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Selection: with tag random-forest [5 articles] 

 

A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform

  
ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 144 (October 2018), pp. 325-340, https://doi.org/10.1016/j.isprsjprs.2018.07.017

Abstract

[Highlights] [::] Demonstrated a paradigm shift in continent-scale 30-m Landsat cropland mapping. [::] Captured spatial extent of very small to very large farms in Australia and China. [::] Applied Random Forest machine learning algorithm on cloud computing platform. [::] Overall accuracies of 30-m cropland products of Australia and China exceeded 94%. [::] Errors of omissions of cropland class were 1.2% for Australia and 20% for China. [::] Product view at: www.croplands.org download at: https://lpdaac.usgs.gov/node/1282. [Abstract] Mapping high resolution (30-m or better) cropland extent over very large areas such as ...

 

(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 33

  
(February 2014)
Keywords: inrmm-list-of-tags   pyrus-tamamschianae   pyrus-theodorovii   pyrus-vallis-demonis   pyrus-voronovii   python   q-learning   qualitative-research   quantile-95   quantitive-variation   quantity-calculus   quantum-computing   quantum-gis   quarantine   quaternary   quercus-agrifolia   quercus-alnifolia   quercus-borealis   quercus-calliprinos   quercus-canariensis   quercus-castaneifolia   quercus-cerrioides   quercus-cerris   quercus-chrysolepis   quercus-coccifera   quercus-coccinea   quercus-crenata   quercus-dalechampii   quercus-douglasii   quercus-emoryi   quercus-engelmannii   quercus-faginea   quercus-falcata   quercus-frainetto   quercus-gambelii   quercus-garryana   quercus-hartwissiana   quercus-ilex   quercus-imbricaria   quercus-imeretina   quercus-kelloggii   quercus-lanuginosa   quercus-liaotungensis   quercus-lobata   quercus-macranthera   quercus-macrolepis   quercus-mirbeckii   quercus-mongolica   quercus-palustris   quercus-parvula   quercus-pedunculata   quercus-pedunculiflora   quercus-petraea   quercus-polycarpa   quercus-pontica   quercus-prinus   quercus-pubescens   quercus-pyrenaica   quercus-robur   quercus-rotundifolia   quercus-rubra   quercus-sessiflora   quercus-sessiliflora   quercus-sicula   quercus-spp   quercus-suber   quercus-trojana   quercus-variabilis   quercus-virgiliana   quercus-virginiana   quercus-wislizeni   quercus-x-morisii   radar   radial-growth   radiocarbon-chronology   rain-shadow   rainfall   rainfall-deciles   rainforest   rainy-days_daily-rainfall   random-forest   random-forests   random-walk   range-altitude   range-modelling   range-shift   rank-based-analysis   rapid-assessment   rare-events   rarely-observed-plant-species   rasterisation   ravine-forest   rcp26   rcp45   rcp60   rcp85   realised-vs-potential-range   realized-niche   reassuring-learning   receptivity  

Abstract

List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( http://mfkp.org/INRMM/tag/inrmm-list-of-tags ). ...

 

Predicting habitat suitability with machine learning models: The potential area of Pinus sylvestris L. in the Iberian Peninsula

  
Ecological Modelling, Vol. 197, No. 3-4. (August 2006), pp. 383-393, https://doi.org/10.1016/j.ecolmodel.2006.03.015

Abstract

We present a modelling framework for predicting forest areas. The framework is obtained by integrating a machine learning software suite within the GRASS Geographical Information System (GIS) and by providing additional methods for predictive habitat modelling. Three machine learning techniques (Tree-Based Classification, Neural Networks and Random Forest) are available in parallel for modelling from climatic and topographic variables. Model evaluation and parameter selection are measured by sensitivity-specificity ROC analysis, while the final presence and absence maps are obtained through maximisation of ...

 

Does the interpolation accuracy of species distribution models come at the expense of transferability?

  
Ecography, Vol. 35, No. 3. (March 2012), pp. 276-288, https://doi.org/10.1111/j.1600-0587.2011.06999.x

Abstract

Model transferability (extrapolative accuracy) is one important feature in species distribution models, required in several ecological and conservation biological applications. This study uses 10 modelling techniques and nationwide data on both (1) species distribution of birds, butterflies, and plants and (2) climate and land cover in Finland to investigate whether good interpolative prediction accuracy for models comes at the expense of transferability – i.e. markedly worse performance in new areas. Models’ interpolation and extrapolation performance was primarily assessed using AUC (the ...

 

Modelling the spatial distribution of tree species with fragmented populations from abundance data

  
Community Ecology, Vol. 10, No. 2. (1 December 2009), pp. 215-224, https://doi.org/10.1556/comec.10.2009.2.12

Abstract

Spatial distribution modelling can be a useful tool for elaborating conservation strategies for tree species characterized by fragmented and sparse populations. We tested five statistical models—Support Vector Regression (SVR), Multivariate Adaptive Regression Splines (MARS), Gaussian processes with radial basis kernel functions (GP), Regression Tree Analysis (RTA) and Random Forests (RF)—for their predictive performances. To perform the evaluation, we applied these techniques to three tree species for which conservation measures should be elaborated and implemented: one Mediterranean species ( Quercus suber ) ...

This page of the database may be cited as:
Integrated Natural Resources Modelling and Management - Meta-information Database. http://mfkp.org/INRMM/tag/random-forest

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.