HistMapR: Rapid digitization of historical land-use maps in R

Auffret, Alistair G.; Kimberley, Adam; Plue, Jan; Skånes, Helle; Jakobsson, Simon; Waldén, Emelie; Wennbom, Marika; Wood, Heather; Bullock, James M.; Cousins, Sara A. O.; Gartz, Mira; Hooftman, Danny A. P.; Tränk, Louise

doi:10.1101/109504

Cited by 15 publications

(18 citation statements)

References 16 publications

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“…Detailed example scripts and input maps are available from Figshare https://doi.org/10.17045/sthlmuni.4649854 (Auffret et al . ).…”

Section: Data Accessibilitymentioning

confidence: 97%

“…The HistMapR package and documentation are hosted at https://github.com/ AGAuffret/HistMapR/. Detailed example scripts and input maps are available from Figshare https://doi.org/10.17045/sthlmuni.4649854 (Auffret et al 2017).…”

Section: Authors' Contributionsmentioning

confidence: 99%

“…All Swedish District Economic and Economic maps that we digitized are available from Figshare, along with the manually-digitized maps used for verification (Auffret et al 2017). Scanned Swedish historical maps can be found at http://his toriskakartor.lantmateriet.se/en (accessed 12 April 2017).…”

Section: Authors' Contributionsmentioning

confidence: 99%

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HistMapR: Rapid digitization of historical land‐use maps in R

et al. 2017

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Summary1. Habitat destruction and degradation represent serious threats to biodiversity, and quantification of land-use change over time is important for understanding the consequences of these changes to organisms and ecosystem service provision. 2. Comparing land use between maps from different time periods allows estimation of the magnitude of habitat change in an area. However, digitizing historical maps manually is time-consuming and analyses of change are usually carried out at small spatial extents or at low resolutions. 3. HistMapR contains a number of functions that can be used to semi-automatically digitize historical land use according to a map's colours, as defined by the RGB bands of the raster image. We test the method on different historical land-use map series and compare results to manual digitizations. 4. Digitization is fast, and agreement with manually digitized maps of around 80-90% meets common targets for image classification. We hope that the ability to quickly classify large areas of historical land use will promote the inclusion of land-use change into analyses of biodiversity, species distributions and ecosystem services.

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“…Detailed example scripts and input maps are available from Figshare https://doi.org/10.17045/sthlmuni.4649854 (Auffret et al . ).…”

Section: Data Accessibilitymentioning

confidence: 97%

Section: Authors' Contributionsmentioning

confidence: 99%

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HistMapR: Rapid digitization of historical land‐use maps in R

et al. 2017

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“…We created a two‐class land cover map of the study area (forest X non‐forest; Figure 1) by classifying a 2017 Google Earth image with the HistMapR v0.1 package (Auffret et al, 2017) in R version 3.5.0 (R Core Team, 2018). We classified the map based on color RGB values for 50 ground samples of each land class.…”

Section: Methodsmentioning

confidence: 99%

Playback point counts and N‐mixture models suggest higher than expected abundance of the critically endangered blond titi monkey in northeastern Brazil

Coelho

Collins

Júnior

et al. 2020

American J Primatol

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Effective management of threatened species requires accurate population size estimation and monitoring. However, reliable population size estimates are lacking for many endangered species. The critically endangered blond titi monkey (Callicebus barbarabrownae) is an endemic primate of the Caatinga biome in Northeastern Brazil.A previous assessment based on presence-only data estimated a minimum population size of 260 mature individuals in 2,636 km 2 , and studies based on visual records suggested very low local relative abundance. However, this cryptic species is known to be difficult to visually detect. We played back recordings of C. barbarabrownae loud calls to count the number of responding groups in 34 sampling sites during 9 consecutive days in a 221-km 2 study area. Repeated group counts at sites were used in N-mixture models, which account for imperfect detection, to estimate the number of groups in relation to dry forest area and distance to villages. We estimated a total of 91 groups in the study area. Considering the mean number of adults per group as three, we estimated a population of 273 adult individuals, resulting in a density of 2.3 individuals/km 2 in the dry forest habitat. Detection probability was four times higher for surveys conducted between sunrise to midmorning than between midmorning to sunset. We also found that C. barbarabrownae abundance increases with increasing dry forest area and increasing distance to the nearest village, indicating the need to promote dry forest restoration in the Caatinga. As our results suggest a larger population of C. barbarabrownae than had been previously estimated for its entire distribution, our results suggest a need for similar assessments in other areas to reliably estimate the total population size. This study demonstrates how playback surveys coupled with N-mixture models can be used to estimate population sizes of acoustically-responsive primates, and thus contribute to more effective conservation management. K E Y W O R D SCaatinga, Callicebus barbarabrownae, dry forest, endangered species, imperfect detection, IUCN Red List

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“…Auffret et al . () have recently proposed an R‐based tool to semiautomatically digitize land use from historical maps, receiving agreement of 80–90% with manually digitized maps. These examples clearly demonstrate the potential of automated extraction methodologies for different maps available over Europe.…”

Section: Introductionmentioning

confidence: 99%

Forest cover mask from historical topographic maps based on image processing

Ostafin

Iwanowski

Kozak

et al. 2017

Geoscience Data Journal

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Research carried out within the FORECOM project (Forest cover changes in mountainous regions -drivers, trajectories and implications, PSRP-008/2010), supported by a grant from Switzerland through the Swiss contribution to the enlarged European Union.This study aimed to obtain accurate binary forest masks which might be directly used in analysis of land cover changes over large areas. A sequence of image processing operations was conceived, parameterized and tested using various topographic maps from mountain areas in Poland and Switzerland. First, the input maps were filtered and binarized by thresholding in Hue-SaturationValue colour space. The second step consisted of a set of morphological image analysis procedures leading to final forest masks. The forest masks were then assessed and compared to manual forest boundary vectorization. The Polish topographical map published in the 1930s showed low accuracy which could be attributed to methods of cartographic presentation used and degradation of original colour prints. For maps published in the 1970s, the automated forest extraction performed very well, with accuracy exceeding 97%, comparable to accuracies of manual vectorization of the same maps performed by nontrained operators. With this method, we obtained a forest cover mask for the entire area of the Polish Carpathians, easily readable in any Geographic Information System software.

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HistMapR: Rapid digitization of historical land-use maps in R

Cited by 15 publications

References 16 publications

HistMapR: Rapid digitization of historical land‐use maps in R

HistMapR: Rapid digitization of historical land‐use maps in R

Playback point counts and N‐mixture models suggest higher than expected abundance of the critically endangered blond titi monkey in northeastern Brazil

Forest cover mask from historical topographic maps based on image processing

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