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.

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.

The validation of a model estimating the Leaf Area Index of grasslands in southern China

2013 ◽  
Vol 35 (3) ◽  
pp. 245 ◽  
Author(s):  
Chengming Sun ◽  
Zhengguo Sun ◽  
Tao Liu ◽  
Doudou Guo ◽  
Shaojie Mu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Mean Squared Error ◽  
Southern China ◽  
Carbon Sink ◽  
Measurement Data ◽  
Light Transmittance ◽  
Area Index ◽  
Normalised Difference Vegetation Index

In order to estimate the leaf area index (LAI) over large areas in southern China, this paper analysed the relationships between normalised difference vegetation index (NDVI) and the vegetation light transmittance and the extinction coefficient based on the use of moderate resolution imaging spectroradiometer data. By using the improved Beer–Lambert Law, a model was constructed to estimate the LAI in the grassy mountains and slopes of southern China with NDVI as the independent variable. The model was validated with field measurement data from different locations and different years in the grassland mountains and slopes of southern China. The results showed that there was a good correlation between the simulated and observed LAI values, and the values of R2 achieved were high. The relative root mean squared error was between 0.109 and 0.12. This indicated that the model was reliable. The above results provided the theoretical basis for the effective management of the grassland resources in southern China and the effective estimation of grassland carbon sink.

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 Identifying Individual Nutrient Deficiencies of Grapevine Leaves Using Hyperspectral Imaging

2021 ◽  
Vol 13 (16) ◽  
pp. 3317
Author(s):  
Sourabhi Debnath ◽  
Manoranjan Paul ◽  
D. M. Motiur Rahaman ◽  
Tanmoy Debnath ◽  
Lihong Zheng ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Spectral Ratio ◽  
Support Vector ◽  
Nutrient Deficiencies ◽  
Wine Quality ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Variation Index ◽  
Grapevine Leaves

The efficiency of a vineyard management system is directly related to the effective management of nutritional disorders, which significantly downgrades vine growth, crop yield and wine quality. To detect nutritional disorders, we successfully extracted a wide range of features using hyperspectral (HS) images to identify healthy and individual nutrient deficiencies of grapevine leaves. Features such as mean reflectance, mean first derivative reflectance, variation index, mean spectral ratio, normalised difference vegetation index (NDVI) and standard deviation (SD) were employed at various stages in the ultraviolet (UV), visible (VIS) and near-infrared (N.I.R.) regions for our experiment. Leaves were examined visually in the laboratory and grouped as either healthy (i.e. control) or unhealthy. Then, the features of the leaves were extracted from these two groups. In a second experiment, features of individual nutrient-deficient leaves (e.g., N, K and Mg) were also analysed and compared with those of control leaves. Furthermore, a customised support vector machine (SVM) was used to demonstrate that these features can be utilised with a high degree of effectiveness to identify unhealthy samples and not only to distinguish from control and nutrient deficient but also to identify individual nutrient defects. Therefore, the proposed work corroborated that HS imaging has excellent potential to analyse features based on healthiness and individual nutrient deficiencies of grapevine leaves.

scholarly journals A feasibility study of uninhabited aircraft systems for rapid and cost-effective plant stress monitoring at green stormwater infrastructure facilities

2020 ◽  
Author(s):  
Kery Prettyman ◽  
Meghna Babbar-Sebens ◽  
Christopher E. Parrish ◽  
Jeremy Matthew Babbar-Sebens
Keyword(s):  
Health Monitoring ◽  
Vegetation Index ◽  
Near Infrared ◽  
Cost Effective ◽  
Plant Diseases ◽  
Vegetation Coverage ◽  
Stress Monitoring ◽  
Urban Stormwater ◽  
Aircraft Systems ◽  
Vegetation Health

Abstract Vegetation health monitoring is key to identifying early signs of water stress, pollutant-induced toxicity, and plant diseases in green urban stormwater facilities. However, rigorous monitoring to collect accurate quantitative data is an expensive and time-consuming process. This paper examines the feasibility of using uninhabited aircraft systems (UAS), in comparison to standard ground-based methods, for monitoring biomass and primary production in two bioswale cells at an urban stormwater facility. Implementation of the UAS-based approach involved flight planning in an urban area to meet resolution requirements of bioswale imagery obtained from near-infrared and red-green-blue cameras. The resulting normalized difference vegetation index (NDVI) estimated from UAS data was tracked over a 2-month period during the transition from spring to summer, showing the spatial distribution of NDVI and the change in vegetation coverage areas over time. In comparison, ground-based measurements of the fraction of intercepted photosynthetically active radiation (PAR) presented multiple practical challenges during implementation in the field, leading to over- and underestimates of intercepted PAR. Overall, UAS-derived NDVI was found to be a valuable reflectance-based, vegetation health-monitoring methodology that can be used by utilities and cities for practical, cost-effective, and rapid assessment of vegetation stress and for long-term maintenance in green stormwater facilities.

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 A Framework for Rice Heavy Metal Stress Monitoring Based on Phenological Phase Space and Temporal Profile Analysis

2019 ◽  
Vol 16 (3) ◽  
pp. 350 ◽  
Author(s):  
Xinyu Zou ◽  
Xiangnan Liu ◽  
Mengxue Liu ◽  
Meiling Liu ◽  
Biyao Zhang
Keyword(s):  
Heavy Metal ◽  
Phase Space ◽  
Vegetation Index ◽  
Profile Analysis ◽  
Heavy Metal Stress ◽  
Metal Stress ◽  
Stress Monitoring ◽  
Temporal Profile ◽  
Rice Growth ◽  
Stress Levels

Previous studies make it possible to use remote sensing techniques to monitor heavy metal stress of rice synchronously and continuously. However, most studies mainly focus on the analysis of rice’s visual symptoms and physiological functions rather than temporal information during the growth period, which may reflect significant changes of rice under heavy metal stress. In this paper, an enhanced spatial and temporal adaptive reflectance fusion model was used to generate synthetic Landsat time series. A normalized difference water index and an enhanced vegetation index were employed to build phenological phase space. Then, the ratio of the rice growth rate fluctuation (GRFI Ratio) was constructed for discriminating the different heavy metal stress levels on rice. Results suggested that the trajectories of rice growth in phenological phase space can depict the similarities and differences of rice growth under different heavy metal stress levels. The most common phenological parameters in the phase space cannot accurately discriminate the heavy metal stress level. However, the GRFI Ratio that we proposed outperformed in discriminating different levels of heavy metal stress. This study suggests that this framework of detecting the heavy metal pollution in paddy filed based on phenological phase space and temporal profile analysis is promising.

scholarly journals Monitoring of Canopy Stress Symptoms in New Zealand Kauri Trees Analysed with AISA Hyperspectral Data

2020 ◽  
Vol 12 (6) ◽  
pp. 926 ◽  
Author(s):  
Jane J. Meiforth ◽  
Henning Buddenbaum ◽  
Joachim Hill ◽  
James Shepherd
Keyword(s):  
New Zealand ◽  
Spectral Range ◽  
Near Infrared ◽  
Hyperspectral Image ◽  
Absolute Error ◽  
Hyperspectral Data ◽  
Aerial Images ◽  
Large Area ◽  
Monitoring Method ◽  
Stress Symptoms

The endemic New Zealand kauri trees (Agathis australis) are under threat by the deadly kauri dieback disease (Phytophthora agathidicida (PA)). This study aimed to identify spectral index combinations for characterising visible stress symptoms in the kauri canopy. The analysis is based on an aerial AISA hyperspectral image mosaic and 1258 reference crowns in three study sites in the Waitakere Ranges west of Auckland. A field-based assessment scheme for canopy stress symptoms (classes 1–5) was further optimised for use with RGB aerial images. A combination of four indices with six bands in the spectral range 450–1205 nm resulted in a correlation of 0.93 (mean absolute error 0.27, RMSE 0.48) for all crown sizes. Comparable results were achieved with five indices in the 450–970 nm region. A Random Forest (RF) regression gave the most accurate predictions while a M5P regression tree performed nearly as well and a linear regression resulted in slightly lower correlations. Normalised Difference Vegetation Indices (NDVI) in the near-infrared / red spectral range were the most important index combinations, followed by indices with bands in the near-infrared spectral range from 800 to 1205 nm. A test on different crown sizes revealed that stress symptoms in smaller crowns with denser foliage are best described in combination with pigment-sensitive indices that include bands in the green and blue spectral range. A stratified approach with individual models for pre-segmented low and high forest stands improved the overall performance. The regression models were also tested in a pixel-based analysis. A manual interpretation of the resulting raster map with stress symptom patterns observed in aerial imagery indicated a good match. With bandwidths of 10 nm and a maximum number of six bands, the selected index combinations can be used for large-area monitoring on an airborne multispectral sensor. This study establishes the base for a cost-efficient, objective monitoring method for stress symptoms in kauri canopies, suitable to cover large forest areas with an airborne multispectral sensor.

Estimation of Eucalypt Forest Leaf Area Index on the South Coast of New South Wales using Landsat MSS Data

1997 ◽  
Vol 45 (5) ◽  
pp. 757 ◽  
Author(s):  
Nicholas Coops ◽  
Antoine Delahaye ◽  
Eddy Pook
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Near Infrared ◽  
New South ◽  
New South Wales ◽  
Monitoring Site ◽  
Area Index ◽  
South Wales ◽  
Normalised Difference Vegetation Index

Research over the last decade has shown that regional estimation of Leaf Area Index (LAI) is possible using the ratio of red and near infrared radiation derived from satellite or airborne sensors. At landscape levels, however, this relationship has been more difficult to establish due to (i) logistic difficulties in measuring seasonal variation in LAI across the landscape over an extended period of time and (ii) difficulties in establishing the effect of understorey, canopy closure, and soil on the spectral radiation at fine spatial resolutions (< 100 m). This paper examines the first issue by utilising a temporal sequence of LAI data of a Eucalyptus mixed hardwood forest (E. maculata Hook., E. paniculata Sm., E. globoidea Blakely, E. pilularis Sm., E. sieberi L.Johnson) in south-eastern New South Wales and comparing it to historical Landsat Multi-Spectral Scanner (MSS) data covering a 9 year period. Field LAI was compared to the Normalised Difference Vegetation Index (NDVI) and the Simple Ratio (SR) derived from the MSS data. Linear relationships were shown to be appropriate to relate both transformations to the LAI data with r2 -values of 0.71 and 0.53 respectively. Using the NDVI relationship, LAI values were estimated along a transect originating from the monitoring site and these were compared to percentage canopy cover values derived from aerial photography.

scholarly journals Monitoring infestations of oak forests by Tortrix viridana (Lepidoptera: Tortricidae) using remote sensing

2016 ◽  
Vol 52 (No. 4) ◽  
pp. 270-276 ◽  
Author(s):  
Gooshbor Leila ◽  
Bavaghar Mahtab Pir ◽  
Amanollahi Jamil ◽  
Ghobari Hamed
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Insect Herbivory ◽  
Oak Forests ◽  
Landsat 8 ◽  
Landsat Images ◽  
Leaf Roller ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Tortrix Viridana

We tested the suitability of Landsat images to track defoliation by insect herbivory with focus on the oak leaf roller, Tortrix viridana (Lep.: Tortricidae). Landsat images from the period before (2002) and after the T. viridana infestation (2007, 2014) were compared in oak forests of Zagros in western Iran. The Normalised Difference Vegetation Index (NDVI) was calculated for the test area from Landsat 5, 7, and 8 images. Because the red and near-infrared spectral bands of Landsat 8 OLI sensors are different from the other two, a model for the calibration of Landsat OLI NDVI was developed. The proposed model with a correlation coefficient of 0.928 and root mean square error of 0.05 turned out to be applicable and the NDVI decreased significantly during the observation period. Taking into account the protection status of the area and small fluctuations in temperature, the decrease in NDVI could be attributed to T. viridana damage.

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