2020
DOI: 10.1016/j.envsci.2020.04.005
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Addressing the need for improved land cover map products for policy support

Abstract: Highlights Despite the widespread importance of land cover products, current production approaches leave many end users unsatisfied. Frequent, global coverage of satellite imagery is now available, enabling new approaches for land cover product generation. New approaches and land cover products can better serve end user needs. A dynamic, automated land cover mapping system is proposed, with challenges outlined and their solutions propose… Show more

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Cited by 56 publications
(42 citation statements)
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“…Such areas (many times transboundary ones) need very accurate monitoring and base maps, which are provided through this work, especially as areas shared between and/or among countries are frequently not mapped with a common legend, if mapped at all. The presented KLC datasets can be used for continuous land cover and land use monitoring, evaluation of management practices and effectiveness, endowment for scientific counsel, habitat modeling, information dissemination, and capacity building in their corresponding countries and to manage natural resources such as forests, soil, biodiversity, ecosystem services, and agriculture (Tolessa et al, 2017). Furthermore, regional climate change, biogeochemical, and hydrologic models are currently capable of using high-resolution LC data for predictions in general (Nissan et al, 2019) and spatially focused (i.e., Africa) (Sylla et al, 2016;Vondou and Haensler, 2017).…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Such areas (many times transboundary ones) need very accurate monitoring and base maps, which are provided through this work, especially as areas shared between and/or among countries are frequently not mapped with a common legend, if mapped at all. The presented KLC datasets can be used for continuous land cover and land use monitoring, evaluation of management practices and effectiveness, endowment for scientific counsel, habitat modeling, information dissemination, and capacity building in their corresponding countries and to manage natural resources such as forests, soil, biodiversity, ecosystem services, and agriculture (Tolessa et al, 2017). Furthermore, regional climate change, biogeochemical, and hydrologic models are currently capable of using high-resolution LC data for predictions in general (Nissan et al, 2019) and spatially focused (i.e., Africa) (Sylla et al, 2016;Vondou and Haensler, 2017).…”
Section: Discussionmentioning
confidence: 99%
“…Several recent studies call for the sharing of product validation datasets (Fritz et al, 2017;Tsendbazar et al, 2018), especially if a collection received financial support from government grants (Szantoi et al, 2020b). Accordingly, the validation datasets (LC-LCC) associated with each of the KLCs are also shared.…”
Section: Introductionmentioning
confidence: 99%
“…Conversely, satellite data can indicate a change in forest land cover over a harvested patch, while a no real land- use change occurred. This problem highlights the importance of having an integrated monitoring system, based in part on the acquisition and interpretation of satellite images, and in part on national scale statistical data derived from field surveys ( Szantoi et al, 2020 ). This makes it possible to reclassify land cover products into land use categories.…”
Section: How the Research Community Can Contribute To The Inventory Processmentioning
confidence: 99%
“…Moreover, map quality control must take into account the realities and goals of the mapping procedure including both technical and financial aspects. Reference data is crucial (Szantoi, Geller et al, 2020) and consists of already existing maps, fine spatial resolution EO images or field surveys, all of which can be costly. Field surveys are sometimes difficult or even impossible due to accessibility constraints or simply because of the mismatch between the insitu field check and the data/image acquisition date.…”
Section: Need For Quality Assessmentsmentioning
confidence: 99%