English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 25m | 562 MB
Implement and interpret common machine learning techniques to build habitat suitability maps for birds in Peninsular Malaysia
In this course, you’ll work with real-world spatial data from Peninsular Malaysia to gain hands-on experience with mapping habitat suitability in conjunction with classical SDM models, such as MaxENT and Bioclim, and machine learning alternatives, such as random forests. The course will ensure that you are equipped to put spatial data and machine learning analysis into practice right away. You’ll have developed the skills necessary for working with ecological data and impress potential employers with your GIS and machine learning skills in R.
Throughout the course, you’ll learn how to map suitable habitats for species using R. You’ll also explore common ecological modeling techniques and species distribution modeling (SDM) using real-life data. As you advance, the course will guide you in implementing some of the common Geographic Information Systems (GIS) and spatial data analysis techniques in R and use it to access ecological data. You’ll perform common GIS and data analysis tasks related to SDMs, including accessing species-presence data, and get to grips with classical SDM techniques.
What You Will Learn
- Read data into the R environment from different sources
- Become well-versed with basic spatial data concepts and data types
- Analyze spatial data using R
- Implement GIS and machine learning methods in spatial data analysis
- Harness the power of GIS and machine learning in R for ecological modeling
- Discover GIS and data analysis tasks related to SDMs including accessing species-presence data via R
- Perform common GIS techniques on raster and other spatial data
Table of Contents
Introduction to the Species Distribution Modelling Course
1 INTRODUCTION TO THE COURSE – Instructor & Course Details
2 What is Species Distribution Modelling
3 Introduction to R for habitat suitability modelling
4 Conclusion to Section 1
The Basics of GIS for Species Distribution Models (SDMs)-Part 1
5 Where to Obtain Raster Data for Building SDMs
6 Accessing and Cleaning GBIF Data
7 Accessing GBIF Data via R’
8 Other Sources of Species Geo-location Data
9 Extract Species Geo-Location Data from Other Sources in R
10 Access Climate & Other Data via R
11 Working with Elevation Data in R
12 Deriving Topographic Products from Elevation Data
13 Conclusions to Section 2
Pre-Processing Raster and Spatial Data for SDMs
14 Some Prerequisites
15 CRS of the Data
16 Clip Raster Data to a Given Extent
17 Resize the Raster Data
18 Basic Data Visualization
19 Conclusions to Section 3
Classical SDM Techniques
20 Underlying Rationale
21 Bioclim
22 Model Evaluation
23 Maxent Interface in R
24 Maxent SDM in R
25 Maxent Analysis with the red package
26 Domain SDM in R
27 Conclusion to Section 4
Machine Learning Models for Habitat Suitability
28 Machine Learning Modelling
29 Pre-processing Steps Prior to Modelling with Presence and Absence Data
30 Prior to Implementing Machine Learning
31 GLMs for Habitat Suitability
32 Support Vector Machines
33 kNN
34 Random Forest (RF)
35 Gradient Boosting Machine (GBM)
36 Further Model Evaluation
37 Conclusions to Section 5
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