Challenges and opportunities in synthesizing historical geospatial data using statistical models

Eitzel, M. V.; Kelly, Maggi; Dronova, Iryna; Valachovic, Yana; Quinn‐Davidson, Lenya; Solera, Jon; Valpine, Perry de

doi:10.1016/j.ecoinf.2015.11.011

Cited by 11 publications

(3 citation statements)

References 56 publications

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“…The effect of all the drivers on LULC and forest recovery was assessed by logistic regressions (Van Doorn and Bakker 2007; Schweizer and Matlack 2014): we fitted models of LULC or forest recovery as a smooth function of the different drivers in a generalised additive model (GAM) with the R package mgcv (Wood 2006) using a logit link function. To take into account spatial autocorrelation in our models (Beale et al 2010; Saas and Gosselin 2014), we incorporated spatial effects as covariates using a smooth function of geographical coordinates (UTM northing and easting), as proposed by Eitzel et al (2016). A Moran test was applied to assess the global autocorrelation in the model residuals using the R package spdep (Bivand 2013); we defined point neighbourhood by Euclidean distance using a binary neighbours list and a maximal threshold distance of 2000 m (Appendix S4).…”

Section: Socioeconomic Factorsmentioning

confidence: 99%

Forest recovery since 1860 in a Mediterranean region: drivers and implications for land use and land cover spatial distribution

Abadie¹,

Dupouey

Avon³

et al. 2017

Landscape Ecol

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Context Land use and land cover (LULC) change is a major part of environmental change. Understanding its long-term causes is a major issue in landscape ecology. Objectives Our aim was to characterise LULC transitions since 1860 and assess the respective and changing effects of biophysical and socioeconomic drivers on forest, arable land and pasture in 1860, 1958 and 2010, and of biophysical, socioeconomic and distance from pre-existing forest on forest recovery for the two time intervals. Methods We assessed LULC transitions by superimposing 1860, 1958 and 2010 LULCs using a regular grid of 1 9 1 km points, in a French Mediterranean landscape (195,413 ha). We tested the effects of drivers using logistic regressions, and quantified pure and joint effects by deviance partitioning. Results Over the whole period, the three main LULCs were spatially structured according to land accessibility and soil productivity. LULC was driven more by socioeconomic than biophysical drivers in 1860, but the pattern was reversed in 2010. A widespread forest recovery mainly occurred on steeper slopes, far from houses and close to pre-existing forest, due to traditional practice abandonment. Forest recovery was better explained by biophysical than by socioeconomic drivers and was more dependent on Electronic supplementary material The online version of this article (

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Section: Socioeconomic Factorsmentioning

confidence: 99%

Forest recovery since 1860 in a Mediterranean region: drivers and implications for land use and land cover spatial distribution

Abadie¹,

Dupouey

Avon³

et al. 2017

Landscape Ecol

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“…This required well trained specialists, and imposed numerous challenges [45]. In recent years, automated classification techniques have been used in interpreting aerial imagery [49,[60][61][62]. Object-based classification is used to efficiently delineate homogeneous polygons of land-cover/land-use based on panchromatic aerial imagery [63].…”

Section: Acquisition and Processing Of Aerial Photosmentioning

confidence: 99%

Reconstructing Long Term High Andean Forest Dynamics Using Historical Aerial Imagery: A Case Study in Colombia

Calbi

Clerici

Borsch

et al. 2020

Forests

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High Andean forests are biodiversity hotspots that also play key roles in the provisioning of vital ecosystem services for neighboring cities. In past centuries, the hinterland of Andean fast-growing cities often experienced a dramatic decline in forested areas, but there are reports that forest cover has been recovering recently. We analyzed aerial imagery spanning the years 1940 to 2007 from nine administrative localities in the Eastern Andean Cordillera of Colombia in order to elucidate precise patterns of forest vegetation change. To this aim, we performed image object-based classification by means of texture analysis and image segmentation. We then derived connectivity metrics to investigate whether forest cover trajectories showed an increase or decrease in fragmentation and landscape degradation. We observed a forest cover recovery in all the examined localities, except one. In general, forest recovery was accompanied by an increase in core habitat areas. The time scale of the positive trends identified partially coincides with the creation of protected areas in the region, which very likely furthered the recovery of forest patches. This study unveils the long-term dynamics of peri-urban high Andean forest cover, providing valuable information on historical vegetation changes in a highly dynamic landscape.

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“…While big data have garnered deserved attention, data generated from individual projects in small volumes at local scales (also called the "long tail of science") (Heidorn, 2008;Hampton et al, 2013;Wallis et al, 2013) and "dark data" including both unstructured and unused digital data collected during routine business and research (Hampton et al, 2013;Wallis et al, 2013;Ferguson et al, 2014) as well as analog, unarchived, non-machine readable historical data (also known as legacy, or heritage data) (Bürgi and Gimmi, 2007;Salmond et al, 2012) have not. Such datasets are the foundations on which big data is often built (Ferguson et al, 2014) and represent a large portion of the data landscape that is currently underutilized but has recognized potential (Michener and Jones, 2012;Bi et al, 2013;Eitzel et al, 2016;Kelly et al, 2016). This paper responds to the need for new theory and methods to move what we call historical dark dataunarchived, non-digital legacy data-from file drawers to the cloud in order to realize its full potential and become an integral part of the digital data landscape.…”

Section: Introductionmentioning

confidence: 99%

From the Field to the Cloud: A Review of Three Approaches to Sharing Historical Data From Field Stations Using Principles From Data Science

Easterday

Paulson

DasMohapatra

et al. 2018

Front. Environ. Sci.

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Historical data play an important role in our understanding of environmental change and ecosystem dynamics. By lengthening the temporal scale of scientific inquiry, historical data reveal insights into the dynamic nature of ecosystems. However, most historical data has yet to make a full contribution, remaining "dark" and out of reach to the broader scientific community. This article responds to several calls stressing the importance of empirical historical materials and urges their preservation and accessibility. Despite the importance of historical data collections, few standards have emerged to integrate historical dark data into the larger digital data landscape. To encourage greater use of historical data across scientific disciplines it is vital to make data findable, accessible, interoperable, and reusable (e.g., the FAIR principles). In this paper we discuss the potential of historical dark data to contribute to the modern digital ecological data landscape. We do this by focusing on three cases from the University of California field and research stations and the groups that have worked to make historical dark data discoverable. Despite the common goal of maximizing the potential use of these data collections, each case and the methods employed are unique, and showcase varying levels of success in achieving the FAIR principles and shepherding historical data into the twenty-first century.

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Challenges and opportunities in synthesizing historical geospatial data using statistical models

Cited by 11 publications

References 56 publications

Forest recovery since 1860 in a Mediterranean region: drivers and implications for land use and land cover spatial distribution

Forest recovery since 1860 in a Mediterranean region: drivers and implications for land use and land cover spatial distribution

Reconstructing Long Term High Andean Forest Dynamics Using Historical Aerial Imagery: A Case Study in Colombia

From the Field to the Cloud: A Review of Three Approaches to Sharing Historical Data From Field Stations Using Principles From Data Science

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