Evaluation of remote sensing methods for continuous cover forestry

The overall aim of the project was to investigate the potential and challenges in the application of high spatial and spectral resolution remote sensing to forest stands in the UK for Continuous Cover Forestry (CCF) purposes. Within the context of CCF, a relatively new forest management strategy that has been implemented in several European countries, the usefulness of digital remote sensing techniques lie in their potential ability to retrieve parameters at sub-stand level and, in particular, in the assessment of natural regeneration and light regimes. The idea behind CCF is the support of a sustainable forest management system reducing disturbance of the forest ecosystem and encouraging the use of more natural methods, e.g. natural regeneration, for which the light environment beneath the forest canopy plays a fundamental role.The study was carried out at a test area in central Scotland, situated within the Queen Elizabeth II Forest Park (lat. 56°10' N, long. 4° 23' W). Six plots containing three different species (Norway spruce, European larch and Sessile oak), characterized by their different light regimes, were established within the area for the measurement of forest variables using a forest inventory approach and hemispherical photography. The remote sensing data available for the study consisted of Landsat ETM+ imagery, a small footprint multi-return lidar dataset over the study area, Airborne Thematic Mapper (ATM) data, and aerial photography with same acquisition date as the lidar data.Landsat ETM+ imagery was used for the spectral characterisation of the species under study and the evaluation of phenological change as a factor to consider for future acquisitions of remotely sensed imagery. Three approaches were used for the discrimination between species: raw data, NDVI, and Principal Component Analysis (PCA). It can be concluded that no single date is ideal for discriminating the species studied (early summer was best) and that a combination of two or three datasets covering their phenological cycles is optimal for the differentiation. Although the approaches used helped to characterize the forest species, especially to the discrimination between spruces, larch and the deciduous oak species, further work is needed in order to define an optimum approach to discriminate between spruce species (e.g. Sitka spruce and Norway spruce) for which spectral responses are very similar. In general, the useful ranges of the indices were small, so a careful and accurate preprocessing of the imagery is highly recommended.Lidar, ATM, and aerial photographic datasets were analysed for the characterisation of vertical and horizontal forest structure. A slope-based algorithm was developed for the extraction of ground elevation and tree heights from multiple return lidar data, the production of a Digital Terrain Model (DTM) and Digital Surface Model (DSM) of the area under study, and for the comparison of the predicted lidar tree heights with the true tree heights, followed by the building of a Digital Canopy Model (DCM) for the determination of percentage canopy cover and tree crown delineation. Mean height and individual tree heights were estimated for all sample plots. The results showed that lidar underestimated tree heights by an average of 1.49 m. The standard deviation of the lidar estimates was 3.58 m and the mean standard error was 0.38 m.This study assessed the utility of an object-oriented approach for deciduous and coniferous crown delineation, based on small-footprint, multiple return lidar data, high resolution ATM imagery, and aerial photography. Special emphasis in the analysis was made in the fusion of aerial photography and lidar data for tree crown detection and classification, as it was expected that the high vertical accuracy of lidar, combined with the high spatial resolution aerial photography would render the best results and would provide the forestry sector with an affordable and accurate means for forest management and planning. Most of the field surveyed trees could be automatically and correctly detected, especially for the spruce and larch plots, but the complexity of the deciduous plots hindered the tree recognition approach, leading to poor crown extent and gap estimations. Indicators of light availability were calculated from the lidar data by calculation of laser hit penetration rates and percentage canopy cover. These results were compared to estimates of canopy openness obtained from hemispherical pictures for the same locations.Finally, the synergistic benefits of all datasets were evaluated and the forest structural variables determined from remote sensing and hemispherical photography were examined as indicators of light availability for regenerating seedlings

Analysis of the effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

UK legislation aims to conserve and enhance biological diversity within the UK and so accurate measurements of forest biodiversity are important to assess efficacy of management activities in this context. Forest structural diversity metrics can be used as indicators of biodiversity and airborne LiDAR data provide a means of producing these metrics. Forest structure metrics derived from LiDAR can be significantly affected by the canopy conditions the datasets are collected under. Existing studies have combined and compared leaf-on and leaf-off LiDAR datasets in existing analyses, however the majority of these utilise field sites where climate, species and terrain are very different to those found in the UK. Additionally, studies comparing leaf-on and leaf-off LiDAR over forested areas assess the changes in pulse penetration through the canopy and how this effects forest structure metrics and not the effect on modelled forest structure diversity. The novel aim of this research is to assess and compare the accuracy of forest structural diversity modelled from two LiDAR surveys collected under leaf-on and leaf-off conditions, and do so in a UK forest environment. A robust methodology for correcting the absolute and relative accuracy between datasets was adopted and comparative analysis of ground detection and return height metrics (maximum, mean and percentiles of return height) and return height diversity (L-CV, CV, kurtosis, standard deviation, skewness and variance) was undertaken. Regression models describing the field tree size diversity measurements were constructed using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Both surveys were consistently effected by growth and differing survey parameters making the isolation and assessment of the effects of seasonal change difficult. Despite this, models created using diversity variables from both LiDAR datasets were generally very similar. Both leaf-on and leaf-off LiDAR dataset models described 65% of the variance in tree height diversity (R² 0.65, RMSE 0.05, p <0.0001), however models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.68, 0.41 and 0.19 respectively and leaf-off models 0.71, 0.62 and 0.26 respectively). When diversity variables calculated from both LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity their efficacy was increased (R² of 0.77 for tree height diversity and 0.72 for DBH diversity). The results suggest strongly that tree height diversity models derived from airborne LiDAR collected (and where appropriate combined) under any seasonal conditions can be used to differentiate between single and multiple storey UK forest structure with confidence. However, leaf-off LiDAR acquisitions can generate models with the ability to better explain the diversity of crown shapes in a forest stand than leaf-on, with no improvement in model performance when the two are combined

Uncertainty in parameterizing floodplain forest friction for natural flood management, using remote sensing

One potential Natural Flood Management (NFM) option is floodplain reforestation or manage existing riparian forests, with a view to increasing flow resistance and attenuate flood hydrographs. However, the effectiveness of floodplain forests as resistance agents, during different magnitude overbank floods, has yet to be appropriately parameterized for hydraulic models. Remote sensing offers high-resolution datasets capable of characterizing vegetation structure from a variety of platforms, but they contain uncertainty. For the first time, we demonstrate uncertainty propagation in remote sensing derivations of complex vegetation structure through roughness prediction and floodplain flow for extreme flows and different forest types (young and old Poplar plantations, young and old Pine plantations, and an unmanaged riparian forest). The lowest uncertainties resulted from terrestrial and airborne lidar, where airborne lidar is currently best at defining canopy leaf area, but more research is needed to determine wood area. Mean literature uncertainties in stem density, trunk diameter, wood, and leaf area indices (20, 10, 30, 20%, respectively) resulted in a combined Manning’s n uncertainty from 11–13% to 11–17% at 2 m to 8 m flow depths. This equates to 7–8% roughness uncertainty per 10% combined forest structure uncertainty. Individually, stem density and trunk diameter uncertainties resulted in the largest Manning’s n uncertainty at all flow depths, especially for flow though Pine plantations. For deeper flows, leaf and woody areas become much more important, especially for unmanaged riparian forests with low canopy morphology. Forest structure errors propagated to flow depth demonstrate that even small flows can change by a decimeter, while deeper flows can change by 40 cm or more. For flow depth, errors in canopy structure are deemed more severe in flows depths beyond 4–6 m. This study highlights the need for lower uncertainty in all forest structure components using remote sensing, to improve roughness parameterization and flood modeling for NFM

The Assessment of habitat condition and consevation status of lowland British woodlands using earth observation techniques.

The successful implementation of habitat preservation and management demands regular and spatially explicit monitoring of conservation status at a range of scales based on indicators. Woodland condition can be described in terms of compositional and structural attributes (e.g. overstorey, understorey, ground flora), evidence of natural turnover (e.g. deadwood and tree regeneration), andanthropogenic influences (e.g.disturbance, damage). Woodland condition assessments are currently conducted via fieldwork, which is hampered by cost, spatial coverage, objectiveness and repeatability.This projectevaluates the ability of airborne remote sensing (RS) techniques to assess woodland condition, utilising a sensor-fusion approach to survey a foreststudy site and develop condition indicators. Here condition is based on measures of structural and compositional diversity in the woodland vertical profile, with consideration of the presence of native species, deadwood, and tree regeneration. A 22 km2 study area was established in the New Forest, Hampshire, UK, which contained a variety of forest types, including managed plantation, semi-ancient coniferous and deciduous woodland. Fieldwork was conducted in 41 field plots located across this range of forest types, each with varying properties. The field plots were 30x30m in size and recorded a total of 39 forest metrics relating to individual elements of condition as identified in the literature. Airborne hyperspectral data (visible and near-infrared) and small footprint LiDAR capturing both discrete-return (DR) and full-waveform (FW) data were acquired simultaneously, under both leaf-on and leaf-off conditions in 2010. For the combined leaf-on and leaf-off datasets a total of 154 metrics were extracted from the hyperspectral data, 187 metrics from the DR LiDAR and 252 metrics from the FW LiDAR. This comprised both area-based and individual tree crown metrics. These metrics were entered into two statistical approaches, ordinary least squares and Akaike information criterion regression, in order to estimate each of the 39 field plot-level forest variables. These estimated variables were then used as inputs to six forest condition assessment approaches identified in the literature. In total, 35 of the 39 field plot-level forest variables could be estimated with a validated NRMSE value below 0.4 using RS data (23 of these models had NRMSE values below 0.3). Over half of these models involved the use of FW LiDAR data on its own or combined with hyperspectral data, demonstrating this to be single most able dataset. Due to the synoptic coverage of the RS data, each of these field plot variables could be estimated and mapped continuously over the entire study site at the 30x30m resolution (i.e. field plot-level scale). The RS estimated field variables were then used as inputs to six forest condition assessment approaches identified in the literature.Three of the derived condition indices were successful based on correspondence with field validation data and woodlandcompartment boundaries. The three successful condition assessment methods were driven primarily by tree size and tree size variation. The best technique for assessing woodland condition was a score-based method which combined seventeen inputs which relate to tree species composition, tree size and variability, deadwood, and understory components; all of whichwere shown to be derived successfully from the appropriate combination of airborne hyperspectral and LiDAR datasets. The approach demonstrated in this project therefore shows that conventional methods of assessing forest condition can be applied with RS derived inputs for woodland assessment purposes over landscape-scale areas

Machine Learning with UAS LiDAR for Winter Wheat Biomass Estimations

peer reviewedAbstract. Biomass is an important indicator in the ecological and management process that can now be estimated at higher temporal and spatial resolutions because of unmanned aircraft systems (UAS). LiDAR sensor technology has advanced enabling more compact sizes that can be integrated with UAS platforms. Its signals are capable of penetrating through vegetation canopies enabling the capture of more information along the plant structure. Separate studies have used LiDAR for crop height, rate of canopy penetrations as related to leaf area index (LAI), and signal intensity as an indicator of plant chlorophyll status or green area index (GAI). These LiDAR products are combined within a machine learning method such as an artificial neural network (ANN) to assess the potential in making accurate biomass estimations for winter wheat

lidR : an R package for analysis of Airborne Laser Scanning (ALS) data

Airborne laser scanning (ALS) is a remote sensing technology known for its applicability in natural resources management. By quantifying the three-dimensional structure of vegetation and underlying terrain using laser technology, ALS has been used extensively for enhancing geospatial knowledge in the fields of forestry and ecology. Structural descriptions of vegetation provide a means of estimating a range of ecologically pertinent attributes, such as height, volume, and above-ground biomass. The efficient processing of large, often technically complex datasets requires dedicated algorithms and software. The continued promise of ALS as a tool for improving ecological understanding is often dependent on user-created tools, methods, and approaches. Due to the proliferation of ALS among academic, governmental, and private-sector communities, paired with requirements to address a growing demand for open and accessible data, the ALS community is recognising the importance of free and open-source software (FOSS) and the importance of user-defined workflows. Herein, we describe the philosophy behind the development of the lidR package. Implemented in the R environment with a C/C++ backend, lidR is free, open-source and cross-platform software created to enable simple and creative processing workflows for forestry and ecology communities using ALS data. We review current algorithms used by the research community, and in doing so raise awareness of current successes and challenges associated with parameterisation and common implementation approaches. Through a detailed description of the package, we address the key considerations and the design philosophy that enables users to implement user-defined tools. We also discuss algorithm choices that make the package representative of the ‘state-of-the-art' and we highlight some internal limitations through examples of processing time discrepancies. We conclude that the development of applications like lidR are of fundamental importance for developing transparent, flexible and open ALS tools to ensure not only reproducible workflows, but also to offer researchers the creative space required for the progress and development of the discipline

Investigating the potential for detecting Oak Decline using Unmanned Aerial Vehicle (UAV) Remote Sensing

This PhD project develops methods for the assessment of forest condition utilising modern remote sensing technologies, in particular optical imagery from unmanned aerial systems and with Structure from Motion photogrammetry. The research focuses on health threats to the UK’s native oak trees, specifically, Chronic Oak Decline (COD) and Acute Oak Decline (AOD). The data requirements and methods to identify these complex diseases are investigatedusing RGB and multispectral imagery with very high spatial resolution, as well as crown textural information. These image data are produced photogrammetrically from multitemporal unmanned aerial vehicle (UAV) flights, collected during different seasons to assess the influence of phenology on the ability to detect oak decline. Particular attention is given to the identification of declined oak health within the context of semi-natural forests and heterogenous stands. Semi-natural forest environments pose challenges regarding naturally occurring variability. The studies investigate the potential and practical implications of UAV remote sensing approaches for detection of oak decline under these conditions. COD is studied at Speculation Cannop, a section in the Forest of Dean, dominated by 200-year-old oaks, where decline symptoms have been present for the last decade. Monks Wood, a semi-natural woodland in Cambridgeshire, is the study site for AOD, where trees exhibit active decline symptoms. Field surveys at these sites are designed and carried out to produce highly-accurate differential GNSS positional information of symptomatic and control oak trees. This allows the UAV data to be related to COD or AOD symptoms and the validation of model predictions. Random Forest modelling is used to determine the explanatory value of remote sensing-derived metrics to distinguish trees affected by COD or AOD from control trees. Spectral and textural variables are extracted from the remote sensing data using an object-based approach, adopting circular plots around crown centres at individual tree level. Furthermore, acquired UAV imagery is applied to generate a species distribution map, improving on the number of detectable species and spatial resolution from a previous classification using multispectral data from a piloted aircraft. In the production of the map, parameters relevant for classification accuracy, and identification of oak in particular, are assessed. The effect of plot size, sample size and data combinations are studied. With optimised parameters for species classification, the updated species map is subsequently employed to perform a wall-to-wall prediction of individual oak tree condition, evaluating the potential of a full inventory detection of declined health. UAV-acquired data showed potential for discrimination of control trees and declined trees, in the case of COD and AOD. The greatest potential for detecting declined oak condition was demonstrated with narrowband multispectral imagery. Broadband RGB imagery was determined to be unsuitable for a robust distinction between declined and control trees. The greatest explanatory power was found in remotely-sensed spectra related to photosynthetic activity, indicated by the high feature importance of nearinfrared spectra and the vegetation indices NDRE and NDVI. High feature importance was also produced by texture metrics, that describe structural variations within the crown. The findings indicate that the remotely sensed explanatory variables hold significant information regarding changes in leaf chemistry and crown morphology that relate to chlorosis, defoliation and dieback occurring in the course of the decline. In the case of COD, a distinction of symptomatic from control trees was achieved with 75 % accuracy. Models developed for AOD detection yielded AUC scores up to 0.98,when validated on independent sample data. Classification of oak presence was achieved with a User’s accuracy of 97 % and the produced species map generated 95 % overall accuracy across the eight species within the study area in the north-east of Monks Wood. Despite these encouraging results, it was shown that the generalisation of models is unfeasible at this stage and many challenges remain. A wall-to-wall prediction of decline status confirmed the inability to generalise, yielding unrealistic results, with a high number of declined trees predicted. Identified weaknesses of the developed models indicate complexity related to the natural variability of heterogenous forests combined with the diverse symptoms of oak decline. Specific to the presented studies, additional limitations were attributed to limited ground truth, consequent overfitting,the binary classification of oak health status and uncertainty in UAV-acquired reflectance values. Suggestions for future work are given and involve the extension of field sampling with a non-binary dependent variable to reflect the severity of oak decline induced stress. Further technical research on the quality and reliability of UAV remote sensing data is also required

An evaluation of LiDAR and optical satellite data for the measurement of structural attributes in British upland conifer plantation forestry

This study evaluates the ability of LiDAR, IKONOS and Landsat ETM+ data to provide estimates of forest structure in British upland conifer plantations. Little use has so far been made of these technologies in the UK, whereas in some other countries remote sensing has become integral to forest management systems. The aim of this thesis is to demonstrate the application of the selected remote sensing systems to provide up-to- date and accurate information on key forest variables such as tree height, volume and density. Two upland conifer areas, located in south-west Scotland and north-east England, were used to develop and validate the regression models used to estimate these forest variables. The ability of LiDAR to provide an accurate measurement of the ground and canopy surfaces was investigated in densely stocked plantations, typical for commercial forestry in the U.K. The results show that, despite the dense nature of the forest canopy, sufficient laser pulses penetrate through to the ground to generate an accurate Digital Terrain Model (DTM). Provided that the ground surface is accurately defined, a point density of 2 returns/m(^2) will enable measurement of tree height to be made. LiDAR-derived top heights were found to be as accurate as field-based measurements (RMSE of 0.57 m). LiDAR-derived top height is easily integrated with established Forestry Commission models to provide volume estimations. Tree density is not accurately estimated using LiDAR data (RMSE of 434 trees/ha). Results strongly suggest that predictive equations developed for top height can be transferred to other conifer forests. Furthermore, the relationship between field-measured top height and laser-derived top height appears to be stable across different conifer species. LiDAR data can be used to identify tree species in pure and mixed stands. Two methods were developed: the first used summary measures based on the laser height distribution and the second the near infrared intensity. These measures when mapped spatially can be used to classify areas by species and to identify areas of anomalous growth and wind damage. At a larger spatial scale. Landsat ETM+ and IKONOS data can provide height estimates up to the point of canopy closure (approximately 10 m). LiDAR-derived height can be used in place of field-based measurements to drive reflectance-based models to estimate height from optical satellite data. The methods developed are transferable to other conifer forests that are managed in a similar way. The results from this thesis show that LİDAR, IKONOS and Landsat ETM+ data provide valuable and complementary information at a_ range of scales and can assist managers to make more informed resource management decisions

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications