scholarly journals Towards improved, cost-effective surveillance of Ixodes ricinus ticks and associated pathogens using species distribution modelling

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Manuela Signorini ◽  
Anna-Sofie Stensgaard ◽  
Michele Drigo ◽  
Giulia Simonato ◽  
Federica Marcer ◽  
...  
Keyword(s):  
Ixodes Ricinus ◽  
Habitat Suitability ◽  
Pathogen Detection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cost Effective ◽  
Limiting Factor ◽  
Sampling Effort ◽  
Remotely Sensed Data ◽  
Ecological Requirements

Various ticks exist in the temperate hilly and pre-alpine areas of Northern Italy, where Ixodes ricinus is the more important. In this area different tick-borne pathogen monitoring projects have recently been implemented; we present here the results of a twoyear field survey of ticks and associated pathogens, conducted 2009-2010 in North-eastern Italy. The cost-effectiveness of different sampling strategies, hypothesized a posteriori based on two sub-sets of data, were compared and analysed. The same two subsets were also used to develop models of habitat suitability, using a maximum entropy algorithm based on remotely sensed data. Comparison of the two strategies (in terms of number of ticks collected, rates of pathogen detection and model accuracy) indicated that monitoring at many temporary sites was more cost-effective than monthly samplings at a few permanent sites. The two model predictions were similar and provided a greater understanding of ecological requirements of I. ricinus in the study area. Dense vegetation cover, as measured by the normalized difference vegetation index, was identified as a good predictor of tick presence, whereas high summer temperatures appeared to be a limiting factor. The study suggests that it is possible to obtain realistic results (in terms of pathogens detection and development of habitat suitability maps) with a relatively limited sampling effort and a wellplanned monitoring strategy.

scholarly journals Identification of Surface Water Storing Sites Using Topographic Wetness Index (TWI) and Normalized Difference Vegetation Index (NDVI)

2018 ◽  
Vol 8 ◽  
pp. 91-100
Author(s):  
Belete Berhanu ◽  
Ethiopia Bisrat
Keyword(s):  
Surface Water ◽  
Water Resource ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cost Effective ◽  
Water Shortage ◽  
Sample Volume ◽  
Runoff Generation ◽  
Topographic Wetness Index ◽  
Wetness Index

Ethiopia is endowed with water and has a high runoff generation area compared to many countries, but the total stored water only goes up to approximately 36BCM. The problem of water shortage in Ethiopia emanates from the seasonality of rainfall and the lack of infrastructure for storage to capture excess runoff during flood seasons. Based on this premise, a method for a syndicate use of topography, land use and vegetation was applied to locate potential surface water storing sites. The steady-state Topographic Wetness Index (TWI) was used to represent the spatial distribution of water flow and water stagnating across the study area and the Normalized Difference Vegetation Index (NDVI) was used to detect surface water through multispectral analysis. With this approach, a number of water storing sites were identified in three categories: primary sources (water bodies based), secondary sources (Swampy/wetland based) and tertiary sources (the land based). A sample volume analysis for the 120354 water storing sites in category two, gives a 44.92BCM potential storing capacity with average depth of 4 m that improves the annual storage capacity of the country to 81BCM (8.6 % of annual renewable water sources). Finally, the research confirmed the TWI and NDVI based approach for water storing sites works without huge and complicated earth work; it is cost effective and has the potential of solving complex water resource challenges through spatial representation of water resource systems. Furthermore, the application of remote sensing captures temporal diversity and includes repetitive archives of data, enabling the monitoring of areas, even those that are inaccessible, at regular intervals.

Evaluating the Persistence of Post-Wildfire Ash: A Multi-Platform Spatiotemporal Analysis

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 68
Author(s):  
Sarah A. Lewis ◽  
Peter R. Robichaud ◽  
Andrew T. Hudak ◽  
Eva K. Strand ◽  
Jan U. H. Eitel ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Cost Effective ◽  
Spatiotemporal Analysis ◽  
Short Wave ◽  
Hydrologic Model ◽  
Landsat 8 ◽  
Data Volume ◽  
Sentinel 2

As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most ‘cost’ effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6–0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources.

Assessment of the climate change-induced impact on forest vegetation in the Mediterranean Basin using an integrated approach

2021 ◽  
Author(s):  
Maria Castellaneta ◽  
Angelo Rita ◽  
Jesus Julio Camarero ◽  
Michele Colangelo ◽  
Francesco Ripullone
Keyword(s):  
Tree Mortality ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Integrated Approach ◽  
Mediterranean Forests ◽  
Climate Extreme ◽  
Remotely Sensed Data ◽  
Climate Anomalies ◽  
Forest Sites ◽  
Tree Ring Data

<p>The recent increase in the frequency and severity of heat weaves and droughts has intensified efforts to understand their impact on forest productivity and tree vigor. These climate extreme events are expected to reduce productivity and increase the tree mortality rate, particularly in drought-prone Mediterranean forests. Thus, our goal is to quantify the impacts of hotter droughts on forests vulnerable to drought in the Italian and Iberian peninsulas by using remotely sensed data (NDVI, Normalized Difference Vegetation Index) to track vegetation changes and tree-ring data from forest sites showing dieback to assess tree’s growth trends. The survey involved the comparison of stands showing dieback where trees showed growth decline and high defoliation rates (decay) versus stands where trees showed low or no defoliation. Our outcomes will be discussed i) to describe the effects of climate anomalies on forest vulnerability in terms of resistance and resilience, and ii) to evaluate the existence of a correlation between vegetation response and “post-disturbance” recovery.</p>

scholarly journals Remote Sensing of Wildfire Using a Small Unmanned Aerial System: Post-Fire Mapping, Vegetation Recovery and Damage Analysis in Grand Bay, Mississippi/Alabama, USA

Drones ◽  
2019 ◽  
Vol 3 (2) ◽  
pp. 43 ◽  
Author(s):  
Sathishkumar Samiappan ◽  
Lee Hathcock ◽  
Gray Turnage ◽  
Cary McCraine ◽  
Jonathan Pitchford ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cost Effective ◽  
Recovery Process ◽  
Turnaround Time ◽  
Coastal Marsh ◽  
Vegetation Recovery ◽  
Burned Area ◽  
Surface Model ◽  
Unmanned Aerial System

Wildfires can be beneficial for native vegetation. However, wildfires can impact property values, human safety, and ecosystem function. Resource managers require safe, easy to use, timely, and cost-effective methods for quantifying wildfire damage and regeneration. In this work, we demonstrate an approach using an unmanned aerial system (UAS) equipped with a MicaSense RedEdge multispectral sensor to classify and estimate wildfire damage in a coastal marsh. We collected approximately 7.2 km2 of five-band multispectral imagery after a wildfire event in February 2016, which was used to create a photogrammetry-based digital surface model (DSM) and orthomosaic for object-based classification analysis. Airborne light detection and ranging data were used to validate the accuracy of the DSM. Four-band airborne imagery from pre- and post-fire were used to estimate pre-fire health, post-fire damage, and track the vegetation recovery process. Immediate and long-term post-fire classifications, area, and volume of burned regions were produced to track the revegetation progress. The UAS-based classification produced from normalized difference vegetation index and DSM was compared to the Landsat-based Burned Area Reflectance Classification. Experimental results show the potential of using UAS and the presented approach compared to satellite-based mapping in terms of classification accuracies, turnaround time, and spatial and temporal resolutions.

scholarly journals Hydrological model parameterization using NDVI values to account for the effects of land cover change on the rainfall–runoff response

2017 ◽  
Vol 48 (6) ◽  
pp. 1455-1473 ◽  
Author(s):  
Vahid Nourani ◽  
Ahmad Fakheri Fard ◽  
Hoshin V. Gupta ◽  
David C. Goodrich ◽  
Faegheh Niazi
Keyword(s):  
Land Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Remote Sensing Data ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Remotely Sensed Data ◽  
Efficiency Criterion ◽  
Mean Values ◽  
Model Parameter

Abstract Classic rainfall–runoff models usually use historical data to estimate model parameters and mean values of parameters are considered for predictions. However, due to climate changes and human effects, model parameters change temporally. To overcome this problem, normalized difference vegetation index (NDVI) derived from remotely sensed data was used in this study to investigate the effect of land cover variations on hydrological response of watersheds using a conceptual rainfall–runoff model. The study area consists of two sub-watersheds (Hervi and Lighvan) with varied land cover conditions. Obtained results show that the one-parameter model generates runoff forecasts with acceptable level of the considered criteria. Remote sensing data were employed to relate land cover properties of the watershed to the model parameter. While a power form of the regression equation could be best fitted to the parameter values using available images of Hervi sub-watershed, for the Lighvan sub-watershed the fitted equation shows somewhat lower correlation due to higher fluctuations of the model parameter. The average values of the Nash–Sutcliffe efficiency criterion of the model were obtained as 0.87 and 0.55, respectively, for Hervi and Lighvan sub-watersheds. Applying this methodology, the model's parameters might be determined using temporal NDVI values.

scholarly journals Modeling the Distribution of Medically Important Tick Species in Florida

Insects ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 190 ◽  
Author(s):  
William H. Kessler ◽  
Claudia Ganser ◽  
Gregory E. Glass
Keyword(s):  
Habitat Suitability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Ixodes Scapularis ◽  
Dermacentor Variabilis ◽  
Amblyomma Americanum ◽  
Suitable Habitat ◽  
Public Health Importance ◽  
Raising Awareness ◽  
Great Public Health

The lone star (Amblyomma americanum), black-legged (Ixodes scapularis) and American dog ticks (Dermacentor variabilis) are species of great public health importance as they are competent vectors of several notable pathogens. While the regional distributions of these species are well characterized, more localized distribution estimates are sparse. We used records of field collected ticks and an ensemble modeling approach to predict habitat suitability for each of these species in Florida. Environmental variables capturing climatic extremes were common contributors to habitat suitability. Most frequently, annual precipitation (Bio12), mean temperature of the driest quarter (Bio9), minimum temperature of the coldest month (Bio6), and mean Normalized Difference Vegetation Index (NDVI) were included in the final models for each species. Agreement between the modeling algorithms used in this study was high and indicated the distribution of suitable habitat for all three species was reduced at lower latitudes. These findings are important for raising awareness of the potential for tick-borne pathogens in Florida.

scholarly journals Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

2018 ◽  
Vol 10 (12) ◽  
pp. 1953 ◽  
Author(s):  
Safa Bousbih ◽  
Mehrez Zribi ◽  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Zohra Lili-Chabaane ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Arid Region ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Remotely Sensed Data ◽  
Semi Arid Region ◽  
Semi Arid ◽  
Sentinel 2 ◽  
Reference Fields

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

scholarly journals CHARACTERIZING LANDSCAPE SPATIAL HETEROGENEITY USING SEMIVARIOGRAM PARAMETERS DERIVED FROM NDVI IMAGES

CERNE ◽  
2017 ◽  
Vol 23 (4) ◽  
pp. 413-422 ◽  
Author(s):  
Eduarda Martiniano de Oliveira Silveira ◽  
José Márcio de Mello ◽  
Fausto Weimar Acerbi Júnior ◽  
Aliny Aparecida dos Reis ◽  
Kieran Daniel Withey ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Heterogeneity ◽  
Spatial Resolution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landscape Heterogeneity ◽  
Landsat 8 ◽  
Remotely Sensed Data ◽  
Object Based ◽  
Geostatistical Techniques

ABSTRACT Assuming a relationship between landscape heterogeneity and measures of spatial dependence by using remotely sensed data, the aim of this work was to evaluate the potential of semivariogram parameters, derived from satellite images with different spatial resolutions, to characterize landscape spatial heterogeneity of forested and human modified areas. The NDVI (Normalized Difference Vegetation Index) was generated in an area of Brazilian amazon tropical forest (1,000 km²). We selected samples (1 x 1 km) from forested and human modified areas distributed throughout the study area, to generate the semivariogram and extract the sill (σ²-overall spatial variability of the surface property) and range (φ-the length scale of the spatial structures of objects) parameters. The analysis revealed that image spatial resolution influenced the sill and range parameters. The average sill and range values increase from forested to human modified areas and the greatest between-class variation was found for LANDSAT 8 imagery, indicating that this image spatial resolution is the most appropriate for deriving sill and range parameters with the intention of describing landscape spatial heterogeneity. By combining remote sensing and geostatistical techniques, we have shown that the sill and range parameters of semivariograms derived from NDVI images are a simple indicator of landscape heterogeneity and can be used to provide landscape heterogeneity maps to enable researchers to design appropriate sampling regimes. In the future, more applications combining remote sensing and geostatistical features should be further investigated and developed, such as change detection and image classification using object-based image analysis (OBIA) approaches.

scholarly journals Predicting the distribution of forest tree species using topographic variables and vegetation index in eastern Acre, Brazil

2015 ◽  
Vol 45 (2) ◽  
pp. 167-174 ◽  
Author(s):  
Symone Maria de Melo FIGUEIREDO ◽  
Eduardo Martins VENTICINQUE ◽  
Evandro Orfanó FIGUEIREDO ◽  
Evandro José Linhares FERREIRA
Keyword(s):  
Tree Species ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Elevation Gradient ◽  
Forest Tree ◽  
Entropy Method ◽  
Slope Aspect ◽  
Tropical Species ◽  
Ecological Requirements ◽  
Timber Tree

Species distribution modeling has relevant implications for the studies of biodiversity, decision making about conservation and knowledge about ecological requirements of the species. The aim of this study was to evaluate if the use of forest inventories can improve the estimation of occurrence probability, identify the limits of the potential distribution and habitat preference of a group of timber tree species. The environmental predictor variables were: elevation, slope, aspect, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). To estimate the distribution of species we used the maximum entropy method (Maxent). In comparison with a random distribution, using topographic variables and vegetation index as features, the Maxent method predicted with an average accuracy of 86% the geographical distribution of studied species. The altitude and NDVI were the most important variables. There were limitations to the interpolation of the models for non-sampled locations and that are outside of the elevation gradient associated with the occurrence data in approximately 7% of the basin area. Ceiba pentandra (samaúma), Castilla ulei (caucho) and Hura crepitans (assacu) is more likely to occur in nearby water course areas. Clarisia racemosa (guariúba), Amburana acreana (cerejeira), Aspidosperma macrocarpon (pereiro), Apuleia leiocarpa (cumaru cetim), Aspidosperma parvifolium (amarelão) and Astronium lecointei (aroeira) can also occur in upland forest and well drained soils. This modeling approach has potential for application on other tropical species still less studied, especially those that are under pressure from logging.

scholarly journals ÍNDICE DE VEGETAÇÃO POR DIFERENÇA NORMALIZADA ASSOCIADO ÀS VARIÁVEIS PLUVIOMÉTRICAS PARA A SUB-BACIA DO RIO ESPINHARAS – PB/RN

Nativa ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 106-114
Author(s):  
Ewerton Medeiros Simões ◽  
Joedla Rodrigues de Lima ◽  
Izaque Francisco Candeia de Mendonça
Keyword(s):  
Rainy Season ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Progressive Increase ◽  
Limiting Factor ◽  
Landsat 8 ◽  
Dry Period ◽  
Rainy Period ◽  
Brazilian Semiarid ◽  
Weather Stations

No semiárido brasileiro, onde se insere o bioma caatinga, a precipitaçãos é um dos fator limitante para seu desenvolvimento sócioeconômico e ambiental, este estudo avaliou a correlação existente entre o nível de cobertura vegetal e as variáveis pluviométricas locais, considerando a climatologia de 2005 e 2015, utilizando-se imagens dos sensores TM e OLI dos satélites Landsat 5 e Landsat 8, respectivamente. O ano de 2005 apresentou maiores valores de NDVI em relação a 2015, com valores máximos de 0,71 e 0,78 no período seco e úmido, respectivamente. No ano de 2015, os valores máximos são de 0,64 e 0,61, para o período seco e úmido, respectivamente. Os maiores valores foram observados no período chuvoso de 2005, nas áreas de influência das estações meteorológicas de Matureia, Salgadinho e Areia de Baraúnas. No período seco, nota-se a baixa variabilidade dos valores de NDVI, sendo as maiores leituras observadas nas estações de Matureia, Salgadinho e Teixeira. As estações que apresentaram as maiores reduções nos valores de NDVI de 2005 para 2015, no período chuvoso, foram Matureia, Santa Teresinha e Salgadinho, com reduções de 41,9%, 38,2% e 32,7%, respectivamente. As correlações mais significativas foram estabelecidas para os períodos secos. As menores correlações foram verificadas no período chuvoso. A elevação dos níveis pluviométricos na região não implicou o aumento progressivo nos valores de NDVI. Palavras-chave: semiárido; geoprocessamento; índice de vegetação normalizada.   Normalized difference vegetation index associated with pluviometric variables for Espinharas River sub-basin - PB/RN States   ABSTRACT: In the Brazilian semiarid, where the caatinga biome is inserted, precipitation is a limiting factor for its socioeconomic and environmental development, This study evaluated the correlation between the level of vegetation cover and the local rainfall variables, considering the climatology of 2005 and 2015, using images from the TM and OLI sensors of the Landsat 5 and Landsat 8 satellites, respectively. The year 2005 presented higher NDVI values compared to 2015, with maximum values of 0.71 and 0.78 in the dry and wet periods, respectively. In 2015, the maximum values are 0.64 and 0.61, for the dry and wet periods, respectively. The highest values were observed in the rainy period of 2005, in the weather stations of Matureia, Salgadinho and Areia de Baraúnas. In the dry period, the low variability of NDVI values is noted, with the highest readings observed in the Matureia, Salgadinho and Teixeira platforms. The platforms that showed the greatest reductions in NDVI values from 2005 to 2015, in the rainy season, were Matureia, Santa Teresinha and Salgadinho, with reductions of 41.9%, 38.2% and 32.7%, respectively. The most significant correlations were established for the dry periods. The smallest correlations were found in the rainy season. The increase in rainfall levels in the region did not imply a progressive increase in NDVI values. Keywords: semiarid; geoprocessing; normalized difference vegetation index.

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