Evaluating satellite remote sensing as a method for measuring yield variability in Avocado and Macadamia tree crops
Accurate yield forecasting in high value fruit tree crops provides vital management information to growers as well as supporting improved decision making, including postharvest handling, storage and forward selling. Current research evaluated the 8 spectral band WorldView 3 (WV-3) with a spatial resolution of 1.2 m, as a tool for exploring the relationship between individual tree canopy reflectance and a number of tree growth parameters, including yield. WV-3 imagery was captured on the 7th of April, 2016, over two Macadamia ('Macadamia integrifolia') and three Avocado ('Persea americana') orchards growing near the Queensland township of Bundaberg, Australia. Using the extent of each block, the WV-3 imagery was sub-setted and classified into 8 Normalised Difference Vegetation index (NDVI) classes. From these classes 6 replicate trees were selected to represent high, medium and low NDVI regions (n=18) and subsequently ground truthed for a number of yield parameters during April and May, 2016. The measured parameters were then correlated against 20 structural and pigment based vegetation indices derived from the 8 band spectral information corresponding to each individual tree canopy (12.6 m2). The results identified a positive relationship between derived vegetation indices (VI) and fruit weight (kg/tree) R2 > 0.69 for Macadamia and R2 > 0.68 for Avocado; and fruit number R2 > 0.6 for Macadamia and R2 > 0.61 for Avocado. The algorithm derived between the optimum VI and yield for each block was then applied across the entire block to derive a yield map. The results show that remote sensing of tree canopy condition can be used to measure yield parameters in Macadamia and Avocado grown in the Bundaberg region.
Multi-temporal landsat algorithms for the yield prediction of sugarcane crops in Australia
Accurate with-in season yield prediction is important for the Australian sugarcane industry as it supports crop management and decision making processes, including those associated with harvest scheduling, storage, milling, and forward selling. In a recent study, a quadratic model was developed from multi-temporal Landsat imagery (30 m spatial resolution) acquired between 2001-2014 (15th November to 31st July) for the prediction of sugarcane yield grown in the Bundaberg region of Queensland, Australia. The resultant high accuracy of prediction achieved from the Bundaberg model for the 2015 and 2016 seasons inspired the development of similar models for the Tully and Mackay growing regions. As with the Bundaberg model, historical Landsat imagery was acquired over a 12 year (Tully) and 10 year (Mackay) period with the capture window again specified to be between 1st November to 30th June to coincide with the sugarcane growing season. All Landsat images were downloaded and processed using Python programing to automate image processing and data extraction. This allowed the model to be applied rapidly over large areas. For each region, the average green normalized difference vegetation index (GNDVI) for all sugarcane crops was extracted from each image and overlayed onto one time scale 1st November to 30th June. Using the quadratic model derived from each regional data set, the maximum GNDVI achieved for each season was calculated and regressed against the corresponding annual average regional sugarcane yield producing strong correlation for both Tully (R2 = 0.89 and RMSE = 5.5 t/ha) and Mackay (R2 = 0.63 and RMSE = 5.3 t/ha). Moreover, the establishment of an annual crop growth profile from each quadratic model has enabled a benchmark of historic crop development to be derived. Any deviation of future crops from this benchmark can be used as an indicator of widespread abiotic or biotic constraints. As well as regional forecasts, the yield algorithms can also be applied at the pixel level to allow individual yield maps to be derived and delivered near real time to all Australian growers and millers.
Using Worldview Satellite Imagery to Map Yield in Avocado (Persea americana): A Case Study in Bundaberg, Australia
Accurate pre-harvest estimation of avocado (Persea americana cv. Haas) yield offers a range of benefits to industry and growers. Currently there is no commercial yield monitor available for avocado tree crops and the manual count method used for yield forecasting can be highly inaccurate. Remote sensing using satellite imagery offers a potential means to achieve accurate pre-harvest yield forecasting. This study evaluated the accuracies of high resolution WorldView (WV) 2 and 3 satellite imagery and targeted field sampling for the pre-harvest prediction of total fruit weight (kg·tree⁻¹) and average fruit size (g) and for mapping the spatial distribution of these yield parameters across the orchard block. WV 2 satellite imagery was acquired over two avocado orchards during 2014, and WV3 imagery was acquired in 2016 and 2017 over these same two orchards plus an additional three orchards. Sample trees representing high, medium and low vigour zones were selected from normalised difference vegetation index (NDVI) derived from the WV images and sampled for total fruit weight (kg·tree⁻¹) and average fruit size (g) per tree. For each sample tree, spectral reflectance data was extracted from the eight band multispectral WV imagery and 18 vegetation indices (VIs) derived. Principal component analysis (PCA) and non-linear regression analysis was applied to each of the derived VIs to determine the index with the strongest relationship to the measured total fruit weight and average fruit size. For all trees measured over the three year period (2014, 2016, and 2017) a consistent positive relationship was identified between the VI using near infrared band one and the red edge band (RENDVI1) to both total fruit weight (kg·tree⁻¹) (R² = 0.45, 0.28, and 0.29 respectively) and average fruit size (g) (R² = 0.56, 0.37, and 0.29 respectively) across all orchard blocks. Separate analysis of each orchard block produced higher R² values as well as identifying different optimal VIs for each orchard block and year. This suggests orchard location and growing season are influencing the relationship of spectral reflectance to total fruit weight and average fruit size. Classified maps of avocado yield (kg·tree⁻¹) and average fruit size per tree (g) were produced using the relationships developed for each orchard block. Using the relationships derived between the measured yield parameters and the optimal VIs, total fruit yield (kg) was calculated for each of the five sampled blocks for the 2016 and 2017 seasons and compared to actual yield at time of harvest and pre-season grower estimates. Prediction accuracies achieved for each block far exceeded those provided by the grower estimates.
'Sugar from Space': Using Satellite Imagery to Predict Cane Yield and Variability
Satellite imagery has been demonstrated to be an effective technology for producing accurate pre-harvest estimates in many agricultural crops. For Australian sugarcane, yield forecasting models have been developed from a single date SPOT satellite image acquired around peak crop growth. However, a failure to acquire a SPOT image at this critical growth stage, from continued cloud cover or from competition for the satellite, can prevent an image being captured and therefore a forecast being made for that season. In order to reduce the reliance on a single image capture and to improve the accuracies of the forecasts themselves, time series yield prediction models have been developed for eight sugarcane growing regions using multiple years of free Landsat satellite images. In addition to the forecasting of average regional yield, an automated computational and programming procedure enabling the derivation of crop vigour variability (GNDVI) maps from the freely available Sentinel 2 satellite imagery was developed. These maps, produced for 15 sugarcane growing regions during the 2017 growing season, identify both variations in crop vigour across regions and within every individual crop. These outputs were made available to collaborating mills within each growing region. This paper presents the accuracies achieved from the time series yield forecasting models versus actual 2017 yields for the respective regions, as well as provides an example of the derived mapping outputs.
Evaluation of the Range Accuracy and the Radiometric Calibration of Multiple Terrestrial Laser Scanning Instruments for Data Interoperability
Terrestrial laser scanning (TLS) data provide 3-D measurements of vegetation structure and have the potential to support the calibration and validation of satellite and airborne sensors. The increasing range of different commercial and scientific TLS instruments holds challenges for data and instrument interoperability. Using data from various TLS sources will be critical to upscale study areas or compare data. In this paper, we provide a general framework to compare the interoperability of TLS instruments. We compare three TLS instruments that are the same make and model, the RIEGL VZ-400. We compare the range accuracy and evaluate the manufacturer's radiometric calibration for the uncalibrated return intensities. Our results show that the range accuracy between instruments is comparable and within the manufacturer's specifications. This means that the spatial XYZ data of different instruments can be combined into a single data set. Our findings demonstrate that radiometric calibration is instrument specific and needs to be carried out for each instrument individually before including reflectanceinformation in TLS analysis. We show that the residuals between the calibrated reflectance panels and the apparent reflectance measured by the instrument are greatest for highest reflectance panels (residuals ranging from 0.058 to 0.312).
Measuring plot scale woodland structure using terrestrial laser scanning
Terrestrial laser scanning (TLS) can be used to characterize a woodland site by measuring structural attributes of the vegetation community. In Australia, government funded programs monitor vegetation structure using manual field surveys to assess change and ecological condition. In this study, we examined whether structural attributes commonly assessed in woodland ecology surveys can be extracted from a single TLS scan. Attributes of the ground, shrub and overstory vegetation layers were evaluated at nine open woodland sites in central Western Queensland. We used 0.1 m voxels to aggregate returns. Our results show that, compared with field assessment by highly experienced ecologists, TLS can rapidly characterize structural attributes for tree canopy cover, maximum tree height, average tree height (R² > 0.9) and average diameter at breast height (R² = 0.77). However, we could not accurately determine shrub height, shrub canopy cover, shrub average height, ground cover (grass, litter and coarse woody debris) or the number of trees per hectare (R² < 0.45). By analysing local minima in the histogram of the maximum height, we determined height thresholds for canopy strata, and applied these to determine the canopy layer with the most biomass – the ecologically dominant layer (EDL). While these results are promising for overstory assessment and defining canopy strata heights using TLS, they suggest that future research should focus on investigating improved classification methods to separate laser returns into shrub and tree objects for structural assessment at the plot scale.
Monitoring gully change: A comparison of airborne and terrestrial laser scanning using a case study from Aratula, Queensland
Airborne laser scanning (ALS) and terrestrial laser scanning (TLS) technologies capture spatially detailed estimates of surface topography and when collected multi-temporally can be used to assess geomorphic change. The sensitivity and repeatability of ALS measurements to characterise geomorphic change in topographically complex environments such as gullies; however, remains an area lacking quantitative research. In this study, we captured coincident ALS and TLS datasets to assess their ability and synergies to detect geomorphic change for a gully located in Aratula, southeast Queensland, Australia. We initially used the higher spatial density and ranging accuracy of TLS to provide an assessment of the Digital Elevation Models (DEM) derived from ALS within a gully environment. Results indicated mean residual errors of 0.13 and 0.09 m along with standard deviation (SD) of residual errors of 0.20 and 0.16 m using pixel sizes of 0.5 and 1.0 m, respectively. The positive mean residual errors confirm that TLS data consistently detected deeper sections of the gully than ALS. We also compared the repeatability of ALS and TLS for characterising gully morphology. This indicated that the sensitivity to detect change using ALS is substantially lower than TLS, as expected, and that the ALS survey characteristics influence the ability to detect change. Notably, we found that using one ALS transect (mean density of 5 points / m²) as opposed to three transects increased the SD of residual error by approximately 30%. The supplied classification of ALS ground points was also demonstrated to misclassify gully features as non-ground, with minimum elevation filtering found to provide a more accurate DEM of the gully. The number and placement of terrestrial laser scans were also found to influence the derived DEMs. Furthermore, we applied change detection using two ALS data captures over a four year period and four TLS field surveys over an eight month period. This demonstrated that ALS can detect large scale erosional changes with head cutting of gully branches migrating approximately 10 m upslope. In comparison, TLS captured smaller scale intra-annual erosional patterns largely undetectable by the ALS dataset with a large rainfall event coinciding with the highest volumetric change (net change >46 m³). Overall, these findings reaffirm the importance of quantifying DEM errors and demonstrate that ALS is unlikely to detect subtle geomorphic changes (<0.45 m) potentially missing significant sediment change. TLS was able to detect more subtle intra-annual changes but was limited in its spatial coverage. This suggests TLS and ALS surveys are complementary technologies and when used together can provide a more detailed understanding of gully processes at different temporal and spatial scales, provided the inherent errors are taken into account.
Estimating Changes in Leaf Area, Leaf Area Density, and Vertical Leaf Area Profile for Mango, Avocado, and Macadamia Tree Crowns Using Terrestrial Laser Scanning
Vegetation metrics, such as leaf area (LA), leaf area density (LAD), and vertical leaf area profile, are essential measures of tree-scale biophysical processes associated with photosynthetic capacity, and canopy geometry. However, there are limited published investigations of their use for horticultural tree crops. This study evaluated the ability of light detection and ranging (LiDAR) for measuring LA, LAD, and vertical leaf area profile across two mango, macadamia and avocado trees using discrete return data from a RIEGL VZ-400 Terrestrial Laser Scanning (TLS) system. These data were collected multiple times for individual trees to align with key growth stages, essential management practices, and following a severe storm. The first return of each laser pulse was extracted for each individual tree and classified as foliage or wood based on TLS point cloud geometry. LAD at a side length of 25 cm voxels, LA at the canopy level and vertical leaf area profile were calculated to analyse tree crown changes. These changes included: (1) pre-pruning vs. post-pruning for mango trees" (2) pre-pruning vs. post-pruning for macadamia trees" (3) pre-storm vs. post-storm for macadamia trees" and (4) tree leaf growth over a year for two young avocado trees. Decreases of 34.13 m2 and 8.34 m2 in LA of mango tree crowns occurred due to pruning. Pruning for the high vigour mango tree was mostly identified between 1.25 m and 3 m. Decreases of 38.03 m2 and 16.91 m2 in LA of a healthy and unhealthy macadamia tree occurred due to pruning. After flowering and spring flush of the same macadamia trees, storm effects caused a 9.65 m2 decrease in LA for the unhealthy tree, while an increase of 34.19 m2 occurred for the healthy tree. The tree height increased from 11.13 m to 11.66 m, and leaf loss was mainly observed between 1.5 m and 4.5 m for the unhealthy macadamia tree. Annual increases in LA of 82.59 m2 and 59.97 m2 were observed for two three-year-old avocado trees. Our results show that TLS is a useful tool to quantify changes in the LA, LAD, and vertical leaf area profiles of horticultural trees over time, which can be used as a general indicator of tree health, as well as assist growers with improved pruning, irrigation, and fertilisation application decisions.
An Accuracy Assessment of Derived Digital Elevation Models from Terrestrial Laser Scanning in a Sub-Tropical Forested Environment
Forest structure attributes produced from terrestrial laser scanning (TLS) rely on normalisation of the point cloud values from sensor coordinates to height above ground. One method to do this is through the derivation of an accurate and repeatable digital elevation model (DEM) from the TLS point cloud that is used to adjust the height. The primary aim of this paper was to test a number of TLS scan configurations, filtering options and output DEM grid resolutions (from 0.02 m to 1.0 m) to define a best practice method for DEM generation in sub-tropical forest environments. The generated DEMs were compared to both total station (TS) spot heights and a 1-m DEM generated from airborne laser scanning (ALS) to assess accuracy. The comparison to TS spot heights found that a DEM produced using the minimum elevation (minimum Z value) from a point cloud derived from a single scan had mean errors >1 m for DEM grid resolutions <0.2 m at a 25-m plot radius. At a 1-m grid resolution, the mean error was 0.19 m. The addition of a filtering approach that combined a median filter with a progressive morphological filter and a global percentile filter was able to reduce mean error of the 0.02-m grid resolution DEM to 0.31 m at a 25-m plot radius using all returns. Using multiple scan positions to derive the DEM reduced the mean error for all DEM methods. Our results suggest that a simple minimum Z filtering DEM method using a single scan at the grid resolution of 1 m can produce mean errors <0.2 m, but for a small grid resolution, such as 0.02 m, a more complex filtering approach and multiple scan positions are required to reduced mean errors. The additional validation data provided by the 1-m ALS DEM showed that when using the combined filtering method on a point cloud derived from a single scan at the plot centre, errors between 0.1 and 0.5 m occurred in the TLS DEM for all tested grid resolutions at a plot radius of 25 m. These findings present a protocol for DEM production from TLS data at a range of grid resolutions and provide an overview of factors affecting DEMs produced from single and multiple TLS scan positions.