Dataset Information

Built-up areas within and around protected areas: Global patterns and 40-year trends.

ABSTRACT: Protected areas (PAs) are a key strategy in global efforts to conserve biodiversity and ecosystem services that are critical for human well-being. Most PAs have some built-up structures within their boundaries or in surrounding areas, ranging from individual buildings to villages, towns and cities. These structures, and the associated human activities, can exert direct and indirect pressures on PAs. Here we present the first global analysis of current patterns and observed long-term trends in built-up areas within terrestrial PAs and their immediate surroundings. We calculate for each PA larger than 5 km² and for its 10-km unprotected buffer zone the percentage of land area covered by built-up areas in 1975, 1990, 2000 and 2014. We find that globally built-up areas cover only 0.12% of PA extent and a much higher 2.71% of the unprotected buffers as of 2014, compared to 0.6% of all land (protected or unprotected). Built-up extent in and around PAs is highest in Europe and Asia, and lowest in Africa and Oceania. Built-up area percentage is higher in coastal and small PAs, and lower in older PAs and in PAs with stricter management categories. From 1975 to 2014, the increase in built-up area was 23 times larger in the 10-km unprotected buffers than within PAs. Our findings show that the development of built-up structures remains limited within the boundaries of PAs but highlight the need to carefully manage the considerable pressure that PAs face from their immediate surroundings.

SUBMITTER: Fuente B

PROVIDER: S-EPMC7789035 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Built-up areas within and around protected areas: Global patterns and 40-year trends.

Fuente Begoña de la B Bertzky Bastian B Delli Giacomo G Mandrici Andrea A Conti Michele M Florczyk Aneta J AJ Freire Sergio S Schiavina Marcello M Bastin Lucy L Dubois Grégoire G

Global ecology and conservation 20201201

Protected areas (PAs) are a key strategy in global efforts to conserve biodiversity and ecosystem services that are critical for human well-being. Most PAs have some built-up structures within their boundaries or in surrounding areas, ranging from individual buildings to villages, towns and cities. These structures, and the associated human activities, can exert direct and indirect pressures on PAs. Here we present the first global analysis of current patterns and observed long-term trends in bu ...[more]

PMID: 33457468

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Project description:BackgroundProtected areas, regarded today as a cornerstone of nature conservation, result from an array of multiple motivations and opportunities. We explored at global and regional levels the current distribution of protected areas along biophysical, human, and biological gradients, and assessed to what extent protection has pursued (i) a balanced representation of biophysical environments, (ii) a set of preferred conditions (biological, spiritual, economic, or geopolitical), or (iii) existing opportunities for conservation regardless of any representation or preference criteria.MethodsWe used histograms to describe the distribution of terrestrial protected areas along biophysical, human, and biological independent gradients and linear and non-linear regression and correlation analyses to describe the sign, shape, and strength of the relationships. We used a random forest analysis to rank the importance of different variables related to conservation preferences and opportunity drivers, and an evenness metric to quantify representativeness.ResultsWe find that protection at a global level is primarily driven by the opportunities provided by isolation and a low population density (variable importance = 34.6 and 19.9, respectively). Preferences play a secondary role, with a bias towards tourism attractiveness and proximity to international borders (variable importance = 12.7 and 3.4, respectively). Opportunities shape protection strongly in "North America & Australia-NZ" and "Latin America & Caribbean," while the importance of the representativeness of biophysical environments is higher in "Sub-Saharan Africa" (1.3 times the average of other regions).DiscussionEnvironmental representativeness and biodiversity protection are top priorities in land conservation agendas. However, our results suggest that they have been minor players driving current protection at both global and regional levels. Attempts to increase their relevance will necessarily have to recognize the predominant opportunistic nature that the establishment of protected areas has had until present times.

Project description:It is common knowledge that the level of landscape heterogeneity may affect the performance of remote sensing based land use / land cover classification. While this issue has been studied in depth for land cover data in general, the specific relationship between the mapping accuracy and morphological characteristics of built-up surfaces has not been analyzed in detail, an urgent need given the recent emergence of a variety of global, fine-resolution settlement datasets. Moreover, previous studies typically rely on aggregated, broad-scale landscape metrics to quantify the morphology of built-up areas, neglecting the fine-grained spatial variation and scale dependency of such metrics. Herein, we aim to fill this knowledge gap by assessing the associations between localized (focal) landscape metrics, derived from binary built-up surfaces and localized data accuracy estimates. We tested our approach for built-up surfaces from the Global Human Settlement Layer (GHSL) for Massachusetts (USA). Specifically, we examined the explanatory power of landscape metrics with respect to both commission and omission errors in the multi-temporal GHS-BUILT R2018A data product. We found that the Landscape Shape Index (LSI) calculated in focal windows exhibits, on average, the highest levels of correlation to focal accuracy measures. These relationships are scale-dependent, and become stronger with increasing level of spatial support. We found that thematic omission error, as measured by Recall, has the strongest relationship to measures of built-up surface morphology across different temporal epochs and spatial resolutions. The results of our regression analysis (R2>0.9), estimating accuracy based on landscape metrics, confirmed these findings. Lastly, we tested the generalizability of our findings by regionally stratifying our regression models and applying them to a different version of the GHSL (i.e., the GHS-BUILT-S2) and a different study area. We observed varying levels of model transferability, indicating that the relationship between accuracy and landscape metrics may be sensor-specific, and is heavily localized for most accuracy metrics, but quite generalizable for the Recall measure. This indicates that there is a strong and generalizable association between morphological properties of built-up land and the degree to which it is "undermapped".

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Dataset Information

Built-up areas within and around protected areas: Global patterns and 40-year trends.

Publications

Built-up areas within and around protected areas: Global patterns and 40-year trends.

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