scholarly journals Identifying Edaphic Factors and Normalized Difference Vegetation Index Metrics Driving Wildlife Mortality From Anthrax in Kenya’s Wildlife Areas

2021 ◽  
Vol 9 ◽  
Author(s):  
Vincent Obanda ◽  
Viola A. Otieno ◽  
Edward M. Kingori ◽  
David Ndeereh ◽  
Olivia W. Lwande ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Spatial Heterogeneity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Localization ◽  
Sub Saharan Africa ◽  
Extreme Weather Events ◽  
Edaphic Factors ◽  
The Impact ◽  
Wildlife Mortality

Anthrax, an acute disease of homeotherms caused by soil-borne Bacillus anthracis is implicated in dramatic declines in wildlife mainly in sub-Saharan Africa. Anthrax outbreaks are often localized in space and time. Therefore, understanding predictors of the spatial and temporal occurrence of anthrax in wildlife areas is useful in supporting early warning and improved response and targeting measures to reduce the impact of epizootic risk on populations. Spatial localization of anthrax is hypothesized to be driven by edaphic factors, while the temporal outbreaks are thought to be driven by extreme weather events including temperature, humidity, rainfall, and drought. Here, we test the role of select edaphic factors and normalized difference vegetation index (NDVI) metrics driven by vegetation structure and climate variability on the spatial and temporal patterns of wildlife mortality from anthrax in key wildlife areas in Kenya over a 20-year period, from 2000 to 2019. There was a positive association between the number of anthrax outbreaks and the total number of months anthrax was reported during the study period and the nitrogen and organic carbon content of the soil in each wildlife area. The monthly occurrence (timing) of anthrax in Lake Nakuru (with the most intense outbreaks) was positively related to the previous month’s spatial heterogeneity in NDVI and monthly NDVI deviation from 20-year monthly means. Generalized linear models revealed that the number of months anthrax was reported in a year (intensity) was positively related to spatial heterogeneity in NDVI, total organic carbon and cation exchange capacity of the soil. These results, examined in the light of experimental studies on anthrax persistence and amplification in the soil enlighten on mechanisms by which these factors are driving anthrax outbreaks and spatial localization.

scholarly journals The Impact of Acquisition Date on the Prediction Performance of Topsoil Organic Carbon from Sentinel-2 for Croplands

2019 ◽  
Vol 11 (18) ◽  
pp. 2143 ◽  
Author(s):  
Emmanuelle Vaudour ◽  
Cécile Gomez ◽  
Thomas Loiseau ◽  
Nicolas Baghdadi ◽  
Benjamin Loubet ◽  
...  
Keyword(s):  
Time Series ◽  
Organic Carbon ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Soil Surface ◽  
Prediction Performance ◽  
Optical Data ◽  
Least Squares Regression ◽  
The Impact ◽  
Sentinel 2

The spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 optical data to predict SOC content over temperate agroecosystems characterized by annual crops, using a single acquisition date. Considering a Sentinel-2 time series, this work intends to analyze the impact of acquisition date, and related weather and soil surface conditions on the prediction performance of topsoil SOC content (plough layer). A Sentinel-2 time-series was gathered, comprised of the dates corresponding to both the maximum of bare soil coverage and minimum of cloud coverage. Cross-validated partial least squares regression (PLSR) models were constructed between soil reflectance image spectra, and SOC content analyzed from 329 top soil samples collected over the study area. Cross-validation R2 ranged from 0.005 to 0.58, root mean square error from 5.86 to 3.02 g·kg−1 and residual prediction deviation values from 1.0 to 1.5 (without unit), according to date. The main factors influencing these differences were soil roughness, in conjunction with soil moisture, and the cloud and cloud shadow cover of the entire tile. The best performing dates were spring dates characterized by both lowest soil surface roughness and moisture content. Normalized difference vegetation index (NDVI) values below 0.35 did not influence prediction performance. This consolidates the previous results obtained during single date acquisitions and offers wider perspectives for the further use of Sentinel-2 into multidate mosaics for digital soil mapping.

scholarly journals Urban Fire Severity and Vegetation Dynamics in Southern California

2020 ◽  
Vol 13 (1) ◽  
pp. 19
Author(s):  
Lauren E. H. Mathews ◽  
Alicia M. Kinoshita
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Current Knowledge ◽  
Fire Severity ◽  
Satellite Image ◽  
Burn Severity ◽  
Native Vegetation ◽  
Riparian Areas ◽  
The Impact

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

scholarly journals Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

2020 ◽  
Vol 12 (2) ◽  
pp. 220 ◽  
Author(s):  
Han Xiao ◽  
Fenzhen Su ◽  
Dongjie Fu ◽  
Qi Wang ◽  
Chong Huang
Keyword(s):  
Spatial Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mangrove Ecosystem ◽  
Early Growth ◽  
Growth Stages ◽  
Human Disturbances ◽  
Vegetation Growth ◽  
Two Parameters ◽  
The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

scholarly journals Seasonal Variations of Daytime Land Surface Temperature and Their Underlying Drivers over Wuhan, China

2021 ◽  
Vol 13 (2) ◽  
pp. 323
Author(s):  
Liang Chen ◽  
Xuelei Wang ◽  
Xiaobin Cai ◽  
Chao Yang ◽  
Xiaorong Lu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Temperature ◽  
Maximum Temperature Difference ◽  
Rapid Urbanization ◽  
The Impact ◽  
The City

Rapid urbanization greatly alters land surface vegetation cover and heat distribution, leading to the development of the urban heat island (UHI) effect and seriously affecting the healthy development of cities and the comfort of living. As an indicator of urban health and livability, monitoring the distribution of land surface temperature (LST) and discovering its main impacting factors are receiving increasing attention in the effort to develop cities more sustainably. In this study, we analyzed the spatial distribution patterns of LST of the city of Wuhan, China, from 2013 to 2019. We detected hot and cold poles in four seasons through clustering and outlier analysis (based on Anselin local Moran’s I) of LST. Furthermore, we introduced the geographical detector model to quantify the impact of six physical and socio-economic factors, including the digital elevation model (DEM), index-based built-up index (IBI), modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), population, and Gross Domestic Product (GDP) on the LST distribution of Wuhan. Finally, to identify the influence of land cover on temperature, the LST of croplands, woodlands, grasslands, and built-up areas was analyzed. The results showed that low temperatures are mainly distributed over water and woodland areas, followed by grasslands; high temperatures are mainly concentrated over built-up areas. The maximum temperature difference between land covers occurs in spring and summer, while this difference can be ignored in winter. MNDWI, IBI, and NDVI are the key driving factors of the thermal values change in Wuhan, especially of their interaction. We found that the temperature of water area and urban green space (woodlands and grasslands) tends to be 5.4 °C and 2.6 °C lower than that of built-up areas. Our research results can contribute to the urban planning and urban greening of Wuhan and promote the healthy and sustainable development of the city.

scholarly journals Resident vegetation modifies climate-driven elevational shift of a mountain sedge

Alpine Botany ◽  
2020 ◽  
Author(s):  
Harald Crepaz ◽  
Georg Niedrist ◽  
Johannes Wessely ◽  
Mattia Rossi ◽  
Stefan Dullinger
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Distribution Data ◽  
Study Region ◽  
Potential Competitor ◽  
Elevational Shift ◽  
The Impact ◽  
Mountain Plant

Abstract Mountain plant species are changing their ranges in response to global warming. However, these shifts vary tremendously in rate, extent and direction. The reasons for this variation are yet poorly understood. A process potentially important for mountain plant re-distribution is a competition between colonizing species and the resident vegetation. Here, we focus on the impact of this process using the recent elevational shift of the sedge Carex humilis in the northern Italian Alps as a model system. We repeated and extended historical sampling (conducted in 1976) of the species in the study region. We used the historical distribution data and historical climatic maps to parameterize a species distribution model (SDM) and projected the potential distribution of the species under current conditions. We compared the historical and the current re-survey for the species in terms of the cover of important potential competitor species as well as in terms of the productivity of the resident vegetation indicated by the Normalized Difference Vegetation Index (NDVI). We found that Carex humilis has shifted its leading range margin upward rapidly (51.2 m per decade) but left many sites that have become climatically suitable since 1976 according to the SDM uncolonized. These suitable but uncolonized sites show significantly higher coverage of all dwarf shrub species and higher NDVI than the sites occupied by the sedge. These results suggest that resistance of the resident vegetation against colonization of migrating species can indeed play an important role in controlling the re-distribution of mountain plants under climate change.

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 Estimating Sahelian and East African soil moisture using the Normalized Difference Vegetation Index

2013 ◽  
Vol 10 (6) ◽  
pp. 7963-7997 ◽  
Author(s):  
A. McNally ◽  
C. Funk ◽  
G. J. Husak ◽  
J. Michaelsen ◽  
B. Cappelaere ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Early Warning ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Drought ◽  
Sub Saharan Africa ◽  
Sub Saharan ◽  
The Relationship ◽  
African Sahel

Abstract. Rainfall gauge networks in Sub-Saharan Africa are inadequate for assessing Sahelian agricultural drought, hence satellite-based estimates of precipitation and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) provide the main source of information for early warning systems. While it is common practice to translate precipitation into estimates of soil moisture, it is difficult to quantitatively compare precipitation and soil moisture estimates with variations in NDVI. In the context of agricultural drought early warning, this study quantitatively compares rainfall, soil moisture and NDVI using a simple statistical model to translate NDVI values into estimates of soil moisture. The model was calibrated using in-situ soil moisture observations from southwest Niger, and then used to estimate root zone soil moisture across the African Sahel from 2001–2012. We then used these NDVI-soil moisture estimates (NSM) to quantify agricultural drought, and compared our results with a precipitation-based estimate of soil moisture (the Antecedent Precipitation Index, API), calibrated to the same in-situ soil moisture observations. We also used in-situ soil moisture observations in Mali and Kenya to assess performance in other water-limited locations in sub Saharan Africa. The separate estimates of soil moisture were highly correlated across the semi-arid, West and Central African Sahel, where annual rainfall exhibits a uni-modal regime. We also found that seasonal API and NDVI-soil moisture showed high rank correlation with a crop water balance model, capturing known agricultural drought years in Niger, indicating that this new estimate of soil moisture can contribute to operational drought monitoring. In-situ soil moisture observations from Kenya highlighted how the rainfall-driven API needs to be recalibrated in locations with multiple rainy seasons (e.g., Ethiopia, Kenya, and Somalia). Our soil moisture estimates from NDVI, on the other hand, performed well in Niger, Mali and Kenya. This suggests that the NDVI-soil moisture relationship may be more robust across rainfall regimes than the API because the relationship between NDVI and plant available water is less reliant on local characteristics (e.g., infiltration, runoff, evaporation) than the relationship between rainfall and soil moisture.

scholarly journals Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

2019 ◽  
Vol 26 (3) ◽  
pp. 117
Author(s):  
Tri Muji Susantoro ◽  
Ketut Wikantika ◽  
Agung Budi Harto ◽  
Deni Suwardi
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Landsat 8 ◽  
Gas Field ◽  
Research Results ◽  
The Third ◽  
Third Phase ◽  
The Impact

This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.

scholarly journals Monitoring of a Calcium Biofortification Workflow for Tubers of Solanum tuberosum L. cv. Picasso Using Smart Farming Technology

2021 ◽  
Vol 3 (1) ◽  
pp. 18
Author(s):  
Ana Rita F. Coelho ◽  
Inês Carmo Luís ◽  
Ana Coelho Marques ◽  
Cláudia Campos Pessoa ◽  
Diana Daccak ◽  
...  
Keyword(s):  
Solanum Tuberosum ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Foliar Application ◽  
Solanum Tuberosum L ◽  
Test System ◽  
Experimental Production ◽  
Large Crop ◽  
The Impact

Due to the rapid growth of the population worldwide and the need to provide food safety in large crop productions, UAVs (unmanned aerial vehicles) are being used in agriculture to provide valuable data for decision making. Accordingly, through precision agriculture, efficient management of resources, using data obtained by the technologies, is possible. Through remote sensed data collected in a crop region, it is possible to create NDVI (normalized difference vegetation index) maps, which are a powerful tool to detect stresses, namely, in plants. Accordingly, using smart farm technology, this study aimed to assess the impact of Ca biofortification on leaves of Solanum tuberosum L. cv. Picasso. As such, using an experimental production field of potato tubers (GPS coordinates: 39°16′38,816′′ N; 9°15′9128′′ W) as a test system, plants were submitted to a Ca biofortification workflow through foliar spraying with CaCl2 or, alternatively, chelated calcium (Ca-EDTA) at concentrations of 12 and 24 kg·ha−1. A lower average NDVI in Ca-EDTA 12 kg·ha−1 treatment after the fourth foliar application was found, which, through the application of the CieLab scale, correlated with lower L (darker color) and hue parameters, regarding control plants. Additionally, a higher Ca content was quantified in the leaves. The obtained data are discussed, and it is concluded that Ca-EDTA 12 kg·ha−1 triggers lower vigor in Picasso potatoes leaves.

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