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.

Assessing the impact of seasonal precipitation and temperature on vegetation in a grass-dominated rangeland

2014 ◽  
Vol 36 (2) ◽  
pp. 185 ◽  
Author(s):  
Fang Chen ◽  
Keith T. Weber
Keyword(s):  
Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Vegetation Growth ◽  
Factors Affecting ◽  
Growing Seasons ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact ◽  
Precipitation And Temperature

Changes in vegetation are affected by many climatic factors and have been successfully monitored through satellite remote sensing over the past 20 years. In this study, the Normalised Difference Vegetation Index (NDVI), derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite, was selected as an indicator of change in vegetation. Monthly MODIS composite NDVI at a 1-km resolution was acquired throughout the 2004–09 growing seasons (i.e. April–September). Data describing daily precipitation and temperature, primary factors affecting vegetation growth in the semiarid rangelands of Idaho, were derived from the Surface Observation Gridding System and local weather station datasets. Inter-annual and seasonal fluctuations of precipitation and temperature were analysed and temporal relationships between monthly NDVI, precipitation and temperature were examined. Results indicated NDVI values observed in June and July were strongly correlated with accumulated precipitation (R2 >0.75), while NDVI values observed early in the growing season (May) as well as late in the growing season (August and September) were only moderately related with accumulated precipitation (R2 ≥0.45). The role of ambient temperature was also apparent, especially early in the growing season. Specifically, early growing-season temperatures appeared to significantly affect plant phenology and, consequently, correlations between NDVI and accumulated precipitation. It is concluded that precipitation during the growing season is a better predictor of NDVI than temperature but is interrelated with influences of temperature in parts of the growing season.

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 Assessment of pine aboveground biomass within Northern Steppe of Ukraine using Sentinel-2 data

2020 ◽  
Vol 66 (No. 8) ◽  
pp. 339-348
Author(s):  
Viktoriia Lovynska ◽  
Yuriy Buchavyi ◽  
Petro Lakyda ◽  
Svitlana Sytnyk ◽  
Yuriy Gritzan ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Free Access ◽  
Biophysical Parameters ◽  
Normalised Difference Vegetation Index ◽  
Pine Stands ◽  
Predicted Values ◽  
Vegetative Indices ◽  
Sentinel 2

The present study offers the results of the spectral characteristics, calculated vegetative indices and biophysical parameters of pine stands of the Northern Steppe of Ukraine region obtained using Sentinel-2 data. For the development of regression models with the prediction of the biomass of pine forests using the obtained spectral characteristics, we used the results of the assessment of the aboveground biomass by the method of field surveys. The results revealed the highest correlation relations between the parameters of the general and trunk biomass with the normalised difference vegetation index (NDVI) and transformed vegetation index (TVI) vegetative indices and the fraction of absorbed photosynthetic active radiation (FARAP) and fraction of vegetation cover (FCOVER) biophysical parameters. To generate the models of determining the forest aboveground biomass (AGB), we used both the single- and two-factor models, the most optimum of which were those containing the NDVI predictor separately and in combination with the FCOVER predictor. The predicted values of the total AGB for the mentioned models equalled 32.5 to 236.3 and 39.9 to 253.4 t·ha<sup>–1</sup>. We performed mapping of the AGB of pine stands of the Northern Steppe using multi-spectral Sentinel-2 images, particularly the spectral characteristics of their derivatives (vegetative indices, biophysical parameters). This study demonstrated promising results for conducting an AGB-mapping of pine woods in the studied region using free-access resources.

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.

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 Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2755
Author(s):  
Peng Fang ◽  
Nana Yan ◽  
Panpan Wei ◽  
Yifan Zhao ◽  
Xiwang Zhang
Keyword(s):  
Aboveground Biomass ◽  
Production Management ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Edge Information ◽  
Casa Model ◽  
Red Edge ◽  
Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

Spectral assessments of wheat plants grown in pots and containers under saline conditions

2013 ◽  
Vol 40 (4) ◽  
pp. 409 ◽  
Author(s):  
Harald Hackl ◽  
Bodo Mistele ◽  
Yuncai Hu ◽  
Urs Schmidhalter
Keyword(s):  
Water Potential ◽  
Water Content ◽  
Aboveground Biomass ◽  
Leaf Water ◽  
Leaf Water Content ◽  
Spectral Indices ◽  
Fresh Weight ◽  
Normalised Difference Vegetation Index ◽  
Spectral Sensing ◽  
Reflectance Ratio

Spectral measurements allow fast nondestructive assessment of plant traits under controlled greenhouse and close-to-field conditions. Field crop stands differ from pot-grown plants, which may affect the ability to assess stress-related traits by nondestructive high-throughput measurements. This study analysed the potential to detect salt stress-related traits of spring wheat (Triticum aestivum L.) cultivars grown in pots or in a close-to-field container platform. In two experiments, selected spectral indices assessed by active and passive spectral sensing were related to the fresh weight of the aboveground biomass, the water content of the aboveground biomass, the leaf water potential and the relative leaf water content of two cultivars with different salt tolerance. The traits were better ascertained by spectral sensing of container-grown plants compared with pot-grown plants. This may be due to a decreased match between the sensors’ footprint and the plant area of the pot-grown plants, which was further characterised by enhanced senescence of lower leaves. The reflectance ratio R760 : R670, the normalised difference vegetation index and the reflectance ratio R780 : R550 spectral indices were the best indices and were significantly related to the fresh weight, the water content of the aboveground biomass and the water potential of the youngest fully developed leaf. Passive sensors delivered similar relationships to active sensors. Across all treatments, both cultivars were successfully differentiated using either destructively or nondestructively assessed parameters. Although spectral sensors provide fast and qualitatively good assessments of the traits of salt-stressed plants, further research is required to describe the potential and limitations of spectral sensing.

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