The patterns of grazed pasture associated with scattered trees across an Australian temperate landscape: an investigation of pasture quantity and quality

2011 ◽  
Vol 33 (2) ◽  
pp. 121 ◽  
Author(s):  
Phoebe Barnes ◽  
Brian R. Wilson ◽  
Mark G. Trotter ◽  
David W. Lamb ◽  
Nick Reid ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Parent Material ◽  
Agricultural Landscapes ◽  
Tree Canopy ◽  
Canopy Reflectance ◽  
Near Infrared Reflectance ◽  
Pasture Production ◽  
Normalised Difference Vegetation Index ◽  
Scattered Trees

Scattered paddock trees occur across agricultural landscapes in Australia. However, in the temperate regions of Australia their numbers are rapidly declining and they may be lost across much of the landscape in 200 years. Here we examined the spatial distribution of green (GDB), senescent (SDB) and total (TDB) dry pasture biomass, and nutrient status of the GDB fraction around scattered Eucalyptus trees on three parent materials (basalt, granite and meta-sediment) in native and sown pastures across a range of grazed temperate landscapes in northern New South Wales. We used a combination of destructive harvests and a handheld active optical canopy reflectance sensor (AOS) with an integrated GPS to examine pasture biomass around scattered trees. The harvested pasture biomass data indicated that under grazed conditions the presence of scattered trees did not introduce significant radial trends in TDB or GDB out to a distance of 3.5 canopy radii regardless of tree species or parent material. The red and near-infrared reflectance-based Normalised Difference Vegetation Index (NDVI), as measured by the AOS, did indicate a consistent azimuthal trend with larger GDB on the southern side of the tree and lower GDB on the northern side in the native pasture. However, this observation must be qualified as the regression coefficient for the relationship between NDVI and GDB was significant but weak (best r2 = 0.42), and SDB reduced its predictive capacity. We also found a higher percentage of GDB under the canopy than in the open paddock. We suggest that the combination of these results may indicate higher grazing pressure under trees than in the open paddock. Pasture nutrient concentration (P, K and S) was higher in both native and sown pastures beneath the tree canopy compared with the open paddock. This study indicates that, in this temperate environment, scattered trees do not adversely affect pasture production, and that they can improve some pasture nutrients.

scholarly journals Combined effects of altered N:P stoichometry and trees on Mediterranean savanna root dynamics

2018 ◽  
Author(s):  
Richard Nair ◽  
Martin Hertel ◽  
Yunpeng Luo ◽  
Gerardo Moreno ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Community Structure ◽  
Root Biomass ◽  
Vegetation Index ◽  
Near Infrared ◽  
Root Length Density ◽  
Root Traits ◽  
Tree Canopy ◽  
Root Dynamics ◽  
Ingrowth Cores ◽  
Normalised Difference Vegetation Index

Abstract. Mediterranean grasslands are highly seasonal and co-limited by water and nutrients. In such systems little is known about root dynamics which may depend on plant habit and environment as well seasonal water shortages and site fertility. This latter factor is affected by the presence of scattered trees and site management including grazing, as well as chronic nitrogen deposition, which may lead to N:P imbalance. In this study we combined observations from minirhizotrons collected in a Mediterranean tree-grass ecosystem (Spanish Dehesa), with root measurements from direct soil cores and ingrowth cores, and above-ground biomass to investigate seasonal root dynamics and root:shoot ratios. We investigated responses to soil fertility, using a nutrient manipulation (N / NP additions) and microhabitats effects between open pasture and under tree canopy locations. Root dynamics over time were compared with indices of above-ground growth drawn from proximal remote sensing (Normalised Difference Vegetation Index and Green Chromatic Coordinate derived from near-infrared enabled digital repeat photography). Results show distinct differences in root dynamics and biomass between treatments and microhabitats. Root biomass was higher with N additions, but not with NP additions in early spring, but by the end of the growing season root biomass had increased with NP in open pastures but not higher than N alone. In contrast, root length density (RLD) in pastures responded stronger to the NP than N only treatment, while beneath trees RLD tended to be higher with only N. Even though root biomass increased, root:shoot ratio decreased under nutrient treatments.We interpret these differences as a shift in community structure and/or root traits under changing stoichiometry and altered nutrient limitations. The timing of maximum root cover, as assessed by the minirhizotrons, did not match with above-ground phenology indicators at the site as root growth was low during autumn despite the greening up of the ecosystem. In other periods, roots responded quickly to rain events on the scale of days, matching changes in above-ground indices. Our results highlight the need for high resolution sampling to increase understanding of root dynamics in such systems, linkage with shifts in community structure and traits, and targeting of appropriate periods of the year for in-depth campaigns.

scholarly journals Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fan Liu ◽  
Chuankuan Wang ◽  
Xingchang Wang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Near Infrared Reflectance ◽  
Modis Ndvi ◽  
Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

scholarly journals Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems

2018 ◽  
Vol 10 (8) ◽  
pp. 1293 ◽  
Author(s):  
Yunpeng Luo ◽  
Tarek S. El-Madany ◽  
Gianluca Filippa ◽  
Xuanlong Ma ◽  
Bernhard Ahrens ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Plant Phenology ◽  
Mediterranean Ecosystem ◽  
Near Infrared Reflectance ◽  
Seasonally Dry ◽  
Growing Season Length ◽  
Dry Down

Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

scholarly journals Monitoring the Impact of Environmental Manipulation on Peatland Surface by Simple Remote Sensing Indices

2018 ◽  
Vol 23 ◽  
pp. 00030 ◽  
Author(s):  
Anshu Rastogi ◽  
Subhajit Bandopadhyay ◽  
Marcin Stróżecki ◽  
Radosław Juszczak
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Environmental Manipulation ◽  
Chlorophyll Index ◽  
The Impact ◽  
Remote Sensing Indices

The behaviour of nature depends on the different components of climates. Among these, temperature and rainfall are two of the most important components which are known to change plant productivity. Peatlands are among the most valuable ecosystems on the Earth, which is due to its high biodiversity, huge soil carbon storage, and its sensitivity to different environmental factors. With the rapid growth in industrialization, the climate change is becoming a big concern. Therefore, this work is focused on the behaviour of Sphagnum peatland in Poland, subjected to environment manipulation. Here it has been shown how a simple reflectance based technique can be used to assess the impact of climate change on peatland. The experimental setup consists of four plots with two kind of manipulations (control, warming, reduced precipitation, and a combination of warming and reduced precipitation). Reflectance data were measured twice in August 2017 under a clear sky. Vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), near-infrared reflectance of vegetation (NIRv), MERIS terrestrial chlorophyll index (MTCI), Green chlorophyll index (CIgreen), Simple Ration (SR), and Water Band Index (WBI) were calculated to trace the impact of environmental manipulation on the plant community. Leaf Area Index of vascular plants was also measured for the purpose to correlate it with different VIs. The observation predicts that the global warming of 1°C may cause a significant change in peatland behaviour which can be tracked and monitored by simple remote sensing indices.

scholarly journals Evaluation and Normalization of Topographic Effects on Vegetation Indices

2020 ◽  
Vol 12 (14) ◽  
pp. 2290
Author(s):  
Rui Chen ◽  
Gaofei Yin ◽  
Guoxiang Liu ◽  
Jing Li ◽  
Aleixandre Verger
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Topographic Effects ◽  
Terrestrial Vegetation ◽  
Mountainous Areas ◽  
Near Infrared Reflectance ◽  
Illumination Condition ◽  
Vegetation Monitoring ◽  
Local Illumination

The normalization of topographic effects on vegetation indices (VIs) is a prerequisite for their proper use in mountainous areas. We assessed the topographic effects on the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the soil adjusted vegetation index (SAVI), and the near-infrared reflectance of terrestrial vegetation (NIRv) calculated from Sentinel-2. The evaluation was based on two criteria: the correlation with local illumination condition and the dependence on aspect. Results show that topographic effects can be neglected for the NDVI, while they heavily influence the SAVI, EVI, and NIRv: the local illumination condition explains 19.85%, 25.37%, and 26.69% of the variation of the SAVI, EVI, and NIRv, respectively, and the coefficients of variation across different aspects are, respectively, 8.13%, 10.46%, and 14.07%. We demonstrated the applicability of existing correction methods, including statistical-empirical (SE), sun-canopy-sensor with C-correction (SCS + C), and path length correction (PLC), dedicatedly designed for reflectance, to normalize topographic effects on VIs. Our study will benefit vegetation monitoring with VIs over mountainous areas.

scholarly journals Simulation and Verification of Vertical Heterogeneity Spectral Response of Winter Wheat Based on the mSCOPE Model

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4570
Author(s):  
Linsheng Huang ◽  
Yuanyuan Zhang ◽  
Guijun Yang ◽  
Dong Liang ◽  
Heli Li ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vertical Profile ◽  
Near Infrared ◽  
Spectral Response ◽  
Visible Region ◽  
Operating Efficiency ◽  
Canopy Reflectance ◽  
Near Infrared Reflectance ◽  
Area Index ◽  
Vertical Heterogeneity

Vertical heterogeneity of the biochemical characteristics of crop canopy is important in diagnosing and monitoring nutrition, disease, and crop yield via remote sensing. However, the research on vertical isomerism was not comprehensive. Experiments were carried out from the two levels of simulation and verification to analyze the applicability of this recently development model. Effects of winter wheat on spectrum were studied when input different structure parameters (e.g., leaf area index (LAI)) and physicochemical parameters (e.g., chlorophyll content (Chla+b) and water content (Cw)) to the mSCOPE (Soil Canopy Observation, Photochemistry, and Energy fluxes) model. The maximum operating efficiency was 127.43, when the winter wheat was stratified into three layers. Meanwhile, the simulation results also proved that: the vertical profile of LAI had an influence on canopy reflectance in almost all bands; the vertical profile of Chla+b mainly affected the reflectivity of visible region; the vertical profile of Cw only affected the near-infrared reflectance. The verification results showed that the vegetation indexes (VIs) selected of different bands were strongly correlated with the parameters of the canopy. LAI, Chla+b and Cw affected VIs estimation related to LAI, Chla+b and Cw respectively. The Root Mean Square Error (RMSE) of the new-proposed NDVIgreen was the smallest, which was 0.05. Sensitivity analysis showed that the spectrum was more sensitive to changes in upper layer parameters, which verified the rationality of mSCOPE model in explaining the law that light penetration in vertical nonuniform canopy gradually decreases with the increase of layers.

scholarly journals Rapid identification of shallow inundation for mosquito disease mitigation using drone-derived multispectral imagery

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Tasya Vadya Sarira ◽  
Kenneth Clarke ◽  
Philip Weinstein ◽  
Lian Pin Koh ◽  
Megan Lewis
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Imaging System ◽  
South Australia ◽  
Rapid Identification ◽  
Breeding Habitat ◽  
Mapping Accuracy ◽  
Normalised Difference Vegetation Index ◽  
Mosquito Breeding

Mosquito breeding habitat identification often relies on slow, labour-intensive and expensive ground surveys. With advances in remote sensing and autonomous flight technologies, we endeavoured to accelerate this detection by assessing the effectiveness of a drone multispectral imaging system to determine areas of shallow inundation in an intertidal saltmarsh in South Australia. Through laboratory experiments, we characterised Near-Infrared (NIR) reflectance responses to water depth and vegetation cover, and established a reflectance threshold for mapping water sufficiently deep for potential mosquito breeding. We then applied this threshold to field-acquired drone imagery and used simultaneous in-situ observations to assess its mapping accuracy. A NIR reflectance threshold of 0.2 combined with a vegetation mask derived from Normalised Difference Vegetation Index (NDVI) resulted in a mapping accuracy of 80.3% with a Cohen’s Kappa of 0.5, with confusion between vegetation and shallow water depths (< 10 cm) appearing to be major causes of error. This high degree of mapping accuracy was achieved with affordable drone equipment, and commercially available sensors and Geographic Information Systems (GIS) software, demonstrating the efficiency of such an approach to identify shallow inundation likely to be suitable for mosquito breeding.

Assessing Hemlock Decline Using Visible and Near-Infrared Spectroscopy: Indices Comparison and Algorithm Development

2005 ◽  
Vol 59 (6) ◽  
pp. 836-843 ◽  
Author(s):  
Jennifer Pontius ◽  
Richard Hallett ◽  
Mary Martin
Keyword(s):  
Linear Regression ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Stress Index ◽  
Regression Equations ◽  
Near Infrared Reflectance ◽  
Full Spectrum ◽  
Hemlock Decline

Near-infrared reflectance spectroscopy was evaluated for its effectiveness at predicting pre-visual decline in eastern hemlock trees. An ASD FieldSpec Pro FR field spectroradiometer measuring 2100 contiguous 1-nm-wide channels from 350 nm to 2500 nm was used to collect spectra from fresh hemlock foliage. Full spectrum partial least squares (PLS) regression equations and reduced stepwise linear regression equations were compared. The best decline predictive model was a 6-term linear regression equation ( R2 = 0.71, RMSE = 0.591) based on: Carter Miller Stress Index (R694/R760), Derivative Chlorophyll Index (FD705/FD723), Normalized Difference Vegetation Index ((R800 – R680)/(R800 + R680)), R950, R1922, and FD1388. Accuracy assessment showed that this equation predicted an 11-class decline rating with a 1-class tolerance accuracy of 96% and differentiated healthy trees from those in very early decline with 72% accuracy. These results indicate that narrow-band sensors could be developed to detect very early stages of hemlock decline, before visual symptoms are apparent. This capability would enable land managers to identify early hemlock woolly adelgid infestations and monitor forest health over large areas of the landscape.

scholarly journals Evaluation of Visible-Near Infrared Reflectance Spectra of Avocado Leaves as a Non-destructive Sensing Tool for Detection of Laurel Wilt

Plant Disease ◽  
2012 ◽  
Vol 96 (11) ◽  
pp. 1683-1689 ◽  
Author(s):  
Sindhuja Sankaran ◽  
Reza Ehsani ◽  
Sharon A. Inch ◽  
Randy C. Ploetz
Keyword(s):  
Discriminant Analysis ◽  
Vegetation Index ◽  
Near Infrared ◽  
Principal Component ◽  
Persea Americana ◽  
Near Infrared Reflectance ◽  
Laurel Wilt ◽  
Linear Discriminant ◽  
Raffaelea Lauricola ◽  
Non Destructive

Laurel wilt, caused by the fungus Raffaelea lauricola, affects the growth, development, and productivity of avocado, Persea americana. This study evaluated the potential of visible-near infrared spectroscopy for non-destructive sensing of this disease. The symptoms of laurel wilt are visually similar to those caused by freeze damage (leaf necrosis). In this work, we performed classification studies with visible-near infrared spectra of asymptomatic and symptomatic leaves from infected plants, as well as leaves from freeze-damaged and healthy plants, both of which were non-infected. The principal component scores computed from principal component analysis were used as input features in four classifiers: linear discriminant analysis, quadratic discriminant analysis (QDA), Naïve-Bayes classifier, and bagged decision trees (BDT). Among the classifiers, QDA and BDT resulted in classification accuracies of higher than 94% when classifying asymptomatic leaves from infected plants. All of the classifiers were able to discriminate symptomatic-infected leaves from freeze-damaged leaves. However, the false negatives mainly resulted from asymptomatic-infected leaves being classified as healthy. Analyses of average vegetation indices of freeze-damaged, healthy (non-infected), asymptomatic-infected, and symptomatic-infected leaves indicated that the normalized difference vegetation index and the simple ratio index were statistically different.

scholarly journals Stress Detection in New Zealand Kauri Canopies with WorldView-2 Satellite and LiDAR Data

2020 ◽  
Vol 12 (12) ◽  
pp. 1906 ◽  
Author(s):  
Jane J. Meiforth ◽  
Henning Buddenbaum ◽  
Joachim Hill ◽  
James D. Shepherd ◽  
John R. Dymond
Keyword(s):  
New Zealand ◽  
Vegetation Index ◽  
Near Infrared ◽  
Mean Squared Error ◽  
Full Range ◽  
Lidar Data ◽  
Stress Monitoring ◽  
Stress Symptoms ◽  
Normalised Difference Vegetation Index ◽  
Stress Levels

New Zealand kauri trees are threatened by the kauri dieback disease (Phytophthora agathidicida (PA)). In this study, we investigate the use of pan-sharpened WorldView-2 (WV2) satellite and Light Detection and Ranging (LiDAR) data for detecting stress symptoms in the canopy of kauri trees. A total of 1089 reference crowns were located in the Waitakere Ranges west of Auckland and assessed by fieldwork and the interpretation of aerial images. Canopy stress symptoms were graded based on five basic stress levels and further refined for the first symptom stages. The crown polygons were manually edited on a LiDAR crown height model. Crowns with a mean diameter smaller than 4 m caused most outliers with the 1.8 m pixel size of the WV2 multispectral bands, especially at the more advanced stress levels of dying and dead trees. The exclusion of crowns with a diameter smaller than 4 m increased the correlation in an object-based random forest regression from 0.85 to 0.89 with only WV2 attributes (root mean squared error (RMSE) of 0.48, mean absolute error (MAE) of 0.34). Additional LiDAR attributes increased the correlation to 0.92 (RMSE of 0.43, MAE of 0.31). A red/near-infrared (NIR) normalised difference vegetation index (NDVI) and a ratio of the red and green bands were the most important indices for an assessment of the full range of stress symptoms. For detection of the first stress symptoms, an NDVI on the red-edge and green bands increased the performance. This study is the first to analyse the use of spaceborne images for monitoring canopy stress symptoms in native New Zealand kauri forest. The method presented shows promising results for a cost-efficient stress monitoring of kauri crowns over large areas. It will be tested in a full processing chain with automatic kauri identification and crown segmentation.

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