Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data

[EN] The use of laser scanning acquired from the air, or ground, holds great potential for the assessment of forest structural attributes, beyond conventional forest inventory. The use of full-waveform airborne laser scanning (ALSFW) data allows for the extraction of detailed information in different vertical strata compared to discrete ALS (ALSD). Terrestrial laser scanning (TLS) can register lower vertical strata, such as understory vegetation, without issues of canopy occlusion, however is limited in its acquisition over large areas. In this study we examine the ability of ALSFW to characterize understory vegetation (i.e. maximum and mean height, cover, and volume), verified using TLS point clouds in a Mediterranean forest in Eastern Spain. We developed nine full-waveform metrics to characterize understory vegetation attributes at two different scales (3.75¿m square subplots and circular plots with a radius of 15¿m); with, and without, application of a height filter to the data. Four understory vegetation attributes were estimated at plot level with high R2 values (mean height: R2¿=¿0.957, maximum height: R2¿=¿0.771, cover: R2¿=¿0.871, and volume: R2¿=¿0.951). The proportion of explained variance was slightly lower at 3.75¿m side cells (mean height: R2¿=¿0.633, maximum height: R2¿=¿0.470, cover: R2¿=¿0.581, and volume R2¿=¿0.651). These results indicate that Mediterranean understory vegetation can be estimated and accurately mapped over large areas with ALSFW. The future use of these types of predictions includes the estimation of ladder fuels, which drive key fire behavior in these ecosystems.This research was developed mainly in the Integrated Remote Sensing Studio (IRSS) of University of British Columbia (UBC) (Canada) as a result of the Erasmus + KA-107 mobility grant. The authors thank the financial support provided by the Spanish Ministerio de Economia y Competitividad and FEDER, in the framework of the project CGL2016-80705-R.Crespo-Peremarch, P.; Tompalski, P.; Coops, N.; Ruiz Fernández, LÁ. (2018). Characterizing understory vegetation in Mediterranean forests using full-waveform airborne laser scanning data. Remote Sensing of Environment. 217:400-413. https://doi.org/10.1016/j.rse.2018.08.033S40041321

Remote sensing technologies for enhancing forest inventories: a review

Forest inventory and management requirements are changing rapidly in the context of an increasingly complex set of economic, environmental, and social policy objectives. Advanced remote sensing technologies provide data to assist in addressing these escalating information needs and to support the subsequent development and parameterization of models for an even broader range of information needs. This special issue contains papers that use a variety of remote sensing technologies to derive forest inventory or inventory-related information. Herein, we review the potential of 4 advanced remote sensing technologies, which we posit as having the greatest potential to influence forest inventories designed to characterize forest resource information for strategic, tactical, and operational planning: airborne laser scanning (ALS), terrestrial laser scanning (TLS), digital aerial photogrammetry (DAP), and high spatial resolution (HSR)/very high spatial resolution (VHSR) satellite optical imagery. ALS, in particular, has proven to be a transformative technology, offering forest inventories the required spatial detail and accuracy across large areas and a diverse range of forest types. The coupling of DAP with ALS technologies will likely have the greatest impact on forest inventory practices in the next decade, providing capacity for a broader suite of attributes, as well as for monitoring growth over time

Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada

Forest inventory and monitoring programs are needed to provide timely, spatially complete (i.e. mapped), and verifiable information to support forest management, policy formulation, and reporting obligations. Satellite images, in particular data from the Landsat Thematic Mapper and Enhanced Thematic Mapper (TM/ETM +) sensors, are often integrated with field plots from forest inventory programs, leveraging the complete spatial coverage of imagery with detailed ecological information from a sample of plots to spatially model forest conditions and resources. However, in remote and unmanaged areas such as Canada's northern forests, financial and logistic constraints can severely limit the availability of inventory plot data. Additionally, Landsat spectral information has known limitations for characterizing vertical vegetation structure and biomass; while clouds, snow, and short growing seasons can limit development of large area image mosaics that are spectrally and phenologically consistent across space and time. In this study we predict and map forest structure and aboveground biomass over 37 million ha of forestland in Saskatchewan, Canada. We utilize lidar plots—observations of forest structure collected from airborne discrete-return lidar transects acquired in 2010—as a surrogate for traditional field and photo plots. Mapped explanatory data included Tasseled Cap indices and multi-temporal change metrics derived from Landsat TM/ETM + pixel-based image composites. Maps of forest structure and total aboveground biomass were created using a Random Forest (RF) implementation of Nearest Neighbor (NN) imputation. The imputation model had moderate to high plot-level accuracy across all forest attributes (R2 values of 0.42–0.69), as well as reasonable attribute predictions and error estimates (for example, canopy cover above 2 m on validation plots averaged 35.77%, with an RMSE of 13.45%, while unsystematic and systematic agreement coefficients (ACuns and ACsys) had values of 0.63 and 0.97 respectively). Additionally, forest attributes displayed consistent trends in relation to the time since and magnitude of wildfires, indicating model predictions captured the dominant ecological patterns and processes in these forests. Acknowledging methodological and conceptual challenges based upon the use of lidar plots from transects, this study demonstrates that using lidar plots and pixel compositing in imputation mapping can provide forest inventory and monitoring information for regions lacking ongoing or up-to-date field data collection programs

Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations

This study aimed to map the stem biomass of an even-aged eucalyptus plantation in southeastern Brazil based on canopy height profile (CHPs) statistics using wall-to-wall discrete return airborne laser scanning (ALS), and compare the results with alternative maps generated by ordinary kriging interpolation from field-derived measurements. The assessment of stem biomass with ALS data was carried out using regression analysis methods. Initially, CHPs were determined to express the distribution of laser point heights in the ALS cloud for each sample plot. The probability density function (pdf) used was the Weibull distribution, with two parameters that in a secondary task, were used as explanatory variables to model stem biomass. ALS metrics such as height percentiles, dispersion of heights, and proportion of points were also investigated. A simple linear regression model of stem biomass as a function of the Weibull scale parameter showed high correlation (adj.R2 = 0.89). The alternative model considering the 30th percentile and the Weibull shape parameter slightly improved the quality of the estimation (adj.R2 = 0.93). Stem biomass maps based on the Weibull scale parameter doubled the accuracy of the ordinary kriging approach (relative root mean square error = 6 % and 13 %, respectively)

Decreasing Net Primary Production in forest and shrub vegetation across southwest Australia

Monitoring changes in the terrestrial carbon cycle and vegetation health can only be undertaken over large areas and on a regular basis using ecological indicators derived from satellite-based sensors. Climate conditions in Mediterranean ecosystems have undergone, and are projected to undergo, significant change in the future with marked impacts on forest and shrubland vegetation. In the southwest of Australia (SWAU), endemic tree species have experienced significant declines in health and mortality since the early 1990s primarily due to these climatic changes. In this paper we examine trends in Net Primary Production (NPP) from 2000 to 2011 as an indicator of productivity and health condition of the woody vegetation across the SWAU region. To do so, we examine NPP estimates derived from satellite imagery and climate data to answer the questions: (1) what is the extent and rate of change in NPP for the SWAU region over the study period, and (2) how important is fire as a contributing factor in the observed trends? Our results suggest that, similar to the global trend in Mediterranean ecosystems, between 2000 and 2011, overall NPP declined across the study region, with the majority of declines occurring in the ecological transition zone between trees and shrubs. Twenty-six percent of the 37,042 square kilometre of woody vegetation that showed a declining NPP trend, was affected by fire. The overall rate of NPP decline for the region was estimated to be -0.38 megaton C per year since 2000, indicating a reduction in the capacity of the region to act as a carbon sink. Under climate change projections, the observed decline trends are likely to continue and our results suggest that the carbon storage potential in this region is gradually decreasing following an ecological shift from tall tree-dominated to lower shrub-dominated vegetation

Identification of de facto protected areas in boreal Canada

Canada is dominated by remote wilderness areas that make important conservation contributions, but are currently only protected de facto by their inaccessibility. Mechanisms for the identification and formal protection of such areas can help ensure that they continue to function naturally and provide essential ecosystem services. However, a lack of spatially explicit, publicly available sources of data on anthropogenic disturbances and natural resource extraction challenges the development of detailed wilderness inventories. We suggest that landscape structure can be used to classify areas of natural landscapes, as trained by the landscape structure of protected areas, and demonstrate this approach by mapping de facto protected areas in Canada's boreal forest. Overall, between 50%, based on landscape structure, and 80%, based on anthropogenic infrastructure alone, of Canada's boreal zone exists in large, intact blocks. The true extent of boreal wilderness likely falls within this range, as existing infrastructure datasets may omit disturbance and the protected area network in far northern areas proved inadequate to train effective wilderness classifications. We anticipate that such efforts may be improved by refining the identification of training areas or by classifying along additional landscape metrics. Nevertheless, the areas identified are valuable candidates for protected area expansion, and can contribute to a reserve network that meets national and regional conservation targets and is representative of the range of vegetation productivities, which was used as a biodiversity surrogate. Our general approach need not be limited to the boreal forest, as it has the potential to successfully identify relatively undisturbed (or less disturbed) areas over a range of systems and across levels of human influence

How do butterflies define ecosystems? A comparison of ecological regionalization schemes

Ecological regionalizations, such as ecoregions or environmental clusters, are often used as coarse filters for conservation. To be effective biodiversity surrogates, regionalizations should contain distinct species assemblages. This condition is not frequently evaluated and regionalizations are rarely assessed comparatively. We used a national dataset of Canadian butterfly collections to evaluate four regionalizations (ecoregions, land cover and productivity regime classifications, and a spatial grid) at two thematic resolutions using analysis of similarity (ANOSIM) and species indicator values. Overall, the spatially constrained schemes (ecoregions and grids) best captured patterns of butterfly community composition and species affinities, indicating that butterfly communities are strongly structured by space at the continent scale. In contrast, when comparing regions only within spatial or environmental neighbourhoods (i.e., comparing between regions that are adjacent along geographic or environmental gradients), all regionalizations performed similarly. Adjacency in environmental space is thus as important as physical adjacency at determining community dissimilarity. Productivity regimes and land cover will be useful biodiversity surrogates when considered in conjunction with space or within a spatially constrained area. This finding was confirmed with two ecoregional case studies (of the Algonquin-Lake Nipissing and Thompson-Okanagan Plateau ecoregions), which also revealed that the relative performance of regionalizations depends upon the context of the study area. We conclude that including species data can improve the efficiency of environmental surrogates for systematic conservation planning

Decreasing net primary production trends in forest and shrub vegetation across southwest Western Australia

Climate conditions in Mediterranean ecosystems have undergone and are projected to undergo significant change in the future. In the southwest of Western Australia, a number of endemic tree species have experienced significant declines in health and mortality since the early 1990s primarily due to these climatic changes. These health declines are likely to have flow-on effects to regional biodiversity as well as the carbon sequestration potential of the woody vegetation in this region. We report on analysis examining trends in Net Primary Production (NPP) of the woody vegetation in the southwest Australia (SWAU) ecoregion from 2000-2011. To do so, we examine NPP estimates derived from satellite imagery and climate data to answer the questions: (1) what is the extent and rate of change in NPP for the SWAU region over the study period, and (2) how important is fire as a contributing factor in the observed trends. Our results suggest that between 2000-2011, overall NPP declined across the region, with the majority of declines occurring in the transition zone between tree-dominated vegetation and shrublands. Fire attributed for just over 25% to the observed declines. The overall rate of NPP decline for the region was estimated to be-0.38 megaton C per year since 2000. Under the current climate change projections, the declining trends are likely to continue and our results suggest that a shift from tree-dominated to lower shrub-dominated vegetation is occurring, gradually decreasing the carbon storage potential in this region

Spatial data, analysis approaches, and information needs for spatial ecosystem service assessments: a review

Operational use of the ecosystem service (ES) concept in conservation and planning requires quantitative assessments based on accurate mapping of ESs. Our goal is to review spatial assessments of ESs, with an emphasis on the socioecological drivers of ESs, the spatial datasets commonly used to represent those drivers, and the methodological approaches used to spatially model ESs. We conclude that diverse strategies, integrating both spatial and aspatial data, have been used to map ES supply and human demand. Model parameters representing abiotic ecosystem properties can be supported by use of well-developed and widely available spatial datasets. Land-cover data, often manipulated or subject to modeling in a GIS, is the most common input for ES modeling; however, assessments are increasingly informed by a mechanistic understanding of the relationships between drivers and services. We suggest that ES assessments are potentially weakened by the simplifying assumptions often needed to translate between conceptual models and widely used spatial data. Adoption of quantitative spatial data that more directly represent ecosystem properties may improve parameterization of mechanistic ES models and increase confidence in ES assessments

Beta-diversity gradients of butterflies along productivity axes

Aim: Several lines of evidence suggest that beta diversity, or dissimilarity in species composition, should increase with productivity: (1) the latitudinal species richness gradient is most closely related to productivity and associated latitudinal beta-diversity relationships have been described, and (2) the scale dependence of the productivity-diversity relationship implies that there should be a positive productivity-beta-diversity relationship. However, such a pattern has not yet been demonstrated at broad scales. We test if there is a gradient of increasing beta diversity with productivity. Location: Canada. Methods: Canada was clustered into regions of similar productivity regimes along three remotely sensed productivity axes (minimum and integrated annual productivity, seasonality of productivity) and elevation. The overall (β j), turnover (β sim) and nestedness (β nes) components of beta diversity within each productivity regime were estimated with pairwise dissimilarity metrics and related to cluster productivity with partial linear regression and with spatial autoregression. Tests were performed for all species, productivity breadth-based subsets (e.g. species occurring in many and a moderate number of productivity regimes), and pre- and post-1970 butterfly records. Beta diversity between adjacent clusters along the productivity gradients was also evaluated. Results: Within-cluster β j and β sim increased with productivity and decreased with seasonality. The converse was true for β nes. All species subsets responded similarly; however, productivity-beta-diversity relationships were weaker for the post-1970 temporal subset and strongest for species of moderate breadth. Between-cluster beta diversity (β j) and nestedness (β nes) declined with productivity. Main conclusions: As predicted, beta diversity of communities within productivity regimes was observed to increase with productivity. This pattern was driven largely by a gradient of species turnover. Therefore, beta diversity may make an important contribution to the broad-scale gradient of species richness with productivity. However, this species richness gradient dominates regional beta diversity between productivity regimes, resulting in decreasing between-productivity dissimilarity with productivity driven by a concurrent decline in nestedness