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 Soil bulk electrical resistivity and forage ground cover: nonlinear models in an alfalfa (Medicago sativa L.) case study

2015 ◽  
Vol 10 (4) ◽  
pp. 215 ◽  
Author(s):  
Roberta Rossi ◽  
Alessio Pollice ◽  
Gianfranco Bitella ◽  
Rocco Bochicchio ◽  
Amedeo D'Antonio ◽  
...  
Keyword(s):  
Electrical Resistivity ◽  
Medicago Sativa ◽  
Vegetation Index ◽  
Nonlinear Models ◽  
Ground Cover ◽  
Plant Productivity ◽  
Additive Models ◽  
Deep Soil ◽  
Soil Features ◽  
Normalised Difference Vegetation Index

Alfalfa is a highly productive and fertility-building forage crop; its performance, can be highly variable as influenced by within-field soil spatial variability. Characterising the relations between soil and forage- variation is important for optimal management. The aim of this work was to model the relationship between soil electrical resistivity (ER) and plant productivity in an alfalfa (<em>Medicago sativa</em> L.) field in Southern Italy. ER mapping was accomplished by a multi-depth automatic resistivity profiler. Plant productivity was assessed through normalised difference vegetation index (NDVI) at 2 dates. A non-linear relationship between NDVI and deep soil ER was modelled within the framework of generalised additive models. The best model explained 70% of the total variability. Soil profiles at six locations selected along a gradient of ER showed differences related to texture (ranging from clay to sandy-clay loam), gravel content (0 to 55%) and to the presence of a petrocalcic horizon. Our results prove that multi-depth ER can be used to localise permanent soil features that drive plant productivity.

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 Drone data reveal heterogeneity in tundra greenness and phenology not captured by satellites

2020 ◽  
Author(s):  
Jakob Johann Assmann ◽  
Isla Heather Myers-Smith ◽  
Jeff Kerby ◽  
Andrew M. Cunliffe ◽  
Gergana N. Daskalova
Keyword(s):  
Vegetation Index ◽  
Leaf Growth ◽  
Spatial Scales ◽  
Yukon Territory ◽  
Ecological Impacts ◽  
The Arctic ◽  
Landsat 8 ◽  
Novel Technologies ◽  
Tundra Ecosystem ◽  
Normalised Difference Vegetation Index

Data across scales are required to monitor ecosystem responses to rapid warming in the Arctic and to interpret tundra greening trends. Here, we tested the correspondence among satellite- and drone-derived seasonal change in tundra greenness to identify optimal spatial scales for vegetation monitoring on Qikiqtaruk - Herschel Island in the Yukon Territory, Canada. We combined time-series of the Normalised Difference Vegetation Index (NDVI) from multispectral drone imagery and satellite data (Sentinel-2, Landsat 8 and MODIS) with ground-based observations for two growing seasons (2016 and 2017). We found high cross-season correspondence in plot mean greenness (drone-satellite Spearman’s ⍴ 0.67-0.87) and pixel-by-pixel greenness (drone-satellite R2 0.58-0.69) for eight one-hectare plots, with drones capturing lower NDVI values relative to the satellites. We identified a plateau in the spatial variation of tundra greenness at distances of around half a metre in the plots, suggesting that these grain sizes are optimal for monitoring such variation in the two most common vegetation types on the island. We further observed a notable loss of seasonal variation in the spatial heterogeneity of landscape greenness (46.2 - 63.9%) when aggregating from ultra-fine-grain drone pixels (approx. 0.05 m) to the size of medium-grain satellite pixels (10 – 30 m). Finally, seasonal changes in drone-derived greenness were highly correlated with measurements of leaf-growth in the ground-validation plots (mean Spearman’s ⍴ 0.70). These findings indicate that multispectral drone measurements can capture temporal plant growth dynamics across tundra landscapes. Overall, our results demonstrate that novel technologies such as drone platforms and compact multispectral sensors allow us to study ecological systems at previously inaccessible scales and fill gaps in our understanding of tundra ecosystem processes. Capturing fine-scale variation across tundra landscapes will improve predictions of the ecological impacts and climate feedbacks of environmental change in the Arctic.

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.

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 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.

Assessment of changes in the state of the rangelands of Inner Mongolia, China between 1998 and 2007 using remotely sensed data

2012 ◽  
Vol 34 (1) ◽  
pp. 103 ◽  
Author(s):  
Z. M. Hu ◽  
S. G. Li ◽  
J. W. Dong ◽  
J. W. Fan
Keyword(s):  
Inner Mongolia ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Temporal Dynamics ◽  
Remotely Sensed ◽  
The State ◽  
Mean Annual Precipitation ◽  
Aboveground Net Primary Productivity ◽  
Remotely Sensed Data ◽  
Normalised Difference Vegetation Index

The spatial annual patterns of aboveground net primary productivity (ANPP) and precipitation-use efficiency (PUE) of the rangelands of the Inner Mongolia Autonomous Region of China, a region in which several projects for ecosystem restoration had been implemented, are described for the years 1998–2007. Remotely sensed normalised difference vegetation index and ANPP data, measured in situ, were integrated to allow the prediction of ANPP and PUE in each 1 km2 of the 12 prefectures of Inner Mongolia. Furthermore, the temporal dynamics of PUE and ANPP residuals, as indicators of ecosystem deterioration and recovery, were investigated for the region and each prefecture. In general, both ANPP and PUE were positively correlated with mean annual precipitation, i.e. ANPP and PUE were higher in wet regions than in arid regions. Both PUE and ANPP residuals indicated that the state of the rangelands of the region were generally improving during the period of 2000–05, but declined by 2007 to that found in 1999. Among the four main grassland-dominated prefectures, the recovery in the state of the grasslands in the Erdos and Chifeng prefectures was highest, and Xilin Gol and Chifeng prefectures was 2 years earlier than Erdos and Hunlu Buir prefectures. The study demonstrated that the use of PUE or ANPP residuals has some limitations and it is proposed that both indices should be used together with relatively long-term datasets in order to maximise the reliability of the assessments.

scholarly journals Improvement of Soil Respiration Parameterization in a Dynamic Global Vegetation Model and Its Impact on the Simulation of Terrestrial Carbon Fluxes

2018 ◽  
Vol 32 (1) ◽  
pp. 127-143 ◽  
Author(s):  
Dongmin Kim ◽  
Myong-In Lee ◽  
Eunkyo Seo
Keyword(s):  
Spatial Distribution ◽  
Soil Respiration ◽  
Soil Temperature ◽  
Vegetation Type ◽  
Carbon Fluxes ◽  
Decomposition Process ◽  
Spatial And Temporal Variation ◽  
In Situ Observation ◽  
Terrestrial Carbon ◽  
Soil Temperature And Moisture

Abstract The Q10 value represents the soil respiration sensitivity to temperature often used for the parameterization of the soil decomposition process has been assumed to be a constant in conventional numerical models, whereas it exhibits significant spatial and temporal variation in the observations. This study develops a new parameterization method for determining Q10 by considering the soil respiration dependence on soil temperature and moisture obtained by multiple regression for each vegetation type. This study further investigates the impacts of the new parameterization on the global terrestrial carbon flux. Our results show that a nonuniform spatial distribution of Q10 tends to better represent the dependence of the soil respiration process on heterogeneous surface vegetation type compared with the control simulation using a uniform Q10. Moreover, it tends to improve the simulation of the relationship between soil respiration and soil temperature and moisture, particularly over cold and dry regions. The modification has an impact on the soil respiration and carbon decomposition process, which changes gross primary production (GPP) through controlling nutrient assimilation from soil to vegetation. It leads to a realistic spatial distribution of GPP, particularly over high latitudes where the original model has a significant underestimation bias. Improvement in the spatial distribution of GPP leads to a substantial reduction of global mean GPP bias compared with the in situ observation-based reference data. The results highlight that the enhanced sensitivity of soil respiration to the subsurface soil temperature and moisture introduced by the nonuniform spatial distribution of Q10 has contributed to improving the simulation of the terrestrial carbon fluxes and the global carbon cycle.

Sign in / Sign up

Export Citation Format

Share Document