Assessing the suitability and sustainability of agricultural development in the North Rupununi region (Guyana) using polarimetric radar


In this novel research, we want to improve the management and protection of savanna areas in Rupununi (Guyana) by using satellite images and drone data.

The North Rupununi is situated in the southern interior of Guyana, South America, and supports a high terrestrial and freshwater biodiversity. This rich biodiversity is not only important for conservation but also supplies local people with a range of livelihood activities, including subsistence fishing and ecotourism. The North Rupununi is characterised by low topography and seasonal flooding and has recently been the target of major agro-business interest particularly for rice cultivation. Guyana is a low income country, and pressure to convert natural habitats in to large-scale industrial farms and associate infrastructure, especially access roads, is having an increasing impact on the North Rupununi. With this pressure have come water pollution and habitat destruction, resulting in the loss of species in general and ecological connectivity in particular.

Increasing pressures from climate change (e.g. the increase in extreme weather events including flooding and droughts) is further adding pressure on biodiversity and indigenous populations in the region. The only way to make sure further development is sustainable is to carry out country-scale assessments, which require periodic surveys. Although these are spectacular ecosystems, surveying them is very time demanding due to the remoteness and the logistic difficulties of reaching these vast areas during the wet season (most roads become impassable). Additionally, many features are more easily visible from above (e.g. location and distribution of surface water). Indigenous communities and key decision makers are therefore in need of inexpensive, simple and rapid methodologies that are able to provide periodic surveys of the hydrological and ecological status of their surrounding landscape.

We want to demonstrate that satellite remote sensing could be a key tool in achieving this. In this project we will use satellite Synthetic Aperture Radar (SAR). SAR is able to obtain images of the environment from space using microwaves. It allows us to acquire images independent of weather condition and solar illumination, which is very valuable in areas with frequent cloud cover. We will also use a cutting edge radar technology called polarimetry (PolSAR). The advantage of PolSAR is that we can use the polarisation of the radar echo to obtain more images and therefore more information about objects in the scene [ESA-PolSAR]. Furthermore, we will make use of the emerging technology of UAV (“drone”) based observation for field validation and rapid local assessment using low cost aircraft.
A strong motivation for using satellite images is that we entered a new era of freely available satellite data (e.g. the ESA Sentinel constellation missions [ESA-Sentinel]). We are experiencing a rapid growth of activities in the Space industry and the Earth Observation sector. When paired to the exponentially growing sector of unmanned aerial monitoring, this opportunity not only supports businesses activities but also provides many state of the art tools to the environmental management community.

The objective of this project is to use satellite images to extract information about floods, soil water content, land use (e.g. agricultural, woodland) and evaluate how these changed in the last decade. The development work will be accompanied by large fieldwork in Guyana with at least two visits to the Rupununi region. The results will feed in to an emerging integrated Landscape Planning and Management strategy for the North Rupununi region currently being championed by an alliance of indigenous and conservation organisations.

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Image Captions

Figure 1: Changes in Flookbourg (near Maracombe bay). Top row: radar polarimetric images. Left: 1st of April 2007; Right: 14th May 2007. The colours are related to the characteristics of the objects. Inside the red circle we identified changes to the vegetation (crops growing), and the mud flat edge. Bottom row: output of the change detector. The detector identifies the colours/objects that were added (on the left) and removed (on the right). ALOS-1 data 2007 (data provide by JAXA, id.1151).


Project objective: We want to use PolSAR combined with targeted field measurements, including drone surveys, to monitor the dynamics of flooding and soil erosion in the North Rupununi region. This is to assess the sustainability and impact of development.

Deliverables: In this project, we will set up a series of methodologies that starting from images acquired from space, which will be able to provide weekly update of floods and soil moisture. Among other products we are interested in monitoring the following events: change in extent of flooding areas, soil erosion, change in vegetation cover.

Novelty: PolSAR is a cutting edge technology and is very useful to retrieve biophysical parameters of vegetation and soil [ESA-PolSAR]. However, this technology has never being used for assessing sustainability of development in seasonally flooded savannas. The research work carried out in this project has the potential to reveal the effect of development in savannas. We will also be using state-of-the art drone imaging systems to scale up and validate local observations to the regional level.

Data (satellite): Archived PolSAR data are already available. Future acquisitions will be carried out synchronised to fieldwork. The datasets used will include the following satellite missions: ALOS-1 and ALOS-2 (Japanese Space Agency); RADARSAT-2 (Canadian Space Agency); COSMO-SkyMED (Italian Space Agency); Sentinel-1 (European Space Agency).

Algorithm development: In this project we will develop algorithms that exploit weekly available PolSAR images combined with sporadic ground measurements to monitor savannah areas and assess the sustainability of human activities.

1) We will monitor changes, such as flood extent and soil erosion or vegetation cover, by applying change detectors. One of the methodologies will be based on the use of optimisations of polarimetric data [Marino et al 2014]. Figure 1 shows an example of the algorithm output applied to satellite data.

2) Analysis of time series. This will allow to evaluate trends in biophysical parameters of grasses and vegetation, identifying soil erosion. This information will be used to assess the changes that the Rupununi region due to human intervention in agriculture and transport infrastructure.

Project Timeline

Year 1

Preparing a literature review on the topics: SAR, drone imaging, savanna and grass ecology. Fieldwork. Start working on detecting floods and monitor soil moisture changes with PolSAR. Attending international training events. Expected submission of a journal paper on monitoring floods with PolSAR.

Year 2

Monitor multi-year changes in flood extend and vegetation cover due to human interventions. Expected submission of a journal paper on retrieving biophysical parameters with PolSAR and drone data.

Year 3

Use models to evaluate the sustainability of human activities based on temporal trends observed. Starting writing the thesis chapters. Expected submission of journal paper on sustainability assessment.

Year 3.5

Complete thesis and submission.

& Skills

This is a multi-disciplinary project including topics related to (a) satellite Earth Observation; (b) drone surveys; (c) physical models (electromagnetic scattering); (d) data analysis; (e) floods, woodlands and grassland; (f) programming.

The successful candidate will have the opportunity to gain valuable skills in the context of: (a) analysing and processing satellite and drone images using Python; (b) planning and accomplishing drone campaigns; (c) developing analytical and empirical models to measure biophysical parameters of the environment; (d) using Geographical Information Systems (GIS) software.

The training will also include the attendance of major international training events such as the training on polarimetric SAR data, provide by ESA in Italy.

References & further reading

[ESA-PolSAR]:[ESA-Sentinel]:[Marino et al 2014]: Marino, A. and Hajnsek, I. (2014).A change detector based on an optimization with polarimetric SAR imagery. IEEE TGRS, 52(8).

Further Information

Please contact Dr. Armando Marino for further information (

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