Risk factors and geospatial modelling for the presence of Fasciola hepatica infection in sheep and goat farms in the Greek temperate Mediterranean environment

Parasitology ◽  
2011 ◽  
Vol 138 (7) ◽  
pp. 926-938 ◽  
Author(s):  
V. KANTZOURA ◽  
M. K. KOUAM ◽  
N. DEMIRIS ◽  
H. FEIDAS ◽  
G. THEODOROPOULOS
Keyword(s):  
Risk Factors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fasciola Hepatica ◽  
Small Ruminants ◽  
Endemic Region ◽  
Pasture Management ◽  
Entire Area ◽  
Model Based ◽  
Geospatial Modelling

SUMMARYRisk factors related to herd and farmer status, farm and pasture management, and environmental factors derived by satellite data were examined for their association with the prevalence of F. hepatica in sheep and goat farms in Thessaly, Greece. Twelve farms (16·2%) and 58 farms (78·4%) of 74 had evidence of infection using coproantigen and serology respectively. The average normalized difference vegetation index (NDVI) of farm location for 12 months before sampling was the most significant environmental risk factor for F. hepatica infection based on high seropositivity. The risk of infection increased by 1% when the value of NDVI increased by 0·01 degree. A geospatial map was constructed to show the relative risk (RR) of Fasciola infection in sheep and goat farms in Thessaly. In addition, geospatial maps of the model-based predicted RR for the presence of Fasciola infection in farms in Thessaly and the entire area of Greece were constructed from the developed model based on NDVI. In conclusion, this study demonstrated that Thessaly should be regarded as an endemic region for Fasciola infection and it represents the first prediction model of Fasciola infection in small ruminants in the Mediterranean basin.

Prevalence and seasonal variation of Fasciola hepatica in slaughtered cattle: the role of climate and environmental factors in Mexico

2021 ◽  
Vol 95 ◽  
Author(s):  
K. Hernández-Guzmán ◽  
P. Molina-Mendoza ◽  
J. Olivares-Pérez ◽  
Y. Alcalá-Canto ◽  
A. Olmedo-Juárez ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fasciola Hepatica ◽  
Temperate Climate ◽  
Wet Season ◽  
Dry Land

Abstract The objective of this study is to determine the prevalence of Fasciola hepatica infection in cattle slaughterhouses, as well as its association with climatic/environmental factors (derived from satellite data), seasonality and climate regions in two states in Mexico. Condemned livers from slaughtered animals were obtained from three abattoirs in the states of Puebla and Veracruz. The overall prevalence of the parasite in cattle between January and December of 2017 was 20.6% (1407 out of 6834); the highest rate of condemnation was observed in Veracruz (26.3%; tropical climate), and the lowest rate was found in Puebla (15.5%; temperate climate). The seasonal prevalence of fluke infection was 18.6%, 14.8% and 28.4% during the wet season, and 17.1%, 12.4% and 22.8% during the dry season in the three abattoir sites, located in the districts of Zacatlán, Teziutlán and Ciudad Alemán, respectively. Liver condemnations due to bovine fasciolosis were prevalent in the Zacatlán, Teziutlán and Ciudad Alemán districts during summer, autumn and summer, respectively. Using generalized estimating equations analysis, we determined six variables – rainfall (wet/dry), land surface temperature day, land surface temperature night, normalized difference vegetation index, seasonality and climate regions (temperate/tropical) – to be significantly associated with the prevalence of condemned livers. Climate region was the variable most strongly associated with F. hepatica infection (odds ratio (OR) 266.59; 95% confidence interval (CI): 241.90–353.34), followed by wet and dry seasons (OR 25.56; 95% CI: 20.56–55.67).

Regional Characterization of Pasture Changes through Time and Space in Rondônia, Brazil

2007 ◽  
Vol 11 (14) ◽  
pp. 1-25 ◽  
Author(s):  
Izaya Numata ◽  
Dar A. Roberts ◽  
Yoshito Sawada ◽  
Oliver A. Chadwick ◽  
Joshua P. Schimel ◽  
...  
Keyword(s):  
Soil Fertility ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Regional Analysis ◽  
Series Data ◽  
Ancillary Data ◽  
Pasture Management ◽  
Climate Conditions ◽  
Pasture Degradation

Abstract Although pasture degradation has been a regional concern in Amazonian ecosystems, our ability to characterize and monitor pasture degradation under different environmental and human-related conditions is still limited. Regional analysis of pasture dynamic patterns was conducted using high-frequency temporal satellite data and ancillary data to better understand pasture degradation under varied soil, environmental, and pasture management conditions in the state of Rondônia, Brazil. The 10-day normalized difference vegetation index (NDVI) composite derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m resolution was used to characterize different grass phenological patterns for 32 counties in Rondônia between 2001 and 2003. Six pasture greenness classes showed that high greenness pasture classes dominated in young pastures, while low greenness pasture classes were least common. As pastures aged, the proportion of high greenness pasture classes decreased and the proportion of low greenness pastures increased, indicating a decrease in forage productivity over time in Rondônia. The magnitude of productivity decline depended on environmental constraints and land use systems. To refine this analysis, trajectories of pasture change were determined using spectral mixture analysis applied to Landsat time series data from 1988 to 2001 with the focus on two counties that show contrasting patterns of potential of grass production: Pimenteira, representing the “degraded” pasture category, and Governador Jorge Teixeira, as the “productive” pasture category. The results revealed a clear pasture degradation pattern in Pimenteira, related to low soil fertility and dry climate conditions, while Governador Jorge Teixeira, with better soil fertility and intermediate precipitation, did not show signs of pasture degradation through time.

scholarly journals Vegetation density analysis using normalized difference vegetation index in East Jakarta, Indonesia

2021 ◽  
Vol 912 (1) ◽  
pp. 012053
Author(s):  
A Zaitunah ◽  
Samsuri ◽  
Y M H Marbun ◽  
A Susilowati ◽  
D Elfiati ◽  
...  
Keyword(s):  
Land Use Planning ◽  
Open Space ◽  
Vegetation Index ◽  
Green Space ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Density ◽  
Entire Area ◽  
River Bank ◽  
Density Class ◽  
Dense Class

Abstract East Jakarta, which is included in the DKI Jakarta Province, continues to grow in population. As a result, the demand for settlement land increases. The presence of plants is critical for environmental equilibrium. The purpose of this study was to determine the vegetation density and its variations in East Jakarta year 2020. The method used the Normalized Difference Vegetation Index (NDVI) analysis and classification. In 2020, the highest NDVI value in East Jakarta was 0.1–0.2, covering 7,952.64 ha (43.07 %) of the entire area, while the lowest value was >0.6, covering 0.06 ha of the total area. The highest vegetation density class in East Jakarta was low dense class, accounting for 7,951.26 ha (43.06 percent) of the whole area, while the lowest density class was under high dense class accounted for 1,116.41 ha (6.04 percent) of the total area. In terms of green open space, there were a city park, a cemetery, a green lane on a road, and a river bank. The municipal park was classified as dense, while the rest were classified as medium dense. The presence of trees within the green space has aided in the area’s vegetation density. It also refers to the role of open green space in enhancing the community’s life and environment’s quality. The importance of educating and guiding the surrounding community about the benefits of vegetation or green open space, then replanting less vegetated land, as well as an integrated land use planning and implementation.The first section in your paper

scholarly journals MAPPING PASTURE AREAS IN WESTERN REGION OF SÃO PAULO STATE, BRAZIL

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 113-118
Author(s):  
A. F. C. Bonamigo ◽  
J. C. Oliveira ◽  
R. A. C. Lamparelli ◽  
G. K. D. A. Figueiredo ◽  
E. E. Campbell ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sao Paulo ◽  
Primary Study ◽  
Meat Production ◽  
Western Region ◽  
São Paulo ◽  
Pasture Management ◽  
Paulo State ◽  
São Paulo State

Abstract. Brazil is one of the largest exporters of cattle meat production. Most of this production is under pasture areas, with different levels of livestock and field management. Remotely sensed images could be interesting tools to detect distinct temporal and spatial patterns of these systems. In this context, classification algorithms have been proposed to use information from satellite images to map different land covers. The Time-Weighted Dynamic Time Warping (TWDTW) is an algorithm that has the advantage of working well with datasets with enough amounts of temporal information and seasonality patterns. In the present work, the TWDTW was performed to classify pasture managements in farms located in Western region of São Paulo State in Brazil for the years 2017 and 2018, as a primary study. It was used Normalized Difference Vegetation Index (NDVI) time series images from Moderate Resolution Imaging Spectroradiometer – MODIS sensor (products MOD13Q1 and MYD13Q) with 250 meters of spatial resolution. In classifications for the years 2017 and 2018, it was observed a predominance of traditional pasture. Total areas of degraded and traditional pasture were very similar between 2017 and 2018. The year of 2017 showed higher spatial distribution of intensified pastures than year 2018. The classification achieved satisfying results with complete accuracy in validation. The information collected from field visits were important to analyse general aspects of the results. Therefore, in this pilot study TWDTW algorithm demonstrated to have potential in differentiating classes of pasture management. Next steps will be to explore the possibilities to classify pasture systems in large areas.

scholarly journals Monitoring Rainfed Alfalfa Growth in Semiarid Agrosystems Using Sentinel-2 Imagery

2021 ◽  
Vol 13 (22) ◽  
pp. 4719
Author(s):  
Andrés Echeverría ◽  
Alejandro Urmeneta ◽  
María González-Audícana ◽  
Esther M González
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Weather Conditions ◽  
Early Summer ◽  
Pasture Management ◽  
Semiarid Areas ◽  
Potential Success ◽  
Highly Correlated ◽  
Sentinel 2

The aim of this study was to assess the utility of Sentinel-2 images in the monitoring of the fractional vegetation cover (FVC) of rainfed alfalfa in semiarid areas such as that of Bardenas Reales in Spain. FVC was sampled in situ using 1 m2 surfaces at 172 points inside 18 alfalfa fields from late spring to early summer in 2017 and 2018. Different vegetation indices derived from a series of Sentinel-2 images were calculated and were then correlated with the FVC measurements at the pixel and parcel levels using different types of equations. The results indicate that the normalized difference vegetation index (NDVI) and FVC were highly correlated at the parcel level (R2 = 0.712), whereas the correlation at the pixel level remained moderate across each of the years studied. Based on the findings, another 29 alfalfa plots (28 rainfed; 1 irrigated) were remotely monitored operationally for 3 years (2017–2019), revealing that location and weather conditions were strong determinants of alfalfa growth in Bardenas Reales. The results of this study indicate that Sentinel-2 imagery is a suitable tool for monitoring rainfed alfalfa pastures in semiarid areas, thus increasing the potential success of pasture management.

scholarly journals Effectiveness of a Complex Fertilisation Technology Applied to Zea mays, Assessed Based on Normalised Difference Vegetation Index (NDVI) from Terra Moderate Resolution Imaging Spectroradiometer (MODIS)

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 754
Author(s):  
Natalia Matłok ◽  
Oskar Basara ◽  
Miłosz Zardzewiały ◽  
Józef Gorzelany ◽  
Maciej Balawejder
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Physiological Measures ◽  
Entire Area ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Remote Sensing Methods

Assessment of effectiveness of fertilisation is a complex, multistage procedure. A few methods, used for this purpose, are based mainly on physiological measures acquired from a limited number of plants. Assessment of the process taking into account the entire area, in which the crop is grown, can be conducted using satellite remote sensing methods. The current study presents four fertilisation schemes applied to maize plants, including innovative foliar fertilizers and soil localized fertilization. Nutritional status and condition of the plants were assessed using Normalized Difference Vegetation Index (NDVI), and the results were analysed in relation to the grain yield. The findings show that the complex fertilisation technology applied to maize is most effective, producing grain yield which was 42.4% higher than the yield from the control variant.

Pemetaan Pulau Kecil Gelasa Kabupaten Bangka Tengah

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.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

ANALISIS SPEKTRAL CITRA SPOT 7 UNTUK IDENTIFIKASI KAWASAN MANGROVE DI PANTAI TIMUR KABUPATEN LAMPUNG SELATAN

2019 ◽  
Vol 3 ◽  
pp. 1213
Author(s):  
Nirmawana Simarmata ◽  
Fitralia Elyza ◽  
Rezalian Vatiady
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index

Konversi hutan manggrove merupakan sumber utama emisi CO dengan jumlah sebesar 1,7 ± 0,6 Pg karbon per tahun. Kegiatan konversi hutan mangrove menjadi lahan tambak melepaskan cadangan karbon ke atmosfir dalam jumlah yang cukup berarti. Ekspansi usaha pertambakan udang di kawasan pesisir Provinsi Lampung semakin meluas dari tahun ke tahun yang berdampak serius pada kondisi hutan mangrove. Kebijakan pembukaan tambak baru telah mengubah bentang hutan mangrove dan akan menimbulkan kerugian sosial yang jauh lebih besar. Menanggapi permasalahan tersebut, Indonesia menjadi salah satu negara yang mengikuti program Reduce Emission from Deforestation and Degradation atau REDD+ dalam melakukan inventarisasi karbon hutan. Indonesia memiliki potensi sumberdaya hutan mangrove yang sangat melimpah. Potensi hutan mangrove Indonesia cukup besar, Indonesia memiliki luas hutan mangrove terbesar di dunia. Salah satunya di Kabupaten Lampung Selatan merupakan kawasan dengan tutupan yang relatif luas di Provinsi Lampung. Karakteristik hutan mangrove dianalisis berdasarkan nilai spektral nya dengan menggunakan indeks vegetasi. Jenis data penginderaan jauh yang digunakan untuk penelitian ini adalah citra SPOT 7. Citra SPOT 7 dianalisis menggunakan Normalized Difference Vegetation Index (NDVI) sehingga diperoleh nilai kehijauan objek mangrove. Nilai indeks vegetasi pada kawasan penelitian mempunyai range antara 0.2 – 0.7. Nilai indeks vegetasi digunakan sebagai parameter untuk memetakan kawasan hutan mangrove di Kabupaten Lampung Selatan.

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