Modelling avian biodiversity using raw, unclassified satellite imagery

St-Louis,; Pidgeon, Anna M.; Kuemmerle, Tobias; Sonnenschein, Ruth; Radeloff, Volker C.; Clayton, Murray K.; Locke, Brian A.; Bash, Dallas; Hostert, Patrick

doi:10.1098/rstb.2013.0197

Cited by 40 publications

(50 citation statements)

References 47 publications

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“…This is an especially important result as large frugivores, such as toucans, are particularly important for seed dispersal as they are capable of carrying large, late-successional tree seeds. These results complement prior research, which showed that both LiDAR-and Landsat-based measures of canopy complexity were strong predictors of bird species richness (Goetz et al, 2007;St-Louis et al, 2014) and bat activity (Jung et al, 2012). In general, frugivore detections increased as canopy height and structural complexity increased, which coincides with prior studies including one that used the framework of this study (Lindell et al, 2012;McDonnell, 1986), but detections decreased as the amount of large gaps, open space, and short vegetation increased.…”

Section: Canopy Structure and Frugivorous Birdssupporting

confidence: 88%

Using lightweight unmanned aerial vehicles to monitor tropical forest recovery

Zahawi

Dandois

Holl

et al. 2015

Biological Conservation

230

211

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a b s t r a c tLarge areas of tropical lands are being removed from agriculture and restored to address conservation goals. However, monitoring the ecological value of these efforts at the individual land-owner scale is rare, owing largely to issues of cost and accessibility. Traditional field-based measures for assessing forest recovery and habitat quality can be labour intensive and costly. Here we assess whether remote sensing measurements from lightweight unmanned aerial vehicles (UAV) are a cost-effective substitute for traditional field measures. An inexpensive UAV-based remote sensing methodology, ''Ecosynth'', was applied to measure forest canopy structure across field plots in a 7-9-yr tropical forest restoration study in southern Costa Rica. Ecosynth methods combine aerial images from consumer-grade digital cameras with computer vision software to generate 3D 'point cloud' models of vegetation at high spatial resolutions. Ecosynth canopy structure measurements were compared to field-based measures and their ability to predict the abundance of frugivorous birds; key seed dispersers that are sensitive to canopy structure. Ecosynth canopy height measurements were highly correlated with field-based measurements (R 2 P 0.85), a result comparable in precision to LiDAR-based remote sensing measurements. Ecosynth parameters were also strongly correlated with above-ground biomass (R 2 P 0.81) and percent canopy openness (R 2 = 0.82). Correlations were weaker with proportion-based measures such as canopy roughness (R 2 = 0.53). Several Ecosynth metrics (e.g., canopy openness and height) predicted frugivore presence and abundance at levels of accuracy similar to those of field-based measurements. Ecosynth UAV remotesensing provides an effective alternate methodology to traditional field-based measures of evaluating forest structure and complexity across landscapes. Furthermore, given the volume of data that can be generated in a single flight plan, as well as the ability to use the technology in remote areas, these methods could expand the scope of studies on forest dynamics and recovery when combined with field-based calibration plots.

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Section: Canopy Structure and Frugivorous Birdssupporting

confidence: 88%

Using lightweight unmanned aerial vehicles to monitor tropical forest recovery

Zahawi

Dandois

Holl

et al. 2015

Biological Conservation

230

211

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“…The Normalized Differenced Vegetation Index (NDVI) derived from satellite images is related to chlorophyll content and cell structure and has been found valuable for habitat analysis of birds [41,42]. The texture derived from satellite images has also been shown to provide useful information for habitat analysis [43][44][45]. As for ALS data, they have proven especially useful for providing information about fine-scale habitat heterogeneity and structure, a fundamental correlate of species diversity (e.g., [46,47]).…”

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confidence: 99%

Can Airborne Laser Scanning (ALS) and Forest Estimates Derived from Satellite Images Be Used to Predict Abundance and Species Richness of Birds and Beetles in Boreal Forest?

et al. 2015

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Abstract:In managed landscapes, conservation planning requires effective methods to identify high-biodiversity areas. The objective of this study was to evaluate the potential of airborne laser scanning (ALS) and forest estimates derived from satellite images extracted at two spatial scales for predicting the stand-scale abundance and species richness of birds and beetles in a managed boreal forest landscape. Multiple regression models based on forest data from a 50-m radius (i.e., corresponding to a homogenous forest stand) had better explanatory power than those based on a 200-m radius (i.e., including also parts of adjacent stands). Bird abundance and species richness were best explained by the ALS variables "maximum vegetation height" and "vegetation cover between 0.5 and 3 m" (both positive). Flying beetle abundance and species richness, as well as epigaeic (i.e., ground-living) beetle richness were best explained by a model including the ALS variable "maximum vegetation height" (positive) and the satellite-derived variable "proportion of pine" (negative). Epigaeic beetle abundance was best explained by "maximum vegetation height" at 50 m (positive) and "stem volume" at 200 m (positive). Our results show that forest estimates derived from OPEN ACCESS Remote Sens. 2015, 7 4234 satellite images and ALS data provide complementary information for explaining forest biodiversity patterns. We conclude that these types of remote sensing data may provide an efficient tool for conservation planning in managed boreal landscapes.

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“…; Selkowitz ), multi‐spectral information provides an important add‐on and helps decipher fuzzy borders between classes and should be considered in fine‐scale studies and applications (St‐Louis et al. ). Moreover, other remote sensing technologies such as Synthetic Aperture Radar (SAR) help discriminate management practices and could reveal useful in improving our results (Inglada et al.…”

Section: Discussionmentioning

confidence: 99%

From ecology to remote sensing: using animals to map land cover

Remelgado

Safi

Wegmann³

2019

Remote Sens Ecol Conserv

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Land cover is a key variable in monitoring applications and new processing technologies made deriving this information easier. Yet, classification algorithms remain dependent on samples collected on the field and field campaigns are limited by financial, infrastructural and political boundaries. Here, animal tracking data could be an asset. Looking at the land cover dependencies of animal behaviour, we can obtain land cover samples over places that are difficult to access. Following this premise, we evaluated the potential of animal movement data to map land cover. Specifically, we used 13 White Storks (Cicona cicona) individuals of the same population to map agriculture within three test regions distributed along their migratory track. The White Stork has adapted to foraging over agricultural lands, making it an ideal source of samples to map this land use. We applied a presence–absence modelling approach over a Normalized Difference Vegetation Index (NDVI) time series and validated our classifications, with high‐resolution land cover information. Our results suggest White Stork movement is useful to map agriculture, however, we identified some limitations. We achieved high accuracies (F1‐scores > 0.8) for two test regions, but observed poor results over one region. This can be explained by differences in land management practices. The animals preferred agriculture in every test region, but our data showed a biased distribution of training samples between irrigated and non‐irrigated land. When both options occurred, the animals disregarded non‐irrigated land leading to its misclassification as non‐agriculture. Additionally, we found difference between the GPS observation dates and the harvest times for non‐irrigated crops. Given the White Stork takes advantage of managed land to search for prey, the inactivity of these fields was the likely culprit of their underrepresentation. Including more species attracted to agriculture – with other land‐use dependencies and observation times – can contribute to better results in similar applications.

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Modelling avian biodiversity using raw, unclassified satellite imagery

Cited by 40 publications

References 47 publications

Using lightweight unmanned aerial vehicles to monitor tropical forest recovery

Using lightweight unmanned aerial vehicles to monitor tropical forest recovery

Can Airborne Laser Scanning (ALS) and Forest Estimates Derived from Satellite Images Be Used to Predict Abundance and Species Richness of Birds and Beetles in Boreal Forest?

From ecology to remote sensing: using animals to map land cover

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