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142--ukot--horizon-scanning

Code for work on UKOT plant horizon scanning materials

175-son2019

State of Nature plants 2019

bda_r_demos

Bayesian Data Analysis demos for R

createidfneighbourhood

Frescalo neighbourhoods for the Ile-de-France

cyprus-utm-grids

Cyprus UTM 10km grid and function to create subdivisions in R

dunescaper

ecology-conservation

example-notebooks

Miscellaneous notebooks to use with Sentinel Hub

frequency-rescaling-demo

Example of how frequency scaling using local occupancy (FreScaLO) works

frescalo

R rewrite of Hill (2012; 10.1111/j.2041-210X.2011.00146.x) fortran code

frescaloneighbourhoods

Various frescalo neighbourhoods

frescalor

FRESCALO implemented in R

iso-3166-countries-with-regional-codes

ISO 3166-1 country lists merged with their UN Geoscheme regional codes in ready-to-use JSON, XML, CSV data sets

npmstrends

Beginning work on data extracts and trends for NPMS species

npmstrends2

Further dev work on NPMS species indicator

occassessswedeneg

occAssess example using Swedish GBIF Cerambycidae data

rangechangesims

Range Change simulations

robitt

Risk-Of-Bias in Temporal Trends in Ecology (ROBITT)

robitt_ideas

Response to ROBITT workshop (some ideas presented in isoslides)

sen2mosaic

Building cloud-free mosaics of Sentinel-2 data for land cover mapping is difficult, with existing tools still under-development and frequently confusing.

shinyforms

Easily create questionnaire-type forms with Shiny

simplefrescalodemo

soar

Species' occurrence analyses in R

starter-academic

🎓 创建一个学术网站. Easily create a beautiful academic résumé or educational website using Hugo, GitHub, and Netlify.

topographical-feature-extraction-using-machine-learning-techniques-from-sentinel-2a-imagery-

The advancement in the satellite technology has made it possible to easily and frequently obtain the satellite images of most of the regions in the Earth. The satellite data contains abundant amount of information which can be very useful for variety of societal applications. However, manual identification of the land cover in a particular area is a very challenging and time-consuming task. we propose a method for classifying the land types from Sentinel 2A imagery using various models like random forest, SVM, Naive Bayes, Decision Tree (CART) and validate which model better classifies them. QGIS software is used to generate training data for the classifier. The analysis reveals that random forest classifier outperforms the rest of the classification methods in terms of better accuracy. This automated approach can be applied to large sets of data, reducing the need for manual labeling.

usspensions

Calculations for USS pensions

visualisefresneighbours

Quick tool for visualising neighbourhoods used in Frescalo