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Author: Aayush Malik

Data Science Associate

International Initiative for Impact Evaluation

New Delhi | London | Washington DC

Welcome

We welcome you to the 3ie tutorial on applications of remote sensing for impact evaluation. Our hope is that equipped with the theoretical considerations and the practical hands-on code, you will be in a better position to decide which application of remote sensing is suitable for your work and what can be done to calculate the same. Moreover, we believe that laden with this geospatial literacy of earth observation, you can generate evidence faster in a cost-effective manner and over a large area. With extensive on-ground surveys limited by the COVID-19 pandemic and socio-political scenarios, remote sensing can help you to scale your work effectively.

This tutorial uses Python and Jupyter Notebooks. Python is a general purpose programming language that is used extensively in the field of machine learning and data analytics. Jupyter Notebooks are executable notebooks which allow us to document and run our code simultaneoulsy using Markdown. To reduce the access barrier, we are sharing this tutorial as a Google Colab Notebook, so that users can run the code and access the tutorial without downloading any software if they desire to do so.

We provide a general guidance on usage of these software. However, we believe that prior experience of basic programming will be helpful.

Tutorial Structure

The tutorial is divided into four modules. We recommend Module 1 for the audience who are not familiar with the concepts of earth observation using remote sensing. It may include commissioners of evaluation, as well as leadership staff who want to be informed consumers of earth observation services. The module introduces the core concepts, opportunities, and challenges associate with remote sensing. The Modules 2, 3, and 4 are geared more towards an audience who have fundamental familiarity with Python and want to use it for doing earth observation. The only prerequisite for Module 2, 3, and 4 is familiarity with basics of coding, in particular Python, although we believe that those who have worked on languages such as R or Javascript may also find it easy to follow the tutorial.

Hands-on exercises are provided throughout the tutorial to provide readers with ample opportunities for practicing what they are learning. Additional Resources are provided at the end of the tutorial for those who would like to delve deeper into the field of remote sensing and its use for causal inference.

Please note that this tutorial is geared towards an audience which is primarily interested in measuring agriculture and water outcomes using Remote Sensing. Further applications of remote sensing such as population density estimation, urban sprawl measurement, object detection, land cover estimation, district/block level economic activity estimation, measurement of pollution indicators, species and biodiversity classification, city-wide solar power generation estimation, waterlogging estimation post flooding etc are some of the current applications of remote sensing which are not part of the scope of current tutorial, but could be developed if useful.

Tutorial Use-Cases

After completing this tutorial successfully, a learner will be able to do the following:

For instance, one may be interested in measuring the impact of an intervention on developing sustainable agriculture or checking deforestation. In this case counting the number of trees may not always yield into measurable and accurate outcomes, however measuring the greenery as shown by a vegetation around the intervention unit may give us valuable information. Such an information can be easily quanitified alongside other covariates of interest into an econometric model to investigate the causal effects of that intervention.

1. Introduction to Remote Sensing

1.1 What is Remote Sensing?

The term remote sensing is made up of two words: remote and sensing. Remote means being far away from the unit of observation (field, building, person) and sensing means using digital sensors to measure the data across time and space. Thus, remote sensing is the science of collecting measurements on units without coming in touch with them. Satellite Imagery is one form of remotely sensed data. For this tutorial, we will focus on satellite imagery only.