GeoHealth Thai Platform: towards a network to gather expertise, knowledge and resources in health geography

International audienceDriven by the recent awareness of the magnitude of climate and environmental changes and their impact on human health, interdisciplinary approaches are increasingly being implemented to understand health inequalities and the dynamics of diseases. Although the availability of data is growing, researchers are facing difficulties in identifying and accessing relevant data and, above all, in using these data, resulting in a paradoxically limited use of geographical information.The GeoHealth Thai Platform project aims to promote geographical and environmental approaches in the understanding of health inequalities through the use of Geographic Information Systems and Remote Sensing techniques. It proposes to address the difficulties encountered by many individual researchers by:•gathering experts and researchers together during workshops, in order to define the needs and identify the barriers to be solved; •training and providing expertise to researchers for the use of Geographic Information Systems and Remote Sensing techniques; •building an open geocatalogue to facilitate the access to spatial data.This project will be supported by a dedicated website, which will integrate the catalogue of geo-referenced data, together with online resources (documents, courses and tutorials). This poster will present the geocatalogue, at the heart of the project, as well as current and future project activities.GeoHealth Thai Platform is funded by Franco-Thai Cooperation Program in Higher Education and Research 2013-2014

Oil Palm Tree Detection with High Resolution Multi-Spectral Satellite Imagery

Oil palm tree is an important cash crop in Thailand. To maximize the productivity from planting, oil palm plantation managers need to know the number of oil palm trees in the plantation area. In order to obtain this information, an approach for palm tree detection using high resolution satellite images is proposed. This approach makes it possible to count the number of oil palm trees in a plantation. The process begins with the selection of the vegetation index having the highest discriminating power between oil palm trees and background. The index having highest discriminating power is then used as the primary feature for palm tree detection. We hypothesize that oil palm trees are located at the local peak within the oil palm area. To enhance the separability between oil palm tree crowns and background, the rank transformation is applied to the index image. The local peak on the enhanced index image is then detected by using the non-maximal suppression algorithm. Since both rank transformation and non-maximal suppression are window based, semi-variogram analysis is used to determine the appropriate window size. The performance of the proposed method was tested on high resolution satellite images. In general, our approach uses produced very accurate results, e.g., about 90 percent detection rate when compared with manual labeling

Enhanced Feature Pyramid Vision Transformer for Semantic Segmentation on Thailand Landsat-8 Corpus

Semantic segmentation on Landsat-8 data is crucial in the integration of diverse data, allowing researchers to achieve more productivity and lower expenses. This research aimed to improve the versatile backbone for dense prediction without convolutions—namely, using the pyramid vision transformer (PRM-VS-TM) to incorporate attention mechanisms across various feature maps. Furthermore, the PRM-VS-TM constructs an end-to-end object detection system without convolutions and uses handcrafted components, such as dense anchors and non-maximum suspension (NMS). The present study was conducted on a private dataset, i.e., the Thailand Landsat-8 challenge. There are three baselines: DeepLab, Swin Transformer (Swin TF), and PRM-VS-TM. Results indicate that the proposed model significantly outperforms all current baselines on the Thailand Landsat-8 corpus, providing F1-scores greater than 80% in almost all categories. Finally, we demonstrate that our model, without utilizing pre-trained settings or any further post-processing, can outperform current state-of-the-art (SOTA) methods for both agriculture and forest classes

Automatic Rice Crop Height Measurement Using a Field Server and Digital Image Processing

Rice crop height is an important agronomic trait linked to plant type and yield potential. This research developed an automatic image processing technique to detect rice crop height based on images taken by a digital camera attached to a field server. The camera acquires rice paddy images daily at a consistent time of day. The images include the rice plants and a marker bar used to provide a height reference. The rice crop height can be indirectly measured from the images by measuring the height of the marker bar compared to the height of the initial marker bar. Four digital image processing steps are employed to automatically measure the rice crop height: band selection, filtering, thresholding, and height measurement. Band selection is used to remove redundant features. Filtering extracts significant features of the marker bar. The thresholding method is applied to separate objects and boundaries of the marker bar versus other areas. The marker bar is detected and compared with the initial marker bar to measure the rice crop height. Our experiment used a field server with a digital camera to continuously monitor a rice field located in Suphanburi Province, Thailand. The experimental results show that the proposed method measures rice crop height effectively, with no human intervention required

Destriping MODIS Data by Facet Model and Histogram Matching

Abstract: The MODerate resolution Imaging Spectrometer (MODIS) is consisted of cross-track scan mirror and linear arrays of ten detectors. Because 1) the abnormal behavior of the response of each detector, 2) optical cross–talk and shortwave infrared thermal leak, and 3) reflectance, emissivity, or polarization of each scan mirror side are the cause of artificial strips appearing in the images. The histogram modification algorithm by Weinreb 1989 seemed to be the most promising way in correcting the strip noise. It is based on an assumption that each sensor view a statistically similar sub scenes. However, undesirable side-effect of Weinreb’s algorithm arise from the certain cases which are statistical differences sensitivity in the image data recorded by the individual detectors, and detector saturation generates stripe. In this paper, the modification of Weinreb’s algorithm was developed and implemented by acquiring image statistics from homogeneous subimages and removing saturated strip by a method based on local gray tone statistics from the facet model

A study of waterborne diseases during flooding using Radarsat-2 imagery and a back propagation neural network algorithm

Flood disasters are closely associated with an increased risk of infection, particularly from waterborne diseases. Most studies of waterborne diseases have relied on the direct determination of pathogens in contaminated water to assess disease risk. In contrast, this study aims to use an indirect assessment that employs a back propagation neural network (BPNN) for modelling diarrheal outbreaks using data from remote sensing and dissolved-oxygen (DO) measurements to reduce cost and time. Our study area is in Ayutthaya province, which was very severely affected by the catastrophic 2011 Thailand flood. BPNN was used to model the relationships among the parameters of the flood and the water quality and the risk of people becoming infected. Radarsat-2 scenes were utilized to estimate flood area and duration, while the flood water quality was derived from the interpolation of DO samples. The risk-ratio function was applied to the diarrheal morbidity to define the level of outbreak detection and the outbreak periods. Tests of the BPNN prediction model produced high prediction accuracy of diarrheal-outbreak risk with low prediction error and a high degree of correlation. With the promising accuracy of our approach, decision-makers can plan rapid and comprehensively preventive measures and countermeasures in advance

Stripe and ring artifact removal with combined wavelet--Fourier filtering

A fast, powerful and stable filter based on combined wavelet and Fourier analysis for the elimination of horizontal or vertical stripes in images is presented and compared with other types of destriping filters. Strict separation between artifacts and original features allowing both, suppression of the unwanted structures and high degree of preservation of the original image information is endeavoured. The results are validated by visual assessments, as well as by quantitative estimation of the image energy loss. The capabilities and the performance of the filter are tested on a number of case studies related to applications in tomographic imaging. The case studies include (i) suppression of waterfall artifacts in electron microscopy images based on focussed ion beam nanotomography, (ii) removal of different types of ring artifacts in synchrotron based X-ray microtomography and (iii) suppression of horizontal stripe artifacts from phase projections in grating interferometry