Multi-sensor modelling of a forest productivity index for radiata pine plantations

Watt, Michael S.; Dash, Jonathan P.; Watt, Pete; Bhandari, Sagar

doi:10.1186/s40490-016-0065-z

Cited by 13 publications

(6 citation statements)

References 61 publications

(64 reference statements)

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“…RF regression utilises ensemble decision tree classifiers, based on bootstrap aggregated sampling (bagging), to construct many individual decision trees, from which a final class assignment is determined [70]. RF has previously been used to successfully model several plantation forest variables using remotely sensed data [6,7,73,74]. The RF algorithm constructs decision trees using a bootstrap sample from the available training data, with the remaining data assigned as out-of-bag (OOB) samples.…”

Section: Discussionmentioning

confidence: 99%

“…Highly productive plantation forests are required to meet the timber and fibre demands of the Earth's population in a sustainable manner. A significant research effort is focussed on maximising the returns from global forest plantations through silvicultural practices [1,2], tree breeding programmes [3,4], improving forest nutrition [5], and developing remote sensing methods to enhance forest management [6][7][8]. Yields from intensively managed monoculture plantations face significant threats from unwanted organisms, the impact of extreme weather events, and increasingly from the adverse effects of air pollution [9,10].…”

Section: Introductionmentioning

confidence: 99%

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UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

2018

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The development of methods that can accurately detect physiological stress in forest trees caused by biotic or abiotic factors is vital for ensuring productive forest systems that can meet the demands of the Earth’s population. The emergence of new sensors and platforms presents opportunities to augment traditional practices by combining remotely-sensed data products to provide enhanced information on forest condition. We tested the sensitivity of multispectral imagery collected from time-series unmanned aerial vehicle (UAV) and satellite imagery to detect herbicide-induced stress in a carefully controlled experiment carried out in a mature Pinus radiata D. Don plantation. The results revealed that both data sources were sensitive to physiological stress in the study trees. The UAV data were more sensitive to changes at a finer spatial resolution and could detect stress down to the level of individual trees. The satellite data tested could only detect physiological stress in clusters of four or more trees. Resampling the UAV imagery to the same spatial resolution as the satellite imagery revealed that the differences in sensitivity were not solely the result of spatial resolution. Instead, vegetation indices suited to the sensor characteristics of each platform were required to optimise the detection of physiological stress from each data source. Our results define both the spatial detection threshold and the optimum vegetation indices required to implement monitoring of this forest type. A comparison between time-series datasets of different spectral indices showed that the two sensors are compatible and can be used to deliver an enhanced method for monitoring physiological stress in forest trees at various scales. We found that the higher resolution UAV imagery was more sensitive to fine-scale instances of herbicide induced physiological stress than the RapidEye imagery. Although less sensitive to smaller phenomena the satellite imagery was found to be very useful for observing trends in physiological stress over larger areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

2018

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“…RF is increasingly being applied to natural resource problems [49] and has previously been used to successfully model several plantation forest variables using remotely-sensed data [50][51][52][53]. The RF algorithm constructs decision trees using a bootstrap sample from the available training data, with the remaining assigned as out-of-bag (OOB) samples.…”

Section: Random Forestmentioning

confidence: 99%

Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection

et al. 2017

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Abstract:The spread of exotic conifers from commercial plantation forests has significant economic and ecological implications. Accurate methods for invasive conifer detection are required to enable monitoring and guide control. In this research, we combined spectral information from aerial imagery with data from airborne laser scanning (ALS) to develop methods to identify invasive conifers using remotely-sensed data. We examined the effect of ALS pulse density and the height threshold of the training dataset on classification accuracy. The results showed that adding spectral values to the ALS metrics/variables in the training dataset led to significant increases in classification accuracy. The most accurate models (kappa range of 0.773-0.837) had either four or five explanatory variables, including ALS elevation, the near-infrared band and different combinations of ALS intensity and red and green bands. The best models were found to be relatively invariant to changes in pulse density (1-21 pls/m 2 ) or the height threshold (0-2 m) used for the inclusion of data in the training dataset. This research has extended and improved the methods for scattered single tree detection and offered valuable insight into campaign settings for the monitoring of invasive conifers (tree weeds) using remote sensing approaches.

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“…An estimate of phenotypic variation across the landscape was mapped through developing a spatial surface of forest productivity. The parametric modelling methods described in (Watt et al, 2015;Watt et al, 2016) were used to describe the distribution of Site Index across the forest based on the ALS data set and Site Index extracted from the field plots described above. The purpose of this process was to provide a response variable that can potentially be linked to genetic and environmental factors across the study forest.…”

Section: Mapping Phenotypic Variationmentioning

confidence: 99%

Forest-Scale Phenotyping: Productivity Characterisation Through Machine Learning

et al. 2020

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Advances in remote sensing combined with the emergence of sophisticated methods for large-scale data analytics from the field of data science provide new methods to model complex interactions in biological systems. Using a data-driven philosophy, insights from experts are used to corroborate the results generated through analytical models instead of leading the model design. Following such an approach, this study outlines the development and implementation of a whole-of-forest phenotyping system that incorporates spatial estimates of productivity across a large plantation forest. In largescale plantation forestry, improving the productivity and consistency of future forests is an important but challenging goal due to the multiple interactions between biotic and abiotic factors, the long breeding cycle, and the high variability of growing conditions. Forest phenotypic expression is highly affected by the interaction of environmental conditions and forest management but the understanding of this complex dynamics is incomplete. In this study, we collected an extensive set of 2.7 million observations composed of 62 variables describing climate, forest management, tree genetics, and fine-scale terrain information extracted from environmental surfaces, management records, and remotely sensed data. Using three machine learning methods, we compared models of forest productivity and evaluate the gain and Shapley values for interpreting the influence of categorical variables on the power of these methods to predict forest productivity at a landscape level. The most accurate model identified that the most important drivers of productivity were, in order of importance, genetics, environmental conditions, leaf area index, topology, and soil properties, thus describing the complex interactions of the forest. This approach demonstrates that new methods in remote sensing and data science enable powerful, landscape-level understanding of forest productivity. The phenotyping method developed here can be used to identify superior and inferior genotypes and estimate a productivity index for individual site. This approach can improve tree breeding and deployment of the right genetics to the right site in order to increase the overall productivity across planted forests.

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Multi-sensor modelling of a forest productivity index for radiata pine plantations

Cited by 13 publications

References 61 publications

UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health

Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection

Forest-Scale Phenotyping: Productivity Characterisation Through Machine Learning

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