scholarly journals Latrine site selection of raccoon dogs in a hilly area in north-eastern Japan

2021 ◽  
pp. 79-85
Author(s):  
Kazuma Watanabe ◽  
Nami Kumagai ◽  
Masayuki U. Saito
Keyword(s):  
Vegetation Index ◽  
Vegetation Type ◽  
Slope Angle ◽  
Normalised Difference Vegetation Index ◽  
North Eastern ◽  
Position Index ◽  
Latrine Site ◽  
The Relationship ◽  
Eastern Japan ◽  
Raccoon Dogs

We evaluated the environment types of raccoon dog latrine sites in the hilly areas of north-eastern Japan. We conducted a route census in the spring and autumn of 2020 to record the latrine sites and analysed the relationship between the presence or absence of latrine sites and environmental factors, namely, topographic position index (TPI), slope, normalised difference vegetation index (NDVI), and vegetation type for each season. To investigate the space use of raccoon dogs, we also conducted camera trapping from July to November 2020 along the spring survey route. We analysed the relationship between the occurrence frequency of raccoon dogs and TPI, slope angle, NDVI, and vegetation type. The analysis showed that latrine sites tended to be located at sites with a high TPI (topography closer to the ridge) in both seasons. However, the occurrence of latrine sites in broadleaf forests was significantly higher in autumn. The frequency of raccoon dogs, based on camera-trap footage, was significantly higher at sites with gentle slopes; although the environment and space used by raccoon dogs at these sites differed. Raccoon dogs possibly select visually and olfactorily conspicuous sites on the ridge as latrine sites to facilitate odour dispersal. In addition, broadleaf forests in autumn are considered important feeding grounds for raccoon dogs, suggesting that the latrine sites were formed near foraging sites.

scholarly journals Predicting Soil Respiration from Plant Productivity (NDVI) in a Sub-Arctic Tundra Ecosystem

2021 ◽  
Vol 13 (13) ◽  
pp. 2571
Author(s):  
Olivia Azevedo ◽  
Thomas C. Parker ◽  
Matthias B. Siewert ◽  
Jens-Arne Subke
Keyword(s):  
Soil Respiration ◽  
Vegetation Index ◽  
Vegetation Type ◽  
Net Ecosystem Exchange ◽  
Plant Productivity ◽  
Ecosystem Monitoring ◽  
Vegetation Communities ◽  
Direct Measurements ◽  
Tundra Ecosystem ◽  
Normalised Difference Vegetation Index

Soils represent the largest store of carbon in the biosphere with soils at high latitudes containing twice as much carbon (C) than the atmosphere. High latitude tundra vegetation communities show increases in the relative abundance and cover of deciduous shrubs which may influence net ecosystem exchange of CO2 from this C-rich ecosystem. Monitoring soil respiration (Rs) as a crucial component of the ecosystem carbon balance at regional scales is difficult given the remoteness of these ecosystems and the intensiveness of measurements that is required. Here we use direct measurements of Rs from contrasting tundra plant communities combined with direct measurements of aboveground plant productivity via Normalised Difference Vegetation Index (NDVI) to predict soil respiration across four key vegetation communities in a tundra ecosystem. Soil respiration exhibited a nonlinear relationship with NDVI (y = 0.202e3.508 x, p < 0.001). Our results further suggest that NDVI and soil temperature can help predict Rs if vegetation type is taken into consideration. We observed, however, that NDVI is not a relevant explanatory variable in the estimation of SOC in a single-study analysis.

scholarly journals Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

2020 ◽  
Vol 39 (3) ◽  
pp. 87-109 ◽  
Author(s):  
Alfred S. Alademomi ◽  
Chukwuma J. Okolie ◽  
Olagoke E. Daramola ◽  
Raphael O. Agboola ◽  
Tosin J. Salami
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Urban Expansion ◽  
Normalised Difference Vegetation Index ◽  
Lagos Lagoon ◽  
The Mean ◽  
Lagoon Environment ◽  
The Relationship ◽  
Derived Data

AbstractThe Lagos Lagoon is under increased pressure from growth in human population, growing demands for natural resources, human activities, and socioeconomic factors. The degree of these activities and the impacts are directly proportional to urban expansion and growth. In the light of this situation, the objectives of this study were: (i) to estimate through satellite imagery analysis the extent of changes in the Lagos Lagoon environment for the periods 1984, 2002, 2013 and 2019 using Landsat-derived data on land cover, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI); and (ii) to evaluate the relationship between the derived data and determine their relative influence on the lagoon environment. The derived data were subjected to descriptive statistics, and relationships were explored using Pearson's correlation and regression analysis. The effect of land cover on LST was measured using the Contribution Index and a trend analysis was carried out. From the results, the mean LSTs for the four years were 22.68°C (1984), 24.34°C (2002), 26.46°C (2013) and 28.40°C (2019). Generally, the mean LSTs is in opposite trend with the mean NDVIs and EVIs as associated with their dominant land cover type. The strongest positive correlations were observed between NDVI and EVI while NDVI had the closest fit with LST in the regression. Built-up areas have the highest contributions to LST while vegetation had a cooling influence. The depletion in vegetative cover has compromised the biodiversity of this environment and efforts are required to reverse this trend.

A novel protocol for assessment of aboveground biomass in rangeland environments

2015 ◽  
Vol 37 (2) ◽  
pp. 157 ◽  
Author(s):  
Charity Mundava ◽  
Antonius G. T. Schut ◽  
Petra Helmholz ◽  
Richard Stovold ◽  
Graham Donald ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Vegetation Index ◽  
Growth Models ◽  
Vegetation Type ◽  
Perennial Grasses ◽  
Heterogeneous Environments ◽  
Growing Seasons ◽  
Normalised Difference Vegetation Index ◽  
Bunch Grass ◽  
Non Destructive

Current methods to measure aboveground biomass (AGB) do not deliver adequate results in relation to the extent and spatial variability that characterise rangelands. An optimised protocol for the assessment of AGB is presented that enables calibration and validation of remote-sensing imagery or plant growth models at suitable scales. The protocol combines a limited number of destructive samples with non-destructive measurements including normalised difference vegetation index (NDVI), canopy height and visual scores of AGB. A total of 19 sites were sampled four times during two growing seasons. Fresh and dry matter weights of dead and green components of AGB were recorded. Similarity of responses allowed grouping into Open plains sites dominated by annual grasses, Bunch grass sites dominated by perennial grasses and Spinifex (Triodia spp.) sites. Relationships between non-destructive measurements and AGB were evaluated with a simple linear regression per vegetation type. Multiple regression models were first used to identify outliers and then cross-validated using a ‘Leave-One-Out’ and ‘Leave-Site-Out’ (LSO) approach on datasets including and excluding the identified outliers. Combining all non-destructive measurements into one single regression model per vegetation type provided strong relationships for all seasons for total and green AGB (adjusted R2 values of 0.65–0.90) for datasets excluding outliers. The model provided accurate assessments of total AGB in heterogeneous environments for Bunch grass and Spinifex sites (LSO-Q2 values of 0.70–0.88), whereas assessment of green AGB was accurate for all vegetation types (LSO-Q2 values of 0.62–0.84). The protocol described can be applied at a range of scales while considerably reducing sampling time.

scholarly journals Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang

2022 ◽  
Vol 14 (1) ◽  
pp. 582
Author(s):  
Shengxin Lan ◽  
Zuoji Dong
Keyword(s):  
Time Series ◽  
Vegetation Dynamics ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Vegetation Type ◽  
Sparse Vegetation ◽  
Type Transformation ◽  
Ndvi Time Series ◽  
The Mean ◽  
The Relationship

Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused on the relationship between vegetation type and NDVI changes. We investigated changes in vegetation in Xinjiang using linear regression of time-series MOD13Q1 NDVI data from 2001 to 2020. MCD12Q1 vegetation type data from 2001 to 2019 were used to analyze transformations among different vegetation types, and the relationship between the transformation of vegetation type and NDVI was analyzed. Approximately 63.29% of the vegetation showed no significant changes. In the vegetation-changed area, approximately 93.88% and 6.12% of the vegetation showed a significant increase and decrease in NDVI, respectively. Approximately 43,382.82 km2 of sparse vegetation and 25,915.44 km2 of grassland were transformed into grassland and cropland, respectively. Moreover, 17.4% of the area with transformed vegetation showed a significant increase in NDVI, whereas 14.61% showed a decrease in NDVI. Furthermore, in areas with NDVI increased, the mean NDVI slopes of pixels in which sparse vegetation transferred to cropland, sparse vegetation transferred to grassland, and grassland transferred to cropland were 9.8 and 3.2 times that of sparse vegetation, and 1.97 times that of grassland, respectively. In areas with decreased NDVI, the mean NDVI slopes of pixels in which cropland transferred to sparse vegetation, grassland transferred to sparse vegetation were 1.75 and 1.36 times that of sparse vegetation, respectively. The combination of vegetation type transformation NDVI time-series can assist in comprehensively understanding the vegetation change characteristics.

scholarly journals A Long-Term Spatiotemporal Analysis of Vegetation Greenness over the Himalayan Region Using Google Earth Engine

Climate ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 109
Author(s):  
Nikul Kumari ◽  
Ankur Srivastava ◽  
Umesh Chandra Dumka
Keyword(s):  
Vegetation Index ◽  
Google Earth ◽  
Himalayan Region ◽  
Spatiotemporal Variability ◽  
Series Data ◽  
Vegetation Greenness ◽  
Normalised Difference Vegetation Index ◽  
Google Earth Engine ◽  
Hydroclimatic Factors ◽  
The Relationship

The Himalayas constitute one of the richest and most diverse ecosystems in the Indian sub-continent. Vegetation greenness driven by climate in the Himalayan region is often overlooked as field-based studies are challenging due to high altitude and complex topography. Although the basic information about vegetation cover and its interactions with different hydroclimatic factors is vital, limited attention has been given to understanding the response of vegetation to different climatic factors. The main aim of the present study is to analyse the relationship between the spatiotemporal variability of vegetation greenness and associated climatic and hydrological drivers within the Upper Khoh River (UKR) Basin of the Himalayas at annual and seasonal scales. We analysed two vegetation indices, namely, normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI) time-series data, for the last 20 years (2001–2020) using Google Earth Engine. We found that both the NDVI and EVI showed increasing trends in the vegetation greening during the period under consideration, with the NDVI being consistently higher than the EVI. The mean NDVI and EVI increased from 0.54 and 0.31 (2001), respectively, to 0.65 and 0.36 (2020). Further, the EVI tends to correlate better with the different hydroclimatic factors in comparison to the NDVI. The EVI is strongly correlated with ET with r2 = 0.73 whereas the NDVI showed satisfactory performance with r2 = 0.45. On the other hand, the relationship between the EVI and precipitation yielded r2 = 0.34, whereas there was no relationship was observed between the NDVI and precipitation. These findings show that there exists a strong correlation between the EVI and hydroclimatic factors, which shows that changes in vegetation phenology can be better captured using the EVI than the NDVI.

Remote sensing of fire severity in the Blue Mountains: influence of vegetation type and inferring fire intensity

2006 ◽  
Vol 15 (2) ◽  
pp. 213 ◽  
Author(s):  
Kate A. Hammill ◽  
Ross A. Bradstock
Keyword(s):  
Vegetation Index ◽  
Forest Cover ◽  
Vegetation Type ◽  
Fire Severity ◽  
Image Resolution ◽  
Fire Intensity ◽  
Vegetation Types ◽  
Open Forest ◽  
Normalised Difference Vegetation Index ◽  
Severity Class

Fire intensity affects ecological and geophysical processes in fire-prone landscapes. We examined the potential for satellite imagery (Satellite Pour l’Observation de la Terre [SPOT2] and Landsat7) to detect and map fire severity patterns in a rugged landscape with variable vegetation near Sydney, Australia. A post-fire, vegetation-based indicator of fire intensity (burnt shrub branch tip diameters, representing the size of fuel consumed) was also used to explore whether fire severity patterns can be used to retrospectively infer patterns of fire intensity. Six severity classes (ranging from unburnt to complete crown consumption) were defined using aerial photograph interpretation and a field assessment across five vegetation types of varying height and complexity (sedge-swamp, heath, woodland, open forest, and tall forest). Using established Normalised Difference Vegetation Index (NDVI) differencing methodology, SPOT2 and Landsat7 imagery yielded similar broad-scale severity patterns across the study area. This was despite differences in image resolution (10 m and 30 m, respectively) and capture dates (2 months and 9 months apart, respectively). However, differences in the total areas mapped for some severity classes were found. In particular, there was reduced differentiation between unburnt and low-severity areas and between crown-scorched and crown-consumed areas when using the Landsat7 data. These differences were caused by fine-scale classification anomalies and were most likely associated with seasonal differences in vegetation condition (associated with time of image capture), post-fire movement of ash, resprouting of vegetation, and low sun elevation. Relationships between field severity class and NDVIdifference values revealed that vegetation type does influence the detection of fire severity using these types of satellite data: regression slopes were greater for woodland, forest, and tall forest data than for sedge-swamp and heath data. The effect of vegetation type on areas mapped in each fire severity class was examined but found to be minimal in the present study due to the uneven distribution of vegetation types in the study area (woodland and open forest cover 86% of the landscape). Field observations of burnt shrub branch tips, which were used as a surrogate for fire intensity, revealed that relationships between fire severity and fire intensity are confounded by vegetation type (mainly height). A method for inferring fire intensity from remotely sensed patterns of fire severity was proposed in which patterns of fire severity and vegetation type are combined.

scholarly journals Photogrammetry for assessment of pasture biomass

2019 ◽  
pp. 33-40 ◽  
Author(s):  
Kathryn Wigley ◽  
Jennifer L. Owens ◽  
Matthias Westerschulte ◽  
Paul Riding ◽  
Jaco Fourie ◽  
...  
Keyword(s):  
Linear Relationship ◽  
Plant Height ◽  
Vegetation Index ◽  
Digital Camera ◽  
Proof Of Concept ◽  
Destructive Sampling ◽  
Normalised Difference Vegetation Index ◽  
Surface Models ◽  
The Relationship ◽  
Strong Linear Relationship

New tools are required to provide estimates of pasture biomass as current methods are time consuming and labour intensive. This proof-of-concept study tested the suitability of photogrammetry to estimate pasture height in a grazed dairy pasture. Images were obtained using a digital camera from one site on two separate occasions (May and June 2017). Photogrammetry-derived pasture height was estimated from digital surface models created using the photos. Pasture indices were also measured using two currently available methods: a Rising Plate Meter (RPM), and Normalised Difference Vegetation Index (NDVI). Empirical pasture biomass measurements were taken using destructive sampling after all other measurements were made, and were used to evaluate the accuracy of the estimates from each method. There was a strong linear relationship between photogrammetry-derived plant height and actual biomass (R2=0.92May and 0.78June) and between RPM and actual biomass (R2=0.91May and 0.78June). The relationship between NDVI and actual biomass was relatively weaker (R2=0.65May and 0.66June). Photogrammetry could be an efficient way to measure pasture biomass with an accuracy comparable to that of the RPM but further work is required to confirm these preliminary findings.

Ecological response of Eucalyptus camaldulensis (river red gum) to extended drought and flooding along the River Murray, South Australia (1997–2011) and implications for environmental flow management

2014 ◽  
Vol 65 (12) ◽  
pp. 1082 ◽  
Author(s):  
Tanya M. Doody ◽  
Simon N. Benger ◽  
Jodie L. Pritchard ◽  
Ian C. Overton
Keyword(s):  
Vegetation Index ◽  
Environmental Flows ◽  
Riparian Forest ◽  
South Australia ◽  
Eucalyptus Camaldulensis ◽  
Millennium Drought ◽  
Normalised Difference Vegetation Index ◽  
Floodplain Inundation ◽  
Flood Pulses ◽  
The Relationship

Riparian forest and woodlands of the lower River Murray floodplain are exhibiting deteriorating health as a result of anthropogenic alterations to flow regimes and south-eastern Australia’s long-term ‘Millennium Drought’ from 1997 to 2009. Extensive flooding in 2010/2011 brought the drought to an end, providing an opportunity to monitor ecological floodplain recovery. The relationship between flooding and lateral recharge and condition of the dominant riparian tree species, Eucalyptus camaldulensis, was determined between 2007 and 2011 using the Landsat (LTM5) Normalised Difference Vegetation Index (NDVI). Linking the river hydrograph with the River Murray Floodplain Inundation Model (RiM-FIM) allowed exploration of the relationship between inundation duration and E. camaldulensis water requirements. Results indicate lateral bank recharge is an important mechanism in the maintenance of vegetation condition along the River Murray channel. Higher in-channel irrigation water delivery during summer months was identified as critical to survival of trees adjacent to the channel during the drought. The research suggests that weir pool manipulation to create in-channel flood pulses will aid E. camaldulensis maintenance. Furthermore, release of environmental flows once every 3 to 5 years to create bank-full flow or preferably overbank flows, will increase hydrological connectivity between river banks, wetlands and riparian zones, providing positive ecological benefits to E. camaldulensis and other floodplain and aquatic ecological assets.

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.

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