Proactive management of invasive species in urban areas is critical to restricting their overall distribution. The objective of this work is to determine whether advanced remote sensing technologies can help to detect invasions effectively and efficiently in complex urban ecosystems such as parks. In Surrey, BC, Canada, Himalayan blackberry (Rubus armeniacus) and English ivy (Hedera helix) are two invasive shrub species that can negatively affect native ecosystems in cities and managed urban parks. Random forest (RF) models were created to detect these two species using a combination of hyperspectral imagery, and light detection and ranging (LiDAR) data. LiDAR-derived predictor variables included irradiance models, canopy structural characteristics, and orographic variables. RF detection accuracy ranged from 77.8 to 87.8% for Himalayan blackberry and 81.9 to 82.1% for English ivy, with open areas classified more accurately than areas under canopy cover. English ivy was predicted to occur across a greater area than Himalayan blackberry both within parks and across the entire city. Both Himalayan blackberry and English ivy were mostly located in clusters according to a Local Moran’s I analysis. The occurrence of both species decreased as the distance from roads increased. This study shows the feasibility of producing highly accurate detection maps of plant invasions in urban environments using a fusion of remotely sensed data, as well as the ability to use these products to guide management decisions.
Detailed vertical forest structure information can be remotely sensed by combining technologies of unmanned aerial systems (UAS) and digital aerial photogrammetry (DAP). A key limitation in the application of DAP methods, however, is the inability to produce accurate digital elevation models (DEM) in areas of dense vegetation. This study investigates the terrain modeling potential of UAS-DAP methods within a temperate conifer forest in British Columbia, Canada. UAS-acquired images were photogrammetrically processed to produce high-resolution DAP point clouds. To evaluate the terrain modeling ability of DAP, first, a sensitivity analysis was conducted to estimate optimal parameters of three ground-point classification algorithms designed for airborne laser scanning (ALS). Algorithms tested include progressive triangulated irregular network (TIN) densification (PTD), hierarchical robust interpolation (HRI) and simple progressive morphological filtering (SMRF). Points were classified as ground from the ALS and served as ground-truth data to which UAS-DAP derived DEMs were compared. The proportion of area with root mean square error (RMSE) <1.5 m were 56.5%, 51.6% and 52.3% for the PTD, HRI and SMRF methods respectively. To assess the influence of terrain slope and canopy cover, error values of DAP-DEMs produced using optimal parameters were compared to stratified classes of canopy cover and slope generated from ALS point clouds. Results indicate that canopy cover was approximately three times more influential on RMSE than terrain slope.
BackgroundMalaria remains the deadliest vector-borne disease despite long-term, costly control efforts. The United Republic of Tanzania has implemented countrywide anti-malarial interventions over more than a decade, including national insecticide-treated net (ITN) rollouts and subsequent monitoring. While previous analyses have compared spatial variation in malaria endemicity with ITN distributions, no study has yet compared Anopheles habitat suitability to determine proper allocation of ITNs. This study assesses where mosquitoes were most likely to thrive before implementation of large-scale ITN interventions in Tanzania and determine if ITN distributions successfully targeted those areas.MethodsUsing Maxent, a species distribution model was constructed relating anopheline mosquito occurrences for 1999–2003 to high resolution environmental observations. A 2011–2012 layer of mosquito net ownership was created using georeferenced data across Tanzania from the Demographic and Health Surveys. The baseline mosquito habitat suitability was compared to subsequent ITN ownership using (1) the average ITN numbers per house and (2) the proportion of households with ≥1 net to test whether national ITN ownership targets have been met and have tracked malaria risk.ResultsElevation, land cover, and human population distribution outperformed variants of temperature and Normalized Difference Vegetation Index (NDVI) in anopheline distribution models. The spatial distribution of ITN ownership across Tanzania was near-random spatially (Moran’s I = 0.07). Householders reported owning 2.488 ITNs on average and 93.41 % of households had ≥1 ITN. Mosquito habitat suitability was statistically unrelated to reported ITN ownership and very weakly to the proportion of households with ≥1 ITN (R2 = 0.051). Proportional ITN ownership/household varied relative to mosquito habitat suitability (Levene’s test F = 3.0037). Quantile regression was used to assess trends in ITN ownership among households with the highest and lowest 10 % of ITN ownership. ITN ownership declined significantly toward areas with the highest vector habitat suitability among households with lowest ITN ownership (t = −3.38). In areas with lowest habitat suitability, ITN ownership was consistently higher.ConclusionsInsecticide-treated net ownership is critical for malaria control. While Tanzania-wide efforts to distribute ITNs has reduced malaria impacts, gaps and variance in ITN ownership are unexpectedly large in areas where malaria risk is highest. Supplemental ITN distributions targeting prime Anopheles habitats are likely to have disproportionate human health benefits.Electronic supplementary materialThe online version of this article (doi:10.1186/s12936-015-0841-x) contains supplementary material, which is available to authorized users.
Fire severity mapping is conventionally accomplished through the interpretation of aerial photography or the analysis of moderate- to coarse-spatial-resolution pre- and post-fire satellite imagery. Although these methods are well established, there is a demand from both forest managers and fire scientists for higher-spatial-resolution fire severity maps. This study examines the utility of high-spatial-resolution post-fire imagery and digital aerial photogrammetric point clouds acquired from an unmanned aerial vehicle (UAV) to produce integrated fire severity–land cover maps. To accomplish this, a suite of spectral, structural and textural variables was extracted from the UAV-acquired data. Correlation-based feature selection was used to select subsets of variables to be included in random forest classifiers. These classifiers were then used to produce disturbance-based land cover maps at 5- and 1-m spatial resolutions. By analysing maps produced using different variables, the highest-performing spectral, structural and textural variables were identified. The maps were produced with high overall accuracies (5m, 89.5±1.4%; 1m, 85.4±1.5%), with the 1-m classification produced at slightly lower accuracies. This reduction was attributed to the inclusion of four additional classes, which increased the thematic detail enough to outweigh the differences in accuracy.
Understanding future tree species migration is challenging due to the unprecedented rate of climate change combined with the presence of human barriers that may limit or impede species movement. Projected changes in climatic conditions outpace migration rates, and more realistic rates of range expansion are needed to make sound environmental policies. In this paper, we develop a modeling approach that takes into account both the geographic changes in the area suitable for the growth and reproduction of tree species, as well as limits imposed geographically on their potential migration using remotely-sensed land cover information. To do so, we combined a physiologically-based decision tree model with a remotely-sensed-derived diffusion-dispersal model to identify the most likely direction of future migration for 15 native tree species in the Pacific Northwest Region of North America, as well as the degree that landscape fragmentation might limit movement. Although projected changes in climate through to 2080 are likely to create favorable environments for range expansion of the 15 tree species by 65% on average, by limiting the potential movement by previously published migration rates and landscape fragmentation, range expansion will likely be 50%-90% of the potential. The hybrid modeling approach using distribution modeling and remotely-sensed data fills a gap between naïve and more complex approaches to take into account major impediments on the potential migration of native tree species.
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