база данных landcover
Вышла новая база типов покрытия земной поверхности, грид 1х1 км, 12 классов поверхности.
Радость модельерам.
A global 1-km consensus land-cover product for biodiversity and ecosystem modelling.
Mao-Ning Tuanmu and Walter Jetz
http://onlinelibrary.wiley.com/doi/10.1111/geb.12182/abstract
Abstract
Aim
For many applications in biodiversity and ecology, existing remote sensing-derived land-cover products have limitations due to among-product inconsistency and their typically non-continuous nature. Here we aim to help address these shortcomings by generating a 1-km resolution global product that provides scale-integrated and accuracy-weighted consensus land-cover information on an approximately continuous scale.
Location
Global.
Methods
Using a generalized classification scheme and an accuracy-based integration approach, we integrated four global land-cover products. We evaluated the performance of this product compared with inputs for estimating subpixel 30-m resolution land cover. We also compared the accuracy of deductive and inductive species distribution models built with the different products for modelling the continental distributions of six avian habitat specialists.
Results
Our product offers accuracy-weighted consensus information on the prevalence of 12 land-cover classes within every nominal 1-km pixel across the globe (except for Antarctica). Compared with the four base products, it better captures the land-cover information contained in the fine-grain validation data for all classes combined and for most individual classes. It also has the highest sensitivity and overall accuracy for detecting the presence of every fine-grain land-cover class. Both deductive and inductive models built with the consensus dataset have the highest or second highest accuracy for modelling bird species distributions.
Main conclusions
Our consensus product integrates the four base products and successfully maximizes accuracy and reduces errors of omission. Specifically, the consensus product reduces limitations caused by misclassifications, false absence rates and the categorical format of existing land-cover products. Consequently, it surpasses single base products in the ability to capture subpixel land-cover information and the utility for modelling species distributions. Both the presented methodology and the consensus product have multiple applications in biodiversity research and for understanding and modelling of global terrestrial ecosystems.
Радость модельерам.
A global 1-km consensus land-cover product for biodiversity and ecosystem modelling.
Mao-Ning Tuanmu and Walter Jetz
http://onlinelibrary.wiley.com/doi/10.1111/geb.12182/abstract
Abstract
Aim
For many applications in biodiversity and ecology, existing remote sensing-derived land-cover products have limitations due to among-product inconsistency and their typically non-continuous nature. Here we aim to help address these shortcomings by generating a 1-km resolution global product that provides scale-integrated and accuracy-weighted consensus land-cover information on an approximately continuous scale.
Location
Global.
Methods
Using a generalized classification scheme and an accuracy-based integration approach, we integrated four global land-cover products. We evaluated the performance of this product compared with inputs for estimating subpixel 30-m resolution land cover. We also compared the accuracy of deductive and inductive species distribution models built with the different products for modelling the continental distributions of six avian habitat specialists.
Results
Our product offers accuracy-weighted consensus information on the prevalence of 12 land-cover classes within every nominal 1-km pixel across the globe (except for Antarctica). Compared with the four base products, it better captures the land-cover information contained in the fine-grain validation data for all classes combined and for most individual classes. It also has the highest sensitivity and overall accuracy for detecting the presence of every fine-grain land-cover class. Both deductive and inductive models built with the consensus dataset have the highest or second highest accuracy for modelling bird species distributions.
Main conclusions
Our consensus product integrates the four base products and successfully maximizes accuracy and reduces errors of omission. Specifically, the consensus product reduces limitations caused by misclassifications, false absence rates and the categorical format of existing land-cover products. Consequently, it surpasses single base products in the ability to capture subpixel land-cover information and the utility for modelling species distributions. Both the presented methodology and the consensus product have multiple applications in biodiversity research and for understanding and modelling of global terrestrial ecosystems.