pertinent to the quantity of wintering waterfowl in the East Dongting Lake wetland. Meanwhile, the mean NDVI in the wintering season is significantly correlated to temperature and water area, with apparent lagging effects. Long-term stability analysis presented a gradually decreasing pattern from the central body of water to the surrounding area. All analyses will help the government to make appropriate management strategies to protect the habitat of wintering waterfowl in the wetland.

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Assessing the Use of Sentinel-2 Time Series Data for Monitoring Cork Oak Decline in Portugal

Remote Sensing ◽

10.3390/rs11212515 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2515 ◽

Cited By ~ 4

Author(s):

Ana Navarro ◽

Joao Catalao ◽

Joao Calvao

Keyword(s):

Time Series ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Series Data ◽

Temporal Dynamics ◽

Cork Oak ◽

Series Data ◽

Tree Level ◽

Oak Decline ◽

Sentinel 2

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing to the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsened. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables monitoring of gradual processes. These processes can be monitored using spectral vegetation indices (VI) as their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

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Temporal Variation in Vegetation Indexes for Pine and Beech Stands During the Vegetation Season, Szczecin Lowland, Poland

Quaestiones Geographicae ◽

10.2478/quageo-2014-0037 ◽

2014 ◽

Vol 33 (3) ◽

pp. 131-143 ◽

Cited By ~ 2

Author(s):

Paweł Piekarski ◽

Zbigniew Zwoliński

Keyword(s):

Species Composition ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Vegetation Season ◽

Vegetation Indexes ◽

Beech Stand ◽

Normalized Difference Index ◽

North Western ◽

Phenological Phases

Abstract Located in north-western Poland, the Bukowska Forest and Goleniowska Forest are vast woodlands consisting of areas with a homogeneous species composition that have been scarcely affected by humans. In this respect, they provided an excellent subject for scientific research, the purpose of which was to determine quantitative differences in selected vegetation indices of pine and beech stands in various periods during their vegetation seasons. Another purpose was to characterize the variation in these indices for each stand in its vegetation season. Four Landsat 5 TM images taken in 2007 and 2010 at four different points of vegetation season provided the basis for the analysis. In the analysis, 19 wooded areas with a homogeneous species composition were tested. In Bukowska Forest, the tested area was a beech stand, and in Goleniowska Forest, it was a pine stand. Acquired data was used to calculate the following vegetation indices: Normalized Difference Vegetation Index (NDVI), Transformed Vegetation Index (TVI), Green Normalized Difference Vegetation Index (Green NDVI), Normalized Difference Greenness Index (NDGI) and Normalized Difference Index (NDI). Subsequent research allowed to establish that the beech and pine stands differed significantly with respect to their calculated vegetation indices. These differences derived both from the biochemical and structural attributes of leaves and needles, as well as from transformations that occur in the stands during vegetation seasons. Analysis of the indices’ allowed us to determine these differences and the influence of the stands’ phenological phases on the indices.

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Sensor system for acquisition of vegetation indexes

Revista Brasileira de Engenharia Agrícola e Ambiental ◽

10.1590/1807-1929/agriambi.v25n4p264-269 ◽

2021 ◽

Vol 25 (4) ◽

pp. 264-269

Author(s):

Thales M. de A. Silva ◽

Domingos S. M. Valente ◽

Francisco de A. de C. Pinto ◽

Daniel M. de Queiroz ◽

Nerilson T. Santos

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Analog Circuit ◽

Dynamic Range ◽

Pearson Correlation ◽

Sensor System ◽

Vegetation Indexes ◽

Simple Ratio ◽

Reflectance Data

ABSTRACT Vegetation indexes are important indicators of the health and yield of agricultural crops. Among the sensors used to evaluate vegetation indexes, proximal sensors can be used for real-time decision-making. Thus, the objective of this study was to develop a proximal sensor system based on phototransistors to acquire and store the following vegetation indexes: normalized difference vegetation index, simple ratio, wide dynamic range vegetation index, soil-adjusted vegetation index, and optimized soil-adjusted vegetation index. The sensor system was developed using an analog circuit to acquire reflectance data from red and near-infrared bands. The sensor system was calibrated according to the results of a spectroradiometer, using Zoysia japonica grass as the target. An algorithm that calculates and stores vegetation indexes in a file was developed. The Pearson correlation between the vegetation indexes obtained with the sensor system and the spectroradiometer was evaluated. The vegetation indexes presented a Pearson correlation higher than 0.92 to the estimated values by the spectroradiometer. Under the evaluation conditions, the proposed sensor system could be used to determine all vegetation indexes evaluated.

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Automated Mapping for Long-Term Analysis of Shifting Cultivation in Northeast India

Remote Sensing ◽

10.3390/rs13061066 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1066

Author(s):

Pulakesh Das ◽

Sujoy Mudi ◽

Mukunda D. Behera ◽

Saroj K. Barik ◽

Deepak R. Mishra ◽

...

Keyword(s):

Shifting Cultivation ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Google Earth ◽

Land Restoration ◽

North East ◽

Crop Suitability ◽

Suitability Maps

Assessment of the spatio-temporal dynamics of shifting cultivation is important to understand the opportunities for land restoration. The past studies on shifting cultivation mapping of North-East (NE) India lack systematic assessment techniques. We have developed a decision tree-based multi-step threshold (DTMT) method for consistent and long-term mapping of shifting cultivation using Landsat data from 1975 to 2018. Widely used vegetation indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio (NBR) and its relative difference NBR (RdNBR) were integrated with the suitable thresholds in the classification, which yielded overall accuracy above 85%. A significant decrease in total shifting cultivation area was observed with an overall reduction of 75% from 1975–1976 to 2017–2018. The methodology presented in this study is reproducible with minimal inputs and can be useful to map similar changes by optimizing the index threshold values to accommodate relative differences for other landscapes. Furthermore, the crop-suitability maps generated by incorporating climate and soil factors prioritizes suitable land use of shifting cultivation plots. The Google Earth Engine (GEE) platform was employed for automatic mapping of the shifting cultivation areas at desired time intervals for facilitating seamless dissemination of the map products. Besides the novel DTMT method, the shifting cultivation and crop-suitability maps generated in this study, can aid in sustainable land management.

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Improving the Accuracy of Multiple Algorithms for Crop Classification by Integrating Sentinel-1 Observations with Sentinel-2 Data

Remote Sensing ◽

10.3390/rs13020243 ◽

2021 ◽

Vol 13 (2) ◽

pp. 243

Author(s):

Amal Chakhar ◽

David Hernández-López ◽

Rocío Ballesteros ◽

Miguel A. Moreno

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Temporal Dynamics ◽

Remote Sensing Data ◽

Radar Data ◽

Optical Data ◽

Support Vector ◽

Sensing Data ◽

Crop Classification

The availability of an unprecedented amount of open remote sensing data, such as Sentinel-1 and -2 data within the Copernicus program, has boosted the idea of combining the use of optical and radar data to improve the accuracy of agricultural applications such as crop classification. Sentinel-1’s Synthetic Aperture Radar (SAR) provides co- and cross-polarized backscatter, which offers the opportunity to monitor agricultural crops using radar at high spatial and temporal resolution. In this study, we assessed the potential of integrating Sentinel-1 information (VV and VH backscatter and their ratio VH/VV with Sentinel-2A data (NDVI) to perform crop classification and to define which are the most important input data that provide the most accurate classification results. Further, we examined the temporal dynamics of remote sensing data for cereal, horticultural, and industrial crops, perennials, deciduous trees, and legumes. To select the best SAR input feature, we tried two approaches, one based on classification with only SAR features and one based on integrating SAR with optical data. In total, nine scenarios were tested. Furthermore, we evaluated the performance of 22 nonparametric classifiers on which most of these algorithms had not been tested before with SAR data. The results revealed that the best performing scenario was the one integrating VH and VV with normalized difference vegetation index (NDVI) and cubic support vector machine (SVM) (the kernel function of the classifier is cubic) as the classifier with the highest accuracy among all those tested.

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