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

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.

scholarly journals Endangered species’ trait responses to environmental variability in agricultural settings

2020 ◽  
Vol 72 (1) ◽  
pp. 13-21
Author(s):  
Tijana Nikolic ◽  
Maja Arok ◽  
Dimitrije Radisic ◽  
Marko Mirc ◽  
Lea Velaja ◽  
...  
Keyword(s):  
Endangered Species ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Landscape ◽  
Linear Models ◽  
Environmental Variability ◽  
Abiotic Factors ◽  
Patch Occupancy ◽  
System Productivity ◽  
Demographic Traits

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.

scholarly journals Spatial Downscaling of GPM Annual and Monthly Precipitation Using Regression-Based Algorithms in a Mountainous Area

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Chesheng Zhan ◽  
Jian Han ◽  
Shi Hu ◽  
Liangmeizi Liu ◽  
Yuxuan Dong
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mountainous Area ◽  
Monthly Precipitation ◽  
Practical Applications ◽  
Precipitation Measurement ◽  
Digital Elevation ◽  
Elevation Model ◽  
Global Precipitation ◽  
Spatial Downscaling

As a fundamental component in material and energy circulation, precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. In this study, we investigated two downscaling algorithms based on the Multiple Linear Regression (MLR) and the Geographically Weighted Regression (GWR), respectively. They were employed to downscale annual and monthly precipitation obtained from the Global Precipitation Measurement (GPM) Mission in Hengduan Mountains, Southwestern China, from 10 km × 10 km to 1 km × 1 km. Ground observations were then used to validate the accuracy of downscaled precipitation. The results showed that (1) GWR performed much better than MLR to regress precipitation on Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM); (2) coefficients of GWR models showed strong spatial nonstationarity, but the spatial mean standardized coefficients were very similar to standardized coefficients of MLR in terms of intra-annual patterns: generally NDVI was positively related to precipitation when monthly precipitation was under 166 mm; DEM was negatively related to precipitation, especially in wet months like July and August; contribution of DEM to precipitation was greater than that of NDVI; (3) residuals’ correction was indispensable for the MLR-based algorithm but should be removed from the GWR-based algorithm; (4) the GWR-based algorithm rather than the MLR-based algorithm produced more accurate precipitation than original GPM precipitation. These results indicated that GWR is a promising method in satellite precipitation downscaling researches and needed to be further studied.

scholarly journals Identification of Drought and Performance Evaluation of MODIS and TRMM through Remote Sensing: A Case Study in North and West Africa during 2002–2018

2020 ◽  
Author(s):  
Malak Henchiri ◽  
Qi Liu ◽  
Bouajila Essifi ◽  
Shahzad Ali ◽  
Wilson Kalisa ◽  
...  
Keyword(s):  
Remote Sensing ◽  
West Africa ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Severity Index ◽  
Precipitation Anomaly ◽  
Drought Severity ◽  
Monthly Precipitation ◽  
Water Index ◽  
And Performance

North and West Africa are the most vulnerable regions to drought, due to the high variation in monthly precipitation. An accurate and efficient monitoring of drought is essential. In this study, we use TRMM data with remote sensing tools for effective monitoring of drought. The Drought Severity Index (DSI), Temperature Vegetation Drought Index (TVDI), Normalized Difference Vegetation Index (NDVI), and Normalized Vegetation Supply Water Index (NVSWI) are more useful for monitoring the drought over North and West Africa. To classify the areas affected by drought, we used the TRMM spatial maps to verify the TVDI, DSI and NVSWI indexes derived from MODIS. The DSI, TVDI, NVSWI and Monthly Precipitation Anomaly (NPA) indexes with the employ of MODIS-derived ET/PET and NDVI were chosen for monitoring the drought in the study area. The seasonal spatial correlation between the DSI, NPA, NVWSI, NDVI, TVDI and TCI indicates that NVSWI, NDVI and DSI present an excellent monitor of drought indexes. The change trend of drought from 2002 to 2018 was also characterized. The frequency of drought showed a decrease during this period.

scholarly journals Phenological behavior of Atlas cedar (Cedrus atlantica) forest to snow and precipitation variability in Boutaleb and Babors Mountains, Algeria

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Khaled Missaoui ◽  
Rachid Gharzouli ◽  
Yamna Djellouli ◽  
Frençois Messner
Keyword(s):  
Time Series ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Descriptive Analysis ◽  
Precipitation Variability ◽  
Monthly Precipitation ◽  
Cedrus Atlantica ◽  
The Past

Abstract. Missaoui K, Gharzouli R, Djellouli Y, Messner F. 2020. Phenological behavior of Atlas cedar (Cedrus atlantica)  forest to snow and precipitation variability in Boutaleb and Babors Mountains, Algeria. Biodiversitas 21: 239-245. Understanding the changes in snow and precipitation variability and how forest vegetation response to such changes is very important to maintain the long-term sustainability of the forest. However, relatively few studies have investigated this phenomenon in Algeria. This study was aimed to find out the response of Atlas cedar (Cedrus atlantica (Endl.) G.Manetti ex Carrière) forest in two areas (i.e Boutaleb and Babors Mountains) and their response to the precipitation and snow variability. The normalized difference vegetation index (NDVI) generated from satellite images of MODIS time series was used to survey the changes of the Atlas cedar throughout the study area well as dataset of monthly precipitation and snow of the province of Setif (northeast of Algeria) from 2000 to 2018. Descriptive analysis using Standarized Precipitation Index (SPI) showed the wetter years were more frequent in the past than in the last two decades. The NDVI values changes in both areas with high values were detected in Babors Mountains with statistically significant differences. Our findings showed important difference in Atlas cedar phenology from Boutaleb mountains to Babors Mountains which likely related to snow factor.

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

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.

scholarly journals Do Exposures to Green Space Reduce the Risk of Hypertension?

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.

scholarly journals Vegetation Resilience under Increasing Drought Conditions in Northern Tanzania

2021 ◽  
Vol 13 (22) ◽  
pp. 4592
Author(s):  
Steye L. Verhoeve ◽  
Tamara Keijzer ◽  
Rehema Kaitila ◽  
Juma Wickama ◽  
Geert Sterk
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Monthly Precipitation ◽  
Quick Recovery ◽  
Northern Tanzania ◽  
Derived Data ◽  
Drought Occurrence

East Africa is comprised of many semi-arid lands that are characterized by insufficient rainfall and the frequent occurrence of droughts. Drought, overgrazing and other impacts due to human activity may cause a decline in vegetation cover, which may result in land degradation. This study aimed to assess drought occurrence, vegetation cover changes and vegetation resilience in the Monduli and Longido districts in northern Tanzania. Satellite-derived data of rainfall, temperature and vegetation cover were used. Monthly precipitation (CenTrends v1.0 extended with CHIRPS2.0) and monthly mean temperatures (CRU TS4.03) were collected for the period of 1940–2020. Eight-day maximum value composite data of the normalized difference vegetation index (NDVI) (NOAA CDR—AVHRR) were obtained for the period of 1981–2020. Based on the meteorological data, trends in rainfall, temperature and drought were determined. The NDVI data were used to determine changes in vegetation cover and vegetation resilience related to the occurrence of drought. Rainfall did not significantly change over the period of 1940–2020, but mean monthly temperatures increased by 1.06 °C. The higher temperatures resulted in more frequent and prolonged droughts due to higher potential evapotranspiration rates. Vegetation cover declined by 9.7% between 1981 and 2020, which is lower than reported in several other studies, and most likely caused by the enhanced droughts. Vegetation resilience on the other hand is still high, meaning that a dry season or year resulted in lower vegetation cover, but a quick recovery was observed during the next normal or above-normal rainy season. It is concluded that despite the overall decline in vegetation cover, the changes have not been as dramatic as earlier reported, and that vegetation resilience is good in the study area. However, climate change predictions for the area suggest the occurrence of more droughts, which might lead to further vegetation cover decline and possibly a shift in vegetation species to more drought-prone species.

scholarly journals Assessment of the Biomass Productivity Decline in the Lower Mekong Basin

2019 ◽  
Vol 11 (23) ◽  
pp. 2796
Author(s):  
Quyet Manh Vu ◽  
Venkat Lakshmi ◽  
John Bolten
Keyword(s):  
Time Series ◽  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Productivity ◽  
Anthropogenic Factors ◽  
Rainfall Time Series ◽  
Mekong Basin ◽  
Lower Mekong Basin ◽  
Productivity Decline

This study aimed to delineate the geographic hotspots of negative trends in biomass productivity in the Lower Mekong Basin countries (Vietnam, Cambodia, Laos, and Thailand) and identify correlated regional environmental and anthropogenic factors. A long-term time-series (1982–2015) of Normalized Difference Vegetation Index at a resolution of approximately 9.16 km × 9.16 km was used to specify the areas with significant decline or increase in productivity. The relationships between vegetation changes and land attributes, such as climate, population density, soil/terrain conditions, and land-cover types, were examined. Rainfall time-series maps were used to identify areas that might have been affected by land degradation from those correlated with rainfall. Most of the detected potentially degraded areas were found in Cambodia, the Northwest and the Highland of Vietnam, the Northern Mountains of Thailand and Laos, and the mountainous border between Laos, Vietnam, and Cambodia. About 15% of the total land area of these four countries experienced a reduction in biomass productivity during the 34-year study period. The map of hotspots of changes in productivity can be used to direct further studies, including those at finer spatial resolution that may support policy makers and researchers in targeting the strategies for combating land degradation.

scholarly journals Protocolo Metodológico para Obtenção dos Valores de Reflectância e de NDVI de Imagens Landsat 8/OLI Utilizando LEGAL

2021 ◽  
Vol 14 (2) ◽  
pp. 869
Author(s):  
João Pedro Ocanha Krizek ◽  
Luciana Cavalcanti Maia Santos
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Environmental Parameters ◽  
Biomass Productivity ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Remote Sensing Images ◽  
De Se ◽  
Correct Estimation

A obtenção dos valores de reflectância se mostra imprescindível para se calcular índices de vegetação, como o NDVI (Normalized Difference Vegetation Index). Este índice é utilizado para classificar a distribuição global da vegetação e para inferir variáveis ecológicas e ambientais, como a produção de fitomassa.  Apesar disso, não é incomum encontrar trabalhos que utilizam os números digitais (ND) para a obtenção direta dos índices de vegetação; entretanto, tais números digitais não representam valores físicos reais e, portanto, não podem ser utilizados diretamente para o cálculo do NDVI. Assim, o objetivo deste artigo é demonstrar um protocolo metodológico para a conversão dos ND das imagens Landsat 8/OLI em valores de reflectância e a subsequente obtenção do NDVI, através da linguagem LEGAL (Linguagem Espacial para Geoprocessamento Algébrico), e, dessa forma, possibilitar a replicação e execução de outras pesquisas que visem obter esse índice de vegetação no software SPRING. Além disso, objetivou-se também demonstrar a importância da conversão dos ND em reflectância, a partir da comparação de uma imagem NDVI gerada através da reflectância com a mesma imagem NDVI gerada por meio dos dados brutos. Os resultados apontaram que a obtenção do NDVI através dos valores brutos de imagens de sensoriamento remoto, sem a necessária conversão dos números digitais em valores reais de reflectância, leva a resultados incorretos na estimativa de dados ecológicos da vegetação, subestimando a fitomassa. Dessa forma, esse trabalho ressalta a importância de se seguir um protocolo metodológico para a estimativa correta da fitomassa, produtividade e outros parâmetros da vegetação.   Methodological protocol for obtaining reflectance and NDVI values from Landsat 8/OLI images using LEGALA B S T R A C TObtaining reflectance values is essential for calculating vegetation indices, such as the NDVI (Normalized Difference Vegetation Index). This index is used to classify the global distribution of vegetation and to infer the ecological and environmental parameters such as phytomass production. Nevertheless, it is common to find works that use digital numbers (DN) to directly obtain vegetation indices; however, such digital numbers do not represent actual physical values and therefore cannot be used directly for NDVI calculation. Thus, this paper aims to demonstrate a methodological protocol for DN conversion of Landsat 8/OLI images into reflectance values and then for obtaining NDVI through the LEGAL (Spatial Language for Algebraic Geoprocessing). Therefore, this protocol enables the replication and execution of other studies aimed to obtain this vegetation index using SPRING. In addition, the objective was also to demonstrate the importance of converting DN to reflectance by comparing an NDVI image generated from reflectance with the same NDVI image generated through the raw data. The results showed that obtaining the NDVI through the raw values of remote sensing images, without the conversion of digital numbers to real reflectance values, leads to incorrect results in the estimation of ecological vegetation data, underestimating phytomass, thus emphasizing the importance of following a methodological protocol for the correct estimation of biomass, productivity and other phytological parameters.Keywords: protocol, NDVI, reflectance, Landsat 8, SPRING

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

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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