Endangered species’ trait responses to environmental variability in agricultural settings

Understanding the spatial and temporal effects of variable environmental conditions on demographic characteristics is important in order to stop the decline of endangered-species populations. To capture interactions between a species and its environment, in this work the demographic traits of the European ground squirrel (EGS), Spermophilus citellus, were modeled as a function of agricultural landscape structure. The habitat suitability index was determined for 20 localities within the study area based on habitat use, management and type. After mapping the habitat patch occupancy in the field, crop cover maps, the average normalized difference vegetation index (NDVI) and automated water extraction index (AWEI) were obtained from satellite images covering the period 2013-2015. This data was used to develop population-level generalized linear models (GLMs) and individual-level conditional mixed-effects models (GLMMs) in R package Ime4, focusing on the key demographic traits of the EGS. The land composition and patch carrying capacity (PCC) are the key determinants of the endangered EGS population size, while system productivity is the main factor influencing individuals? body condition after monitoring for variations across sampling years and age classes. The proposed landscape structural models show that human activities and abiotic factors shape the demographic rates of the EGS. Thus, to conserve threatened species, an appropriate focus on the spatial adaptation strategies should be employed.

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Spatial Modeling of Forage Crops for Tiger Prey Species in the Area Surrounding Highway 304 in the Dong Phayayen-Khao Yai Forest Complex

Environment and Natural Resources Journal ◽

10.32526/ennrj/19/2020234 ◽

2021 ◽

Vol 19 (3) ◽

pp. 220-229

Author(s):

Paanwaris Paansri ◽

◽

Natcha Sangprom ◽

Warong Suksavate ◽

Aingorn Chaiyes ◽

...

Keyword(s):

Spatial Modeling ◽

Habitat Management ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Linear Models ◽

Wildlife Habitat ◽

Forage Crops ◽

Grass Biomass ◽

Sambar Deer ◽

Shrub Biomass

Spatial modeling is an analytical procedure that simulates real-world conditions using remote sensing and geographic information systems. The field data in this study were collected from 318 survey plots in the area surrounding highway 304 in the Dong Phayayen-Khao Yai Forest Complex (DPKY-FC) during the 2019 rainy season. Forage-crop biomass was collected from all plots. We focused on sambar deer (Rusa unicolor) and gaur (Bos gaurus), which are the main prey for tigers in this area. We created spatial models using generalized linear models with stepwise regression. The results indicated that the normalized difference vegetation index (NDVI) varied directly with grass biomass but inversely with shrub biomass (p<0.05). Elevation varied directly with forb biomass but inversely with shrub biomass (p<0.05). The probability of occurrence of sambar deer varied directly with distance from disturbance variables, distance from the stream, and grass biomass (p<0.001), but inversely with NDVI (p<0.05). The occurrence of gaur varied directly with NDVI (p=0.08), but varied inversely with slope, distance from the road, and distance from the stream (p<0.05). Our results demonstrate that spatial modeling can be an effective tool for wildlife habitat management in the area surrounding highway 304.

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Do Exposures to Green Space Reduce the Risk of Hypertension?

10.21203/rs.3.rs-131621/v1 ◽

2020 ◽

Author(s):

Jie Jiang ◽

Gongbo Chen ◽

Baojing Li ◽

Yuanan Lu ◽

Yuming Guo ◽

...

Keyword(s):

Blood Pressure ◽

Vegetation Index ◽

Green Space ◽

Normalized Difference Vegetation Index ◽

Linear Models ◽

Cross Sectional Study ◽

Central China ◽

Mixed Linear Models ◽

Cross Sectional ◽

Standardized Protocol

Abstract Background: Few epidemiological research examined the effects of greenness on cardiovascular diseases in developing countries. We aimed to explore the relationships between green space and hypertension and blood pressure in China.Methods: This cross-sectional study recruited 39, 259 adults from five counties in central China. Blood pressure measurements were performed according to a standardized protocol. Normalized diﬀerence vegetation index (NDVI) and enhanced vegetation index (EVI) was used to assess the exposure to greenness. We used mixed linear models to test greenspace-cardiovascular disease outcome pathways.Results: Higher green space was related to decreased hypertension prevalence and blood pressure. After fully adjusting the covariates, each interquartile range increase in NDVI500m and EVI500m were related to an 8% decrease in odds of hypertension. The changes in SBP and DBP (95% CI) were - 0.88 mm Hg (- 1.17, - 0.58) and - 0.64 mm Hg (- 0.82, - 0.46) for NDVI, and - 0.79 mm Hg (- 1.14, - 0.45) and - 0.67 mm Hg (- 0.87, - 0.46) for EVI, respectively. Subgroup analyses showed that the effects of green space were more pronounced in males, smokers, and drinkers.Conclusions: The effects of green space may reduce the risk of hypertension. Also, behavioral factors may affect this potential pathway.

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Relating goat milk production according to the Normalized Difference Vegetation Index and precipitations in the Chaco forest throughout the 2016-2018 period

Revista de la Facultad de Agronomía ◽

10.24215/16699513e078 ◽

2021 ◽

Vol 120 (2) ◽

pp. 078

Author(s):

José L. Tiedemann ◽

Jorge Nelson Leguizamón-Carate ◽

Florencia Salinas ◽

Florencia Frau

Keyword(s):

Milk Production ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Linear Models ◽

Goat Milk ◽

Biomass Productivity ◽

Monthly Precipitation ◽

Average Monthly Precipitation ◽

Forest Stability ◽

Energetic Reserves

This work aimed to quantify and relate goat milk production and the Normalized Difference of Vegetation Index of the semiarid Chaco forest and the monthly average precipitation along the 2016-2018 period. The work was carried out in El Polear, in Santiago del Estero, Argentina. Even though the NDVI of the forest and its lower strata biomass productivity were affected by drought, its milk production curve remained unaffected. This may be due to the forest stability resulting from the deep rooted trees that includes, to the strategic displacement of the phenophase in its lower strata (broadleaves, herbaceous) in drought seasons and the adaptation to the changes in the goat diet selectivity before forage fluctuations. Winter NDVI peaks should be considered for new lines of research on their contribution to the energetic reserves of the goat component at the beginning of winter. Significant straight relationships (p<0.05) were found between the average goat milk production and the average monthly precipitation (r=0.64) as well as the NDVI and the semiarid Chaco forest (r=0.59). The resulting linear models involving goat milk production with both precipitation and NDVI had moderate and significant (p<0.05) explaining power (R2=0.41) and (R2=0.35), respectively. These models make the seasonal goat milk production predictable and the planning and the making decision process of both producers and the agroindustry easier.

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Nesting sites in agricultural landscapes may reduce the reproductive success of populations of Blanding’s Turtles (Emydoidea blandingii)

Canadian Journal of Zoology ◽

10.1139/cjz-2015-0154 ◽

2016 ◽

Vol 94 (1) ◽

pp. 61-67 ◽

Cited By ~ 5

Author(s):

A.B. Mui ◽

C.B. Edge ◽

J.E. Paterson ◽

B. Caverhill ◽

B. Johnson ◽

...

Keyword(s):

Site Selection ◽

Vegetation Cover ◽

Environmental Variables ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Landscape ◽

Agricultural Landscapes ◽

Turtle Species ◽

Emydoidea Blandingii ◽

Parallel Change

Almost all turtle species nest in terrestrial environments and maternal site selection represents a critical component of nest success. Females use cues in the current environment to predict the future conditions for embryo development. However, in disturbed landscapes, current and future conditions may not be correlated. We compared selection of nest sites by Blanding’s Turtles (Emydoidea blandingii (Holbrook, 1838)) in a (relatively undisturbed) park and a (heavily disturbed) agricultural landscape in Ontario, Canada, using field measurements and satellite imagery. Environmental variables were compared using logistic regression and Akaike’s information criterion (AIC) based on data measured at nest (presence) and random (pseudoabsence) locations. Specific environmental variables associated with site selection differed between study areas. Most notably, NDVI (normalized difference vegetation index, a proxy for vegetation cover) increased significantly during the year at the agricultural locale, corresponding with the growth of planted fields. No parallel change was observed at the park locale where canopy cover remained more consistent. An increase in vegetation cover may alter nest temperatures and soil moisture. Combined with the unpredictability in timing of crop sowing and harvesting, findings suggest that nests in agricultural fields may act as ecological sinks and that other species nesting in similarly altered habitats may be subjected to the same threats.

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Retrieving wheat Biomass by using a hyper-spectral device on UAV

Advances in Animal Biosciences ◽

10.1017/s2040470017001182 ◽

2017 ◽

Vol 8 (2) ◽

pp. 833-836

Author(s):

L. Xia ◽

R. R. Zhang ◽

L. P. Chen ◽

Y. Wen ◽

F. Zhao ◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Linear Models ◽

Hyperspectral Data ◽

Coefficient Of Determination ◽

Vegetation Indexes ◽

Spectral Device ◽

Dimensional Distribution ◽

Aerial Vehicle

In this study, the biomass of winter wheat was estimated by using hyperspectral data obtained from a hyperspectral camera on an Unmanned Aerial Vehicle (UAV). Every two bands from the hyperspectral data were selected to calculate two kinds of vegetation indexes: the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI). Linear models were established between winter wheat biomass and those indexes, and coefficient of determination R2 was used to draw the two-dimensional distribution of R2 values. The comparison between NDVI and RVI for pixel covered by soil and wheat showed that RVI is more efficient to mask the influence from soil than NDVI. For calculating the NDVI, optimal bands are located mainly around 820 nm and 725 nm to 750 nm. For assessing RVI, the wavelength range from 820 to 832 nm, 794 to 808 nm, 770 to 788 nm, 725 nm to 750 nm and 890 nm for RVI are most suitable. Those optimal bands can achieve a coefficient of determination R2 higher than 0.88 by using the linear regression model in the study.

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Phytomass mapping of the "seridó caatinga" vegetation by the plant area and the normalized difference vegetation indeces

Scientia Agricola ◽

10.1590/s0103-90162002000400014 ◽

2002 ◽

Vol 59 (4) ◽

pp. 707-715 ◽

Cited By ~ 11

Author(s):

Thomaz Corrêa e Castro da Costa ◽

Luciano José de Oliveira Accioly ◽

Maria Ap. José de Oliveira ◽

Nivaldo Burgos ◽

Flávio Hugo Barreto Batista da Silva

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Linear Models ◽

Dry Weight ◽

Empirical Method ◽

Orbital Parameter ◽

Landsat Images ◽

Area Index ◽

Plant Area Index ◽

Plant Area

Phytomass is a critical information for economic and environmental activities like the establishment of policies for timber resources, forest management, studies of plant nutrient cycling, CO2 sink, among other. The phytomass of a Caatinga area was obtained by an empirical method using normalized difference vegetation index (NDVI) of Landsat images, the plant area index (PAI) and the phytomass inventory. At a first stage, linear, logarithmic and non-linear models were developed and tested. Bush and tree specimens were considered in the study, so that most of the individuals that contribute to the spectral answer detected by satellite images were included. At a second stage, the orbital parameter NDVI was used to map the PAI, which was used to map the phytomass, based on the relationship of this phytomass as a function of PAI. The residues between measurements and estimates based on NDVI varied from 0 to 84%, while the residues of total dry weight of phytomass per ha obtained by mapping and by dendrometrical equations varied from 5 to 104%, with a large trend of 166 and 448% in open Caatinga areas, due to the contribution of the herbaceous stratum to NDVI.

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Evaluation of RapidEye vegetation indices for prediction of biophysical/biochemical variables of winter wheat

Aerospace Research in Bulgaria ◽

10.3897/arb.v30.e06 ◽

2018 ◽

Vol 30 ◽

pp. 63-74

Author(s):

Ilina Kamenova ◽

Petar Dimitrov ◽

Rusina Yordanova

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Linear Models ◽

Chlorophyll Concentration ◽

Vegetation Indices ◽

Area Index ◽

Leaf Chlorophyll ◽

Red Edge ◽

Absorbed Photosynthetically Active Radiation

The aim of the study is to evaluate the possibility for using RapidEye data for prediction of Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR), fraction of vegetation Cover (fCover), leaf Chlorophyll Concentration (CC) and Canopy Chlorophyll Content (CCC) of winter wheat. The relation of a number of vegetation indices (VIs) with these crop variables are accessed based on a regression analysis. Indices, which make use of the red edge band, such as Chlorophyll Index red edge (CIre) and red edge Normalized Difference Vegetation Index (reNDVI), were found most useful, resulting in linear models with R2 of 0.67, 0.71, 0.72, and 0.76 for fCover, LAI, CCC, and fAPAR respectively. CC was not related with any of the VIs.

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Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting

Remote Sensing ◽

10.3390/rs11091073 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1073 ◽

Cited By ~ 11

Author(s):

Pedro C. Towers ◽

Albert Strever ◽

Carlos Poblete-Echeverría

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Linear Models ◽

Dynamic Range ◽

Spectral Response ◽

Incident Light ◽

Vegetation Indices ◽

Area Index

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.

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Automatic Boundary Delineation of Agricultural Fields in Multi Temporal Satellite Imagery with Segmentation

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.b4045.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 3527-3535

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Agricultural Landscape ◽

Performance Metrics ◽

Satellite Image ◽

Small Variation ◽

Current Method ◽

Land Use Classification ◽

Novel Approach ◽

Multi Temporal

A right difference in agricultural areas is the primary necessity for any sector-primarily based implementation together with estimating agricultural subsidies. Improved decision remote sensing image currently offer higher useful geographic records to delineate regions; however, their automatic managing is tedious. Its miles therefore critical to increase strategies that permit this activity to be completed right away. In any such process, a novel approach named improving the Enhanced Gustafson-Kessel-Like clustering (EGKL) version explores the use of a pc-mastering device to define agrarian areas. The current method seems for limits as either segment corners or linear traits are adjoining regions of small variation all the time series. Nearby everyday deviations from all images a while are coupled, ensuing in a sequence of extended directional edge filters. Even though, in order beautify the excellent of boundary delineation, this advised paintings is merged with sequential features of small variability across the time collection, which includes the standard deviation (STD), Near-Infra Red (NIR) band, or an index along with the Normalized Difference Vegetation Index (NDVI), or band ratios (particularly for hill us of a), or important component images. A photograph evaluation of the effects obtained with the aid of a methodology relevant to two fields of an excessive-resolution satellite image of the fractured agricultural landscape shows that it is helpful to apply the guide vector machines technique for such a task. Finally, the experimental results reveal that the proposed segmentation method is more efficient than the existing segmentation techniques in factors of each quantitative overall performance metrics and appropriateness for land-use classification.

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Pemetaan Pulau Kecil Gelasa Kabupaten Bangka Tengah

Journal of Tropical Marine Science ◽

10.33019/jour.trop.mar.sci.v2i1.909 ◽

2019 ◽

Vol 2 (1) ◽

pp. 11-14

Author(s):

Wahyu Adi

Keyword(s):

Coral Reefs ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Image Interpretation ◽

Vegetation Coverage ◽

Small Island ◽

Shallow Waters ◽

Small Islands ◽

Seagrass Beds ◽

Cover Density

Pulau Kecil Gelasa merupakan daerah yang belum banyak diteliti. Pemetaan ekosistem di pulau kecil dilakukan dengan bantuan citra Advanced Land Observing Satellite (ALOS). Penelitian terdahulu diketahui bahwa ALOS memiliki kemampuan memetakan terumbu karang dan padang lamun di perairan dangkal serta mampu memetakan kerapatan penutupan vegetasi. Metode interpretasi citra menggunakan alogaritma indeks vegetasi pada citra ALOS yaitu NDVI (Normalized Difference Vegetation Index), serta pendekatan Lyzengga untuk mengkoreksi kolom perairan. Hasil penelitian didapatkan luasan Padang Lamun di perairan dangkal 41,99 Ha, luasan Terumbu Karang 125,57 Ha. Hasil NDVI di daratan/ pulau kecil Gelasa untuk Vegetasi Rapat seluas 47,62 Ha; luasan penutupan Vegetasi Sedang 105,86 Ha; dan penutupan Vegetasi Jarang adalah 34,24 Ha. Small Island Gelasa rarely studied. Mapping ecosystems on small islands with the image of Advanced Land Observing Satellite (ALOS). Previous research has found that ALOS has the ability to map coral reefs and seagrass beds in shallow water, and is able to map vegetation cover density. The method of image interpretation uses the vegetation index algorithm in the ALOS image, NDVI (Normalized Difference Vegetation Index), and the Lyzengga approach to correct the water column. The results of the study were obtained in the area of Seagrass Padang in the shallow waters of 41.99 ha, the area of coral reefs was 125.57 ha. NDVI results on land / small islands Gelasa for dense vegetation of 47.62 ha; area of Medium Vegetation coverage 105.86 Ha; and the coverage of Rare Vegetation is 34.24 Ha.

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