This article describes the first published quantum algorithm for the analysis of temporal sequences of satellite images. This algorithm can be used for the purpose of detecting deforestation in tropical forests. We achieved a reasonable accuracy on a quantum simulator even though we had a relatively small number of parameters. The algorithm we propose opens promising avenues for the remote sensing community when quantum computing technologies will become widely available.

The continued increase of anthropogenic pressure on the Earth's ecosystems is degrading the natural environ-ment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products - and there are many available - are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system ar-chitecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users' specific needs would fundamentally change the relationship between users and land cover products - requiring more government support to make these systems a reality.

The observation of global-scale land cover (LC) is of importance to international initiatives such as the United Nations Framework Convention on Climate Change (UNFCCC) and Kyoto protocol, governments, and scientific communities in their understanding and monitoring of the changes affecting the environment, and the coordination of actions to mitigate and adapt to global change. As such, reliable and consistent global LC (GLC) datasets are being sought. For instance, GLC datasets are used as an input for many Global Circulation Models, Earth Systems Models and Integrated Assessment Models used for global and regional climate simulations, dynamic vegetation modelling, carbon (stock) modelling, ecosystem modelling, land surface modelling, and impact assessments (Hibbard et al. 2010; Herold et al. 2011). The selection of GLC datasets and their quality have a significant influence on the outcomes of these models (Hibbard et al. 2010; Nakaegawa 2011). However, the existing GLC datasets are often selected without considering their quality and suitability for a specific application (Verburg et al. 2011). This is due, notably, to the lack of interoperability and inter-comparability between the datasets (Jung et al. 2006; Herold et al. 2008). Uncertainties of LC datasets also result in considerable differences in modelling outcomes (Hibbard et al. 2010; Nakaegawa 2011; Verburg et al. 2011). For instance, Benitez et al. (2004) have noted that the choice of GLC dataset influenced the model results by as much as 45 %. Moreover, lower

Obtaining information on vertical forest structure requires detailed data acquisition and analysis which is often performed at a plot level. With the growing availability of multi-modal satellite remote sensing (SRS) datasets, their usability towards forest structure estimation is increasing. We assessed the relationship of PlanetScope-, Sentinel-2-, and Landsat-7-derived vegetation indices (VIs), as well as ALOS-2 PALSAR-2- and Sentinel-1-derived backscatter intensities with a terrestrial laser scanner (TLS) and conventionally measured forest structure parameters acquired from 25 field plots in a tropical montane cloud forest in Kafa, Ethiopia. Results showed that canopy gap-related forest structure parameters had their highest correlation (|r| = 0.4 − 0.48) with optical sensor-derived VIs, while vegetation volume-related parameters were mainly correlated with red-edge- and short-wave infrared band-derived VIs (i.e., inverted red-edge chlorophyll index (IRECI), normalized difference moisture index), and synthetic aperture radar (SAR) backscatters (|r| = −0.57 − 0.49). Using stepwise multi-linear regression with the Akaike information criterion as evaluation parameter, we found that the fusion of different SRS-derived variables can improve the estimation of field-measured structural parameters. The combination of Sentinel-2 VIs and SAR backscatters was dominant in most of the predictive models, while IRECI was found to be the most common predictor for field-measured variables. The statistically significant regression models were able to estimate cumulative plant area volume density with an R2 of 0.58 and with the lowest relative root mean square error (RRMSE) value (0.23). Mean gap and number of gaps were also significantly estimated, but with higher RRMSE (R2 = 0.52, RRMSE = 1.4, R2 = 0.68, and RRMSE = 0.58, respectively). The models showed poor performance in predicting tree density and number of tree species (R2 = 0.28, RRMSE = 0.41, and R2 = 0.21, RRMSE = 0.39, respectively). This exploratory study demonstrated that SRS variables are sensitive to retrieve structural differences of tropical forests and have the potential to be used to upscale biodiversity relevant field-based forest structure estimates.

Increasing anthropogenic pressure leads to loss of habitat through deforestation and degradation in tropical forests. While deforestation can be monitored relatively easily, forest management practices are often subtle processes, that are difficult to capture with for example satellite monitoring. Conventional measurements are well established and can be useful for management decisions, but it is believed that Terrestrial Laser Scanning (TLS) has a role in quantitative monitoring and continuous improvement of methods. In this study we used a combination of TLS and conventional forest inventory measures to estimate forest structural parameters in four different forest types in a tropical montane cloud forest in Kafa, Ethiopia. Here, the four forest types (intact forest, coffee forest, silvopasture, and plantations) are a result of specific management practices (e.g. clearance of understory in coffee forest), and not different forest communities or tree types. Both conventional and TLS derived parameters confirmed our assumptions that intact forest had the highest biomass, silvopasture had the largest canopy gaps, and plantations had the lowest canopy openness. Contrary to our expectations, coffee forest had higher canopy openness and similar biomass as silvopasture, indicating a significant loss of forest structure. The 3D vegetation structure (PAVD - Plant area vegetation density) was different between the forest types with the highest PAVD in intact forest and plantation canopy. Silvopasture was characterised by a low canopy but high understorey PAVD, indicating regeneration of the vegetation and infrequent fuelwood collection and/or non-intensive grazing. Coffee forest canopy had low PAVD, indicating that many trees had been removed, despite coffee needing canopy shade. These findings may advocate for more tangible criteria such as canopy openness thresholds in sustainable coffee certification schemes. TLS as tool for monitoring forest structure in plots with different forest types shows potential as it can capture the 3D position of the vegetation volume and open spaces at all heights in the forest. To quantify changes in different forest types, consistent monitoring of 3D structure is needed and here TB is an add-on or an alternative to conventional forest structure monitoring. However, for the tropics, TLS-based automated segmentation of trees to derive DBH and biomass is not widely operational yet, nor is species richness determination in forest monitoring. Integration of data sources is needed to fully understand forest structural diversity and implications of forest management practices on different forest types.

Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.

Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.

Tropical forests host at least two-thirds of the world’s flora and fauna diversity and store 25% of the terrestrial above and belowground carbon. However, biodiversity decline due to deforestation and forest degradation of tropical forest is increasing at an alarming rate. Biodiversity dynamics due to natural and anthropogenic disturbances are mainly monitored using established field survey approaches. However, such approaches appear to fall short at addressing complex disturbance factors and responses. We argue that the integration of state-of-the-art monitoring approaches can improve the detection of subtle biodiversity disturbances and responses in changing tropical forests, which are often data-poor. We assess the state-of-the-art technologies used to monitor biodiversity dynamics of changing tropical forests, and how their potential integration can increase the detail and accuracy of biodiversity monitoring. Moreover, the relevance of these biodiversity monitoring techniques in support of the UNCBD Aichi targets was explored using the Essential Biodiversity Variables (EBVs) as a framework. Our review indicates that although established field surveys were generally the dominant monitoring systems employed, the temporal trend of monitoring approaches indicates the increasing application of remote sensing and in -situ sensors in detecting disturbances related to agricultural activities, logging, hunting and infrastructure. The relevance of new technologies (i.e., remote sensing, in situ sensors, and DNA barcoding) in operationalising EBVs (especially towards the ecosystem structure, ecosystem function, and species population classes) and the Aichi targets has been assessed. Remote sensing application is limited for EBV classes such as genetic composition and species traits but was found most suitable for ecosystem structure class. The complementarity of remote sensing and emerging technologies were shown in relation to EBV candidates such as species distribution, net primary productivity, and habitat structure. We also developed a framework based on the primary biodiversity attributes, which indicated the potential of integration between monitoring approaches. In situ sensors are suitable to help measure biodiversity composition, while approaches based on remote sensing are powerful for addressing structural and functional biodiversity attributes. We conclude that, synergy between the recent biodiversity monitoring approaches is important and possible. However, testing the suitability of monitoring methods across scales, integrating heterogeneous monitoring technologies, setting up metadata standards, and making interpolation and/or extrapolation from observation at different scales is still required to design a robust biodiversity monitoring system that can contribute to effective conservation measures.

This chapter aims to assist biodiversity observation networks across the world in coordinating comprehensive freshwater biodiversity observations at national, regional or continental scales. We highlight special considerations for freshwater biodiversity and methods and tools available for monitoring. We also discuss options for storing, accessing, evaluating and reporting freshwater biodiversity data and for ensuring their use in making decisions about the conservation and sustainable management of freshwater biodiversity and provision of ecosystem services.

The planned construction of hundreds of hydroelectric dams in the Amazon basin has the potential to provide invaluable ‘clean’ energy resources for aiding in securing future regional energy needs and continued economic growth. These mega-structures, however, directly and indirectly interfere with natural ecosystem dynamics, and can cause noticeable tree loss. To improve our understanding of how hydroelectric dams affect the surrounding spatiotemporal patterns of forest disturbances, this case study integrated remote sensing spectral mixture analysis, GIS proximity analysis and statistical hypothesis testing to extract and evaluate spatially-explicit patterns of deforestation (clearing of entire forest patch) and forest degradation (reduced tree density) in the 80,000 km2 neighborhoods of the Brazil's Tucuruí Dam, the first large-scale hydroelectric project in the Amazon region, over a period of 25 years from 1988 to 2013. Results show that the average rates of deforestation were consistent during the first three time periods 1988–1995 (620 km2 per year), 1995–2001 (591 km2 per year), and 2001–2008 (660 km2 per year). However, such rate dramatically fell to half of historical levels after 2008, possibly reflecting the 2008 global economic crisis and enforcement of the Brazilian Law of Environmental Crimes. The rate of forest degradation was relatively stable from 1988 to 2013 and, on average, was 17.8% of the rate of deforestation. Deforestation and forest degradation were found to follow similar spatial patterns across the dam neighborhoods, upstream reaches or downstream reaches at the distances of 5 km–80 km, suggesting that small and large-scale forest disturbances may have been influencing each other in the vicinity of the dam. We further found that the neighborhoods of the Tucuruí Dam and the upstream region experienced similar degrees of canopy loss. Such loss was mainly attributed to the fast expansion of the Tucuruí town, and the intensive logging activities alongside major roads in the upstream reservoir region. In contrast, a significantly lower level of forest disturbance was discovered in the downstream region.

The observation of global-scale land cover (LC) is of importance to international initiatives such as the United Nations Framework Convention on Climate Change (UNFCCC) and Kyoto protocol, governments, and scientific communities in their understanding and monitoring of the changes affecting the environment, and the coordination of actions to mitigate and adapt to global change. As such, reliable and consistent global LC (GLC) datasets are being sought. For instance, GLC datasets are used as an input for many Global Circulation Models, Earth Systems Models and Integrated Assessment Models used for global and regional climate simulations, dynamic vegetation modelling, carbon (stock) modelling, ecosystem modelling, land surface modelling, and impact assessments (Hibbard et al., 2010; Herold et al., 2011).

Leading species at the forest stand level is a required forest inventory attribute. Information regarding leading species enables the calculation of volume and biomass in support of forest monitoring and reporting activities. In this study, approaches for leading species estimation based upon very high spatial resolution (pixel sided <1 m) have been developed and implemented, with opportunities for improving attribute accuracy using data fusion methods. Over a study region located in the Yukon Territory, Canada, we apply the Dempster-Shafer Theory (DST) to integrate multiple resolutions of satellite imagery (including spatial and spectral), topographic information, and fire disturbance history records for the estimation of leading species.Among the data source combinations tested in the study, the QuickBird panchromatic combined with selected optical channels from Landsat-5 Thematic Mapper (TM) imagery provided the highest overall accuracy (70.4%) for identifying leading species and improved the accuracy by 3.1% over a baseline from a classification-tree based method applied on all data sources. Additional insights to the application of DST to fuse satellite imagery with ancillary data sources to map leading stand species in a boreal environment are also elaborated upon, including the range and distribution of training data and DST mass function establishment. (c) 2012 Elsevier B.V. All rights reserved.

Many areas of forest across northern Canada are challenging to monitor on a regular basis as a result of their large extent and remoteness. Although no forest inventory data typically exist for these northern areas, detailed and timely forest information for these areas is required to support national and international reporting obligations. We developed and tested a sample-based approach that could be used to estimate forest stand height in these remote forests using panchromatic Very High Spatial Resolution (VHSR, < 1 m) optical imagery and light detection and ranging (lidar) data. Using a study area in central British Columbia, Canada, to test our approach, we compared four different methods for estimating stand height using stand-level and crown-level metrics generated from the VHSR imagery. ‘Lidar plots’ (voxel-based samples of lidar data) are used for calibration and validation of the VHSR-based stand height estimates, similar to the way that field plots are used to calibrate photogrammetric estimates of stand height in a conventional forest inventory or to make empirical attribute estimates from multispectral digital remotely sensed data. A k-nearest neighbours (k-NN) method provided the best estimate of mean stand height (R 2 = 0.69; RMSE = 2.3 m, RMSE normalized by the mean value of the estimates (RMSE-%) = 21) compared with linear regression, random forests, and regression tree methods. The approach presented herein demonstrates the potential of VHSR panchromatic imagery and lidar to provide robust and representative estimates of stand height in remote forest areas where conventional forest inventory approaches are either too costly or are not logistically feasible. While further evaluation of the methods is required to generalize these results over Canada to provide robust and representative estimation, VHSR and lidar data provide an opportunity for monitoring in areas for which there is no detailed forest inventory information available.

Plot-based sampling with ground measurements or photography is typically used to establish and maintain National Forest Inventories (NFI). The re-measurement phase of the Canadian NFI is an opportunity to develop novel methods for the estimation of forest attributes such as stand height, crown closure, volume, and aboveground biomass (AGB) from satellite, rather than, airborne imagery. Based on panchromatic Very High Spatial Resolution (VHSR) images and Light Detection and Ranging (LiDAR) data acquired in the Yukon Territory, Canada, we propose an approach for boreal forest stand attribute characterization. Stand and tree objects are delineated, followed by modeling of stand height, volume, and AGB using metrics derived from the stand and tree crown objects. The calibration and validation of the models are based on co-located LiDAR-derived estimates. A k-nearest neighbor approach provided the best accuracy for stand height estimation (R2 = 0.76, RMSE = 1.95 m). Linear regression models were the most efficient for estimating stand volume (R2 = 0.94, RMSE = 9.6 m3/ha) and AGB (R2 = 0.92, RMSE = 22.2 t/ha). This study was implemented for one Canadian ecozone and demonstrated the capacity of a methodology to produce forest inventory attributes with acceptable accuracies offering potential to be applied to other boreal regions.

A workshop entitled 'REDD+ measuring, reporting and verification science solutions to policy challenges' was organized by the WWF Forest and Climate Initiative, WWF Netherlands and Wageningen University REDD@WUR network from 10th to 12th June 2013 in Zeist, The Netherlands. The purpose of this workshop was to assess the status and development of monitoring approaches in light of the evowlving REDD+ measuring, reporting and verification needs from different actors in the REDD+ measuring, reporting and verification process. Accordingly, the most important gaps were identified and led to the development of research priorities with focus on better linking local and national REDD+ efforts on five themes, namely: monitoring and measurement; reporting and verification; reference levels; measuring, reporting and verification of safeguards; and benefit sharing.

We summarize the capacity of high spatial resolution (< 1 m) digital aerial imagery to support forest health monitoring. We review the current use of digital aerial imagery in the context of the recent mountain pine beetle epidemic in western Canada. Supported by this review, we posit that high spatial resolution digital aerial imagery can play at least two critical roles in forest health monitoring. First, the capacity to characterize damage at the individual tree level directly supports a broad range of forest health information needs (e. g., tree-level attributes for estimating the population at risk and for inputs to models, estimates of mortality, rates of population growth). Second, the level of detail afforded by the digital high spatial resolution aerial imagery provides critical calibration and validation data for lower spatial resolution remotely sensed imagery (e. g., QuickBird, Landsat) for large-area detection and mapping of forest damage and can be used in a double sampling scheme as a bridge between detailed field measures and landscape-level estimates of mortality. In an era with increasing numbers of commercially deployed sensors capable of acquiring high spatial resolution satellite imagery, the flexibility and cost-effectiveness of aerial image options should not be disregarded. Moreover, experiences with airborne imagery can continue to inform applications using high spatial resolution satellite imagery for forest health information needs. (C) 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). [DOI: 10.1117/1.JRS.6.062527]

This study confirmed the ability of the Dempster–Shafer theory (DST) and the Dezert–Smarandache (Free DSm model) theory to significantly improve the quality of maps of regenerating forest stands in southern Quebec, Canada compared to a classical Maximum Likelihood Algorithm (MLA). The proposed approach uses data fusion methods that allow the integration of remotely sensed imagery with conventional maps of ecophysiographic features. While the MLA provided an overall accuracy of 82.75%, the DST and Free DSm models had overall accuracies of 90.14% and 91.13% respectively. In addition, this study showed that the data fusion methods can model the influence of biophysical parameters (e.g., surface deposits and drainage) on the growth potential of regenerating forest stands. This study illustrates the importance of the mass function allocation for each ancillary data source. We found that a Bayesian belief configuration provided results equivalent to those obtained when representing data uncertainty. This demonstrates the difficulty in modelling uncertainty associated with each ancillary source.

In Canada, forest companies and government are faced with the important challenge of monitoring forest stand health. This task is especially difficult when the objective is to monitor the health of mature deciduous stands. Mapping methods for tree health have been proposed using multispectral and hyperspectral airborne sensors; however, acquiring airborne data over large areas remains costly. In addition, some studies have pointed out that forest dieback is characterized by multi-causality. Therefore, we propose a large-scale mapping method which includes a model to parameterize several factors influencing forest vigour. A high-spatial resolution satellite image was fused with a series of biophysical parameters using the Dempster–Shafer theory (DST). The study was performed over mature deciduous forests in the province of Québec, Canada. The fusion of a Satellite Pour l’Observation de la Terre (SPOT)-5 high resolution geometric (HRG) image with a surface deposit map and an ice storm damage-intensity map provided the best results, improving the overall accuracy by 15.1%, when compared with a K‐nearest-neighbour (KNN) algorithm using the SPOT-5 image alone. Moreover, the DST improved the accuracy of the vigour class identification, halving the standard deviation when compared with the KNN method. This study shows how the DST can be used to model the influence of biophysical parameters when combined with multispectral information to better assess the health of mature deciduous stands.

Mean stand height is an important parameter for forest volume and biomass estimation in support of monitoring and management activities. Information on mean stand height is typically obtained through the manual interpretation of aerial photography, often supplemented by the collection of field calibration data. In remote areas where forest management practices may not be spatially exhaustive or where it is difficult to acquire aerial photography, alternate approaches for estimating stand height are required. One approach is to use very high spatial resolution (VHSR) satellite imagery (pixels sided less than 1 m) as a surrogate for air photos. In this research we demonstrate an approach for modelling mean stand height at four sites in the Yukon Territory, Canada, from QuickBird panchromatic imagery. An object-based approach was used to generate homogenous segments from the imagery (analogous to manually delineated forest stands) and an algorithm was used to automatically delineate individual tree crowns within the segments. A regression tree was used to predict mean stand height from stand-level metrics generated from the image grey-levels and within-stand objects relating individual tree crown characteristics. Heights were manually interpreted from the QuickBird imagery and divided into separate sets of calibration and validation data. The effects of calibration data set size and the input metrics used on the regression tree results were also assessed. The approach resulted in a model with a significant R2 of 0.53 and an RMSE of 2.84 m. In addition, 84.6% of the stand height estimates were within the acceptable error for photo interpreted heights, as specified by the forest inventory standards of British Columbia. Furthermore, residual errors from the model were smallest for the stands that had larger mean heights (i.e., > 20 m), which aids in reducing error in subsequent estimates of biomass or volume (since stands with larger trees contribute more to overall estimates of volume or biomass). Estimated and manually interpreted heights were reclassified into 5-metre height classes (a schema frequently used for forest analysis and modelling applications) and compared; classes corresponded in 54% of stands assessed, and all stands had an estimated height class that was within ± 1 class of their actual class. This study demonstrates the capacity of VHSR panchromatic imagery (in this case QuickBird) for generating useful estimates of mean stand heights in unmonitored, remote, or inaccessible forest areas.