@Article{SeeSLMFCPSZMSACVGRSMKHSVKO:2015:BuHyLa,
author = "See, Linda and Schepaschenko, Dmitry and Lesiv, Myroslava and
Mccallum, Ian and Fritz, Steffen and Comber, Alexis and Perger,
Christoph and Schill, Christian and Zhao, Yuanyuan and Maus,
Victor Wegner and Siraj, Muhammad Athar and Albrecht, Franziska
and Cipriani, Anna and Vakolyuk, Mar'Yana and Garcia, Alfredo and
Rabia, Ahmed H. and Singha, Kuleswar and Marcarini, Abel Alan and
Kattenborn, Teja and Hazarika, Rubul and Schepaschenko, Maria and
Van Der Velde, Marijn and Kraxner, Florian and Obersteiner,
Michael",
affiliation = "{International Institute of Applied Systems Analysis (IIASA)} and
{International Institute of Applied Systems Analysis (IIASA)} and
{Lviv Polytechnic National University} and {International
Institute of Applied Systems Analysis (IIASA)} and {International
Institute of Applied Systems Analysis (IIASA)} and {University of
Leicester} and {International Institute of Applied Systems
Analysis (IIASA)} and {University of Freiburg} and {Tsinghua
University} and {Instituto Nacional de Pesquisas Espaciais (INPE)}
and {National University of Sciences and Technology (IGIS-NUST)}
and {University of Vienna} and {Universit{\`a} degli Studi di
Modena e Reggio Emilia} and {International Institute of Applied
Systems Analysis (IIASA)} and {National Institute of Agricultural
Technology (INTA)} and {Damanhour University} and {Gauhati
University} and {AgroParis Tech} and {Albert-Ludwigs-University
Freiburg} and {Institute of Geological Science of the National
Academy of Sciences of Ukraine} and {Russian Institute of
Continuous Education in Forestry} and {International Institute of
Applied Systems Analysis (IIASA)} and {International Institute of
Applied Systems Analysis (IIASA)} and {International Institute of
Applied Systems Analysis (IIASA)}",
title = "Building a hybrid land cover map with crowdsourcing and
geographically weighted regression",
journal = "ISPRS Journal of Photogrammetry and Remote Sensing",
year = "2015",
volume = "103",
pages = "48--56",
keywords = "Land cover, Validation, Crowdsourcing, Map integration, Global
land cover, Geographically weighted regression.",
abstract = "Land cover is of fundamental importance to many environmental
applications and serves as critical baseline information for many
large scale models e.g. in developing future scenarios of land use
and climate change. Although there is an ongoing movement towards
the development of higher resolution global land cover maps,
medium resolution land cover products (e.g. GLC2000 and MODIS) are
still very useful for modelling and assessment purposes. However,
the current land cover products are not accurate enough for many
applications so we need to develop approaches that can take
existing land covers maps and produce a better overall product in
a hybrid approach. This paper uses geographically weighted
regression (GWR) and crowdsourced validation data from Geo-Wiki to
create two hybrid global land cover maps that use medium
resolution land cover products as an input. Two different methods
were used: (a) the GWR was used to determine the best land cover
product at each location; (b) the GWR was only used to determine
the best land cover at those locations where all three land cover
maps disagree, using the agreement of the land cover maps to
determine land cover at the other cells. The results show that the
hybrid land cover map developed using the first method resulted in
a lower overall disagreement than the individual global land cover
maps. The hybrid map produced by the second method was also better
when compared to the GLC2000 and GlobCover but worse or similar in
performance to the MODIS land cover product depending upon the
metrics considered. The reason for this may be due to the use of
the GLC2000 in the development of GlobCover, which may have
resulted in areas where both maps agree with one another but not
with MODIS, and where MODIS may in fact better represent land
cover in those situations. These results serve to demonstrate that
spatial analysis methods can be used to improve medium resolution
global land cover information with existing products.",
doi = "10.1016/j.isprsjprs.2014.06.016",
url = "http://dx.doi.org/10.1016/j.isprsjprs.2014.06.016",
issn = "0924-2716",
label = "lattes: 4419790523552484 10
SeeSLMFCPSZMSACVGRSMKHSVKO:2014:BuHyLa",
language = "en",
targetfile = "see_building.pdf",
urlaccessdate = "02 maio 2024"
}