Recent (1986-2006) Vegetation-Specific NDVI Trends in Northern Canada from Satellite Data

Recent northern vegetation changes caused by climate warming have been well documented, using experimental plot warming to examine vegetation-specific changes and satellite image data to examine overall trends. Previous remote sensing efforts have employed the Normalized Difference Vegetation Index (NDVI) from AVHRR, whose 1 km to 8 km pixel size is too large for examination of broad scale vegetation-specific responses because of mixing within the pixel footprint. In this paper, we reconcile differences between field- and remote sensing-based approaches by using both medium-resolution (30 m) and coarse resolution (1 km) data to study 20 years of vegetation-specific responses to northern climate warming (1986 to 2006). Trends are compared among vegetation communities from two separate Landsat classifications in Canada’s eastern and western forest-tundra transition zone, as well as a 1 km AVHRR database recently developed over Canada. A comparison of absolute trends among mapped vegetation communities revealed lichen-dominated communities consistently exhibiting lower trends than those dominated by vascular plants, with both exhibiting increasing NDVI. Our results and those obtained from experimental warming suggest that the magnitude difference in NDVI increase between lichen and vascular vegetation is related to increasing vigor and biomass of vascular vegetation, in contrast to physiological impairment of lichen due to the short-term secondary effect of temperature on moisture. In the longer term, succession from lichen to vascular is likely responsible for the small observed NDVI increase over lichen-dominated regions. The fact that both Landsat and AVHRR exhibited similar relative vegetation-specific trends in NDVI suggests that this phenomenon may be widespread in the North.CCes derniers temps, les changements sur la végétation dans le Nord causés par le réchauffement climatique ont été bien documentés grâce à une parcelle expérimentale faisant l’objet d’un réchauffement qui permet d’examiner les changements propres à la végétation, ainsi que grâce à des données et images obtenues par satellite permettant d’examiner les tendances générales. Les travaux de télédétection antérieurs recouraient à l’indice d’activité végétale obtenu à partir d’un radiomètre perfectionné à très haute résolution (AVHRR), dont la taille de pixel de 1 km à 8 km est trop grande pour permettre l’examen des réactions à grande échelle de la végétation en raison du mélangeage dans la zone de couverture des pixels. Dans cette communication, nous faisons le rapprochement des différences entre les méthodes de prélèvement de données sur le terrain et les méthodes de prélèvement des données par télédétection en recourant à des données à moyenne résolution (30 m) et à des données à résolution grossière (1 km) dans le but d’étudier les réactions de la végétation échelonnées sur 20 ans dans le cadre du réchauffement climatique dans le Nord (de 1986 à 2006). Les tendances sont comparées entre les diverses communautés végétales à partir de deux classifications Landsat distinctes dans la zone de transition forêt-toundra de l’est et de l’ouest du Canada, ainsi qu’à partir d’une banque de données prélevées au Canada à l’aide d’un radiomètre perfectionné à très haute résolution de 1 km récemment mis au point. La comparaison des tendances absolues parmi les communautés végétales mappées a révélé des communautés dominées par le lichen affichant constamment des tendances moins élevées que les communautés dominées par les plantes vasculaires, toutes deux présentant un indice d’activité végétale accrue. Nos résultats et ceux obtenus dans le cadre du réchauffement expérimental laissent croire que la différence de magnitude en ce qui a trait à l’accroissement de l’indice d’activité végétale entre la végétation de lichen et la végétation vasculaire se rapporte à l’accroissement de la vigueur et de la biomasse de la végétation vasculaire, par contraste avec l’altération physiologique du lichen attribuable à l’effet secondaire à court terme de la température sur l’humidité. À plus long terme, la succession du lichen aux plantes vasculaires est vraisemblablement responsable des petites augmentations observées au titre de l’indice d’activité végétale dans les régions dominées par le lichen. Le fait que le Landsat et l’AVHRR aient tous deux permis de dénoter des tendances relatives semblables du point de vue de la végétation et de l’indice d’activité végétale laisse entendre que ce phénomène peut être étendu dans le Nord

Relating Biomass and Leaf Area Index to Non-destructive Measurements in Order to Monitor Changes in Arctic Vegetation

This paper reports an alternative method for seasonal and long-term monitoring of biomass and the leaf area index (LAI) at Arctic tundra sites. Information related to the historical and projected change in abundance and distribution of biomass and LAI is required to address numerous environmental and resource management issues. Observations of earth from satellites could potentially be used to derive seasonal and long-term changes in biomass and the LAI. To realize this potential, seasonal and long-term ground monitoring data for validation are essential; however, the conventional destructive sampling method for measuring biomass and the LAI does not allow repetitive measurements at the same plots and thus is not suitable for monitoring change over time. Alternative methods, such as sampling nearby similar plots, can be laborious and easily subject to large sampling errors, especially in Arctic tundra sites with low vegetation cover. In this study, we developed a practical method for relating non-destructive measurements (percent cover and mean height) to biomass and the LAI for 13 major Arctic plant groups, or seven plant functional types, on the basis of measurements at 196 plots across Canada’s Arctic tundra ecosystems. Using the method at the plant group level to estimate plot total vascular aboveground biomass, foliage biomass, and LAI, we had r2 = 0.91–0.95 and relative mean absolute error of 25–29%. By this method, one could monitor seasonal and long-term changes in biomass and the LAI through repeated, non-destructive observations of percent cover and mean height at the same permanent plots.Cette communication présente une méthode de rechange en vue de la surveillance saisonnière et à long terme de la biomasse et de l’indice de surface foliaire (LAI) de sites de toundra de l’Arctique. Afin de relever divers enjeux relatifs à la gestion de l’environnement et des ressources, il faut recueillir des données se rapportant au changement historique et projeté en matière d’abondance et de répartition de la biomasse et du LAI. On pourrait éventuellement recourir aux observations de la Terre à partir de satellites afin de déceler les changements saisonniers et à long terme caractérisant la biomasse et le LAI. Pour en arriver là, il est essentiel de disposer de données saisonnières et à long terme au sol à des fins de validation. Cependant, la méthode d’échantillonnage destructeur classique permettant de mesurer la biomasse et le LAI ne permettent pas la prise de mesures répétitives aux mêmes sites et par conséquent, elle ne convient pas à la surveillance du changement qui s’exerce au fil du temps. D’autres méthodes, telles que l’échantillonnage de sites semblables dans les environs, peuvent s’avérer laborieuses et facilement faire l’objet d’importantes erreurs d’échantillonnage, surtout aux sites de toundra de l’Arctique dont la couverture végétale est basse. Dans le cadre de cette étude, nous avons mis au point une méthode pratique pour établir un rapport entre les mesures non destructives (pourcentage de couverture et hauteur moyenne) et la biomasse et le LAI de 13 groupes végétaux importants de l’Arctique, ou sept types végétaux fonctionnels en fonction de la mesure de 196 sites à la grandeur des écosystèmes de toundra de l’Arctique canadien. En nous appuyant sur la méthode des groupes végétaux pour estimer la biomasse vasculaire totale à ciel ouvert des sites, la biomasse foliaire et le LAI, nous avions r2 = 0,91–0,95 et une erreur absolue relative moyenne de 25 à 29%. Au moyen de cette méthode, il serait possible de surveiller les changements saisonniers et à long terme en matière de biomasse et de LAI grâce à des observations répétées et non destructives du pourcentage de la couverture et de la hauteur moyenne aux mêmes sites permanents

Mapping Seasonal Inundation Frequency (1985–2016) along the St-John River, New Brunswick, Canada using the Landsat Archive

Extreme flood events in recent years in Canada have highlighted the need for historical information to better manage future flood risk. In this paper, a methodology to generate flood maps from Landsat to determine historical inundation frequency is presented for a region along the St-John River, New Brunswick, Canada that experiences annual springtime flooding from snowmelt and river ice. 1985–2016 Landsat data from the USGS archive were classified by combining See5 decision trees to map spectrally variable water due to spring ice and sediment, and image thresholding to map inundated floodplains. Multiple scenes representing each year were overlaid to produce seasonal time-series of spring (March–May) and summer (June–August) maximum annual water extents. Comparisons of annual surface water maps were conducted separately for each season against historical hydrometric water depth as a measure of relative springtime flood severity, and 1 m water masks from digital orthophotos were used to perform a formal accuracy assessment of summer water. Due to Landsat’s 16-day revisit time, peak flood depth was poorly related to flood extent; however, spring depth measured during Landsat acquisitions was significantly related to extent (tau = 0.6, p-value < 0.001). Further, summer maps validated against 30 m water fractions scaled from 1 m water masks were over 97% accurate. Limitations with respect to the assessment of flood extent from depth, timing differences between peak flood depth and extent due to Landsat revisit time and cloud cover, and suggestions to overcome limitations through multi-sensor integration including radar are discussed

Comparing Landsat and RADARSAT for Current and Historical Dynamic Flood Mapping

Mapping the historical occurrence of flood water in time and space provides information that can be used to help mitigate damage from future flood events. In Canada, flood mapping has been performed mainly from RADARSAT imagery in near real-time to enhance situational awareness during an emergency, and more recently from Landsat to examine historical surface water dynamics from the mid-1980s to present. Here, we seek to integrate the two data sources for both operational and historical flood mapping. A main challenge of a multi-sensor approach is ensuring consistency between surface water mapped from sensors that fundamentally interact with the target differently, particularly in areas of flooded vegetation. In addition, automation of workflows that previously relied on manual interpretation is increasingly needed due to large data volumes contained within satellite image archives. Despite differences between data received from both sensors, common approaches to surface water and flooded vegetation mapping including multi-channel classification and region growing can be applied with sensor-specific adaptations for each. Historical open water maps from 202 Landsat scenes spanning the years 1985–2016 generated previously were enhanced to improve flooded vegetation mapping along the Saint John River in New Brunswick, Canada. Open water and flooded vegetation maps were created over the same region from 181 RADARSAT 1 and 2 scenes acquired between 2003–2016. Comparisons of maps from different sensors and hydrometric data were performed to examine consistency and robustness of products derived from different sensors. Simulations reveal that the methodology used to map open water from dual-pol RADARSAT 2 is insensitive to up to about 20% training error. Landsat depicts open water inundation well, while flooded vegetation can be reliably mapped in leaf-off conditions. RADARSAT mapped approximately 8% less open water area than Landsat and 0.5% more flooded vegetation, while the combined area of open water and flooded vegetation agreed to within 0.2% between sensors. Derived historical products depicting inundation frequency and trends were also generated from each sensor’s time-series of surface water maps and compared

Detecting Landscape Changes in High Latitude Environments Using Landsat Trend Analysis: 2. Classification

Mapping landscape dynamics is necessary to assess cumulative impacts due to climate change and development in Arctic regions. Landscape changes produce a range of temporal reflectance trajectories that can be obtained from remote sensing image time-series. Mapping these changes assumes that their trajectories are unique and can be characterized by magnitude and shape. A companion paper in this issue describes a trajectory visualization method for assessing a range of landscape disturbances. This paper focusses on generating a change map using a time-series of calibrated Landsat Tasseled Cap indices from 1985 to 2011. A reference change database covering the Mackenzie Delta region was created using a number of ancillary datasets to delineate polygons describing 21 natural and human-induced disturbances. Two approaches were tested to classify the Landsat time-series and generate change maps. The first involved profile matching based on trajectory shape and distance, while the second quantified profile shape with regression coefficients that were input to a decision tree classifier. Results indicate that classification of robust linear trend coefficients performed best. A final change map was assessed using bootstrapping and cross-validation, producing an overall accuracy of 82.8% at the level of 21 change classes and 87.3% when collapsed to eight underlying change processes

An Optimistic AND-parallel Prolog implementation

Bibliography: p. 107-109

Evaluating Simulated RADARSAT Constellation Mission (RCM) Compact Polarimetry for Open-Water and Flooded-Vegetation Wetland Mapping

When severe flooding occurs in Canada, the Emergency Geomatics Service (EGS) is tasked with creating and disseminating maps that depict flood extents in near real time. EGS flood mapping methods were created with efficiency and robustness in mind, to allow maps to be published quickly, and therefore have the potential to generate high-repeat water products that can enhance frequent wetland monitoring. The predominant imagery currently used is synthetic aperture radar (SAR) from RADARSAT-2 (R2). With the commissioning phase of the RADARSAT Constellation Mission (RCM) complete, the EGS is adapting its methods for use with this new source of SAR data. The introduction of RCM’s circular-transmit linear-receive (CTLR) beam mode provides the option to exploit compact polarimetric (CP) information not previously available with R2. The aim of this study was to determine the most effective CP parameters for use in mapping open water and flooded vegetation, using current EGS methodologies, and compare these products to those created by using R2 data. Nineteen quad-polarization R2 scenes selected from three regions containing wetlands prone to springtime flooding were used to create reference flood maps, using existing EGS tools. These scenes were then used to simulate 22 RCM CP parameters at different noise floors and spatial resolutions representative of the three RCM beam modes. Using multiple criteria, CP parameters were ranked in order of importance and entered into a stepwise classification procedure, for evaluation against reference R2 products. The top four CP parameters —m-chi-volume or m-delta-volume, RR intensity, Shannon Entropy intensity (SEi), and RV intensity—achieved a maximum agreement with baseline R2 products of upward of 98% across all 19 scenes and three beam modes. Separability analyses between flooded vegetation and other land-cover classes identified four candidate CP parameters—RH intensity, RR intensity, SEi, and the first Stokes parameter (SV0)—suitable for flooded-vegetation-region growing. Flooded-vegetation-region-growing CP thresholds were found to be dependent on incidence angle for each of these four parameters. After region growing using each of the four candidate CP parameters, RH intensity was deemed best to map flooded vegetation, based on our evaluations. The results of the study suggest a set of suitable CP parameters to generate flood maps from RCM data, using current EGS methodologies that must be validated further as real RCM data become available

ORDER-INDEPENDENT UNIFICATION AND BACKTRACKING

Two algorithms for order-independent unification are presented. Both assume that unifications are timestamped, giving a natural ordering on unifications from lowest timestamp to highest. The first algorithm permits lower-timestamped unifications to be performed after higher-timestamped unifications have already been done, without requiring previous unifications to be backtracked and later redone. The binding state that results is indistinguishable from that produced by a lowest-to-highest execution. The second algorithm extends the first by allowing intermediate unifications to be undone, again avoiding work in undoing and redoing unifications with higher timestamps. These algorithms are designed for distributed, fully AND-parallel Prolog execution; asynchronous events occurring on different CPSs can be reconciled without too much overhead.We are currently acquiring citations for the work deposited into this collection. We recognize the distribution rights of this item may have been assigned to another entity, other than the author(s) of the work.If you can provide the citation for this work or you think you own the distribution rights to this work please contact the Institutional Repository Administrator at [email protected]