Atmospheric corrections of low altitude thermal infrared airborne images acquired over a tropical cropped area

Corrections atmosphériques d'images thermiques de cultures tropicales acquises à basse altitude. / Accurate corrections of atmospheric effects on thermal infrared remote sensing data are an essential pre-requisite for the development of thermal infrared airborne-derived crop water stress indices. These corrections can be performed using ground surface temperature measurements, which are time consuming and expensive. Atmospheric effects can also be corrected using radiative transfer models that require knowledge of atmospheric status. The latter can be accurately characterized from radiosoundings, but these are usually unavailable. It can also be derived from meteorological model simulations, but spatial and temporal resolution are often too coarse. This study proposes performing atmospheric corrections by using temperature and relative humidity profiles acquired in flight from onboard sensors during data collection. Such measurements are used to document the atmospheric radiative transfer model MATISSE. First results from an experimentation over a tropical cropped area show that corrections are made with a 1.46 °K accuracy

Application of High-Resolution Thermal Infrared Remote Sensing and GIS to Assess the Urban Heat Island Effect

Day and night airborne thermal infrared image data at 5 m spatial resolution acquired with the 15-channel (0.45 micron - 12.2 micron) Advanced Thermal and Land Applications Sensor (ATLAS) over Alabama, Huntsville on 7 September, 1994 were used to study changes in the thermal signatures of urban land cover types between day and night. Thermal channel number 13 (9.6 micron - 10.2 micron) data with the best noise-equivalent temperature change (NEAT) of 0.25 C after atmospheric corrections and temperature calibration were selected for use in this analysis. This research also examined the relation between land cover irradiance and vegetation amount, using the Normalized Difference Vegetation Index (NDVI), obtained by ratioing the difference and the sum of the red (channel number 3: 0.60-0.63 micron) and reflected infrared (channel number 6: 0.76-0.90 micron) ATLAS data. Based on the mean radiance values, standard deviations, and NDVI extracted from 351 pairs of polygons of day and night channel number 13 images for the city of Huntsville, a spatial model of warming and cooling characteristics of commercial, residential, agricultural, vegetation, and water features was developed using a GIS approach. There is a strong negative correlation between NDVI and irradiance of residential, agricultural, and vacant/transitional land cover types, indicating that the irradiance of a land cover type is greatly influenced by the amount of vegetation present. The predominance of forests, agricultural, and residential uses associated with varying degrees of tree cover showed great contrasts with commercial and services land cover types in the center of the city, and favors the development of urban heat islands. The high-resolution thermal infrared images match the complexity of the urban environment, and are capable of characterizing accurately the urban land cover types for the spatial modeling of the urban heat island effect using a GIS approach

Aerial Semantic Mapping for Precision Agriculture using Multispectral Imagery

Nowadays constant technological evolution cover several necessities and daily tasks in our society. In particular, drones usage, given its wide vision to capture the terrain surface images, allows to collect large amounts of information with high efficiency, performance and accuracy. This master dissertation’s main purpose is the analysis, classification and respective mapping of different terrain types and characteristics, using multispectral imagery. Solar radiation flow reflected on the surface is captured by the used multispectral camera’s different lenses (RedEdge-M, created by Micasense). Each one of these five lenses is able to capture different colour spectrums (i.e. Blue, Green, Red, Near-Infrared and RedEdge). It is possible to analyse the various spectrum indices from the collected imagery, according to the fusion of different combinations between coloured bands (e.g. NDVI, ENDVI, RDVI. . . ). This project engages a ROS (Robot Operating System) framework development, capable of correcting different captured imagery and, hence, calculating the implemented spectral indices. Several parametrizations of terrain analysis were carried throughout the project, and this information was represented in semantic maps by layers (e.g. vegetation, water, soil, rocks). The obtained experimental results were validated in the scope of several projects incorporated in PDR2020, with success rates between 70% and 90%. This framework can have multiple technical applications, not only in Precision Agriculture, but also in vehicles autonomous navigation and multi-robot cooperation

Coastal Eye: Monitoring Coastal Environments Using Lightweight Drones

Monitoring coastal environments is a challenging task. This is because of both the logistical demands involved with in-situ data collection and the dynamic nature of the coastal zone, where multiple processes operate over varying spatial and temporal scales. Remote sensing products derived from spaceborne and airborne platforms have proven highly useful in the monitoring of coastal ecosystems, but often they fail to capture fine scale processes and there remains a lack of cost-effective and flexible methods for coastal monitoring at these scales. Proximal sensing technology such as lightweight drones and kites has greatly improved the ability to capture fine spatial resolution data at user-dictated visit times. These approaches are democratising, allowing researchers and managers to collect data in locations and at defined times themselves. In this thesis I develop our scientific understanding of the application of proximal sensing within coastal environments. The two critical review pieces consolidate disparate information on the application of kites as a proximal sensing platform, and the often overlooked hurdles of conducting drone operations in challenging environments. The empirical work presented then tests the use of this technology in three different coastal environments spanning the land-sea interface. Firstly, I use kite aerial photography and uncertainty-assessed structure-from-motion multi-view stereo (SfM-MVS) processing to track changes in coastal dunes over time. I report that sub-decimetre changes (both erosion and accretion) can be detected with this methodology. Secondly, I used lightweight drones to capture fine spatial resolution optical data of intertidal seagrass meadows. I found that estimations of plant cover were more similar to in-situ measures in sparsely populated than densely populated meadows. Lastly, I developed a novel technique utilising lightweight drones and SfM-MVS to measure benthic structural complexity in tropical coral reefs. I found that structural complexity measures were obtainable from SfM-MVS derived point clouds, but that the technique was influenced by glint type artefacts in the image data. Collectively, this work advances the knowledge of proximal sensing in the coastal zone, identifying both the strengths and weaknesses of its application across several ecosystems.Natural Environment Research Council (NERC

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Spatial characteristics of the remotely-sensed surface urban heat island in Baton Rouge, LA: 1988-2003

Our understanding of urban effects on local climate remains unsatisfactory due to several difficulties: 1) the inherent complexity of the city-atmosphere system, 2) lack of a clear conceptual theoretical framework for inquiry, and 3) the high expense and enormous difficulties of acquiring a sufficient quantity of high-quality, high-resolution (both spatially and temporally) observations in cities. Using remotely-sensed data, this study analyzes urban heat islands (UHI) that are manifested through an elevation in the surface thermal emissions within urban regions known as surface heat islands (SHI). The study area for this research endeavor is Baton Rouge, Louisiana. Whereas the surface air temperature-derived UHI did not portray an accurate representation of distinct changes in surface temperature across the study area, the remotely-sensed surface temperature-derived SHI proved to reveal microscale differences that the surface air temperature-derived UHI was unable to depict. This study also provided verification that altering amounts of vegetation within a given land cover over time can reveal changes in surface temperature values, thus providing a means to reconstruct and predict future SHIs. This was achieved through regression equations predicting surface temperatures from known NDVI values. Finally, the moist static energy parameter was evaluated to test for a better indicator of the UHI over time throughout the study area. A decreasing temporal trend in MSE was identified throughout the study period (1988 - 2003) whereas no significant linear trend occurred in air temperature. This is supported by change detection rates generated from a comparison of the 1988 and 2003 LANDSAT data sets, as well as the range in 1988 and 2003 predicted surface temperatures (as a function of land cover)