Combining Hyperspectral Imaging and Small Unmanned Aerial Systems for Grapevine Moisture Stress Assessment

It has been shown that mild water deficit in grapevine contributes to wine quality, in terms of especially grape and subsequent wine flavor. Water deficit irrigation and selective harvesting are implemented to optimize quality, but these approaches require rigorous measurement of vine water status. While traditional in-field physiological measurements have made operational implementation onerous, modern small unmanned aerial systems (sUAS) have presented the unique opportunity for rigorous management across vast areas. This study sought to fuse hyperspectral remote sensing, sUAS, and sound multivariate analysis techniques for the purposes of assessing grapevine water status. High-spatial and -spectral resolution hyperspectral data were collected in the visible/near-infrared (VNIR; 400-1000nm) and short-wave infrared (SWIR; 950-2500 nm) spectral regions across three flight days at a commercial vineyard in the Finger Lakes region of upstate New York. A pressure chamber was used to collect traditional field measurements of stem water potential (ψstem) during image acquisition. We completed some preliminary exploration of spectral smoothing, signal-to-noise ratio, and calibration techniques in forging our experimental design. We then correlated our hyperspectral data with a limited stress range (wet growing season) of traditional measurements for ψstem using multiple linear regression (R2 between 0.34 and 0.55) and partial least squares regression (R2 between 0.36 and 0.39). We demonstrated statistically significant trends in our experiment, further qualifying the potential of hyperspectral data, collected via sUAS, for the purposes of grapevine water management. There was indication that the chlorophyll and carotenoid absorption regions in the VNIR, as well as several SWIR water band regions warrant further exploration. This work was limited since we did not have access to experimentally-controlled plots, and future work should ensure a full range of water stress. Ultimately, models will need validation in different vineyards with a full range of plant stress

GPU Accelerated Classifier Benchmarking for Wildfire Related Tasks

Forest fires cause devastating amounts of damage generating negative consequences in the economy, the environment, the populations’ quality of life and in worst case the loss of lives. Having this in mind, the quick and timely prediction of forest fires is a major factor in the mitigation or even negation of the aforementioned consequences. Remote sensing is the process of obtaining information about an object or phenomena without direct interaction. This is the premise on which satellites acquire data of planet Earth. These observations produce enormous amounts of data on a daily basis. This data can be used to find correlation between land surface variables and conditions that are prone to fire ignition. Recently, in this field of study, there has been an effort to automate the process of correlation using machine learning techniques, such as Support Vector Machines and Artificial Neural Networks, in conjunction with a data mining approach, where historical data of a specific area is analysed in order to sort out the major primers of forest fire ignitions and identifying trends. The drawback of this approach is the large amount of time even the simplest task takes to process. GPU processing is the most recent strategy to accelerate this process. The thesis aims to study the behaviour of GPU parallelized classifiers with the ever increasing amounts of data to process and understand if these are appropriate for use in forest predictive tasks

Biomass retrieval based on genetic algorithm feature selection and support vector regression in Alpine grassland using ground-based hyperspectral and Sentinel-1 SAR data

A general framework for the integration of multi-sensor data for dry and fresh biomass retrieval is proposed and tested in Alpine meadows and pastures. To this purpose, hyperspectral spectroradiometer (as simulation of hyperspectral imagery) and biomass samples were collected in field campaigns and Copernicus Sentinel-1 Interferometric Wide (IW) swath SAR backscattering coefficients were used. First, a genetic algorithm feature selection was performed on hyperspectral data, and afterwards the resulting most sensitive bands where combined with SAR data within a support vector regression (SVR) model. The most sensitive hyperspectral bands were mainly located in different regions of the SWIR range for both fresh and dry biomass, and in the red and near-infrared regions mainly for dry biomass, but with less influence for fresh biomass. The R (2) correlation values between the sampled and the estimated biomass range from 0.24 to 0.71. The relatively low performances are mainly related to the saturation effect in the optical bands, as well as to the paucity of points for high values of biomass. The methodology allows a better understanding of the interaction between grassland systems and the electromagnetic spectrum by offering a model with a reduced number of narrow bands in the context of a multi-sensor integration

Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety of existing data sources, the satellites continuously observing the surface of the Earth are key to the monitoring of our environment. The new generation of satellite sensors are tremendously increasing the possibilities of applications but also increasing the need for efficient processing methodologies in order to extract information relevant to the users' needs in an automatic or semi-automatic way. This is where machine learning comes into play to transform complex data into simplified products such as maps of land-cover changes or classes by learning from data examples annotated by experts. These annotations, also called labels, may actually be difficult or costly to obtain since they are established on the basis of ground surveys. As an example, it is extremely difficult to access a region recently flooded or affected by wildfires. In these situations, the detection of changes has to be done with only annotations from unaffected regions. In a similar way, it is difficult to have information on all the land-cover classes present in an image while being interested in the detection of a single one of interest. These challenging situations are called novelty detection or one-class classification in machine learning. In these situations, the learning phase has to rely only on a very limited set of annotations, but can exploit the large set of unlabeled pixels available in the images. This setting, called semi-supervised learning, allows significantly improving the detection. In this Thesis we address the development of methods for novelty detection and one-class classification with few or no labeled information. The proposed methodologies build upon the kernel methods, which take place within a principled but flexible framework for learning with data showing potentially non-linear feature relations. The thesis is divided into two parts, each one having a different assumption on the data structure and both addressing unsupervised (automatic) and semi-supervised (semi-automatic) learning settings. The first part assumes the data to be formed by arbitrary-shaped and overlapping clusters and studies the use of kernel machines, such as Support Vector Machines or Gaussian Processes. An emphasis is put on the robustness to noise and outliers and on the automatic retrieval of parameters. Experiments on multi-temporal multispectral images for change detection are carried out using only information from unchanged regions or none at all. The second part assumes high-dimensional data to lie on multiple low dimensional structures, called manifolds. We propose a method seeking a sparse and low-rank representation of the data mapped in a non-linear feature space. This representation allows us to build a graph, which is cut into several groups using spectral clustering. For the semi-supervised case where few labels of one class of interest are available, we study several approaches incorporating the graph information. The class labels can either be propagated on the graph, constrain spectral clustering or used to train a one-class classifier regularized by the given graph. Experiments on the unsupervised and oneclass classification of hyperspectral images demonstrate the effectiveness of the proposed approaches

Tree species discrimination in temperate woodland using high spatial resolution Formosat-2 time series

Assessment and mapping of the tree species distribution is an important technical task for forest ecosystem services and habitat monitoring. Since traditional methods (e.g. field surveys) used for the mapping of the tree species tend to be time consuming, date lagged and too expensive, a technology of remote sensing might potentially offer a practical solution for the problem of tree species mapping, especially over large areas. The main purpose of this study was to investigate the potential of Formosat-2 multi-spectral image time series for classification of the tree species in temperate woodlands. Since phenological variations might increase spectral separability of the trees species, additional aim of the study was to assess the possibility of using multispectral-image time series as an alternative to hyper-spectral data for forest type mapping. Noise from the Formosat-2 images was removed with the Whittaker smoother algorithm, which performed quite well although some additional work might be needed during the selection of the optimal regularization parameter. Several supervised classification methods, Support Vector Machines (SVM), Random Forest (RF) and Gaussian Mixture Model (GMM), were used to discriminate tree species from the image time series. All of the classifiers performed reasonably well, with classification accuracies from 88.5 % to 99.2 % (Kappa statistic), although SVM model was the most accurate, while GMM was the most efficient in terms of computing time. High classification accuracy also indicated that the multi-spectral image time series and remote sensing might be a useful method for the mapping of tree species

Estimation of soil types by non linear analysis of remote sensing data

International audienceThe knowledge of soil type and soil texture is crucial for environmental monitoring purpose and risk assessment. Unfortunately, their mapping using classical techniques is time consuming and costly. We present here a way to estimate soil types based on limited field observations and remote sensing data. Due to the fact that the relation between the soil types and the considered attributes that were extracted from remote sensing data is expected to be non-linear, we apply Support Vector Machines (SVM) for soil type classification. Special attention is drawn to different training site distributions and the kind of input variables. We show that SVM based on carefully selected input variables proved to be an appropriate method for soil type estimation

Hyperspectral characterization of freezing injury and its biochemical impacts in oilseed rape leaves

Automatic detection and monitoring of freezing injury in crops is of vital importance for assessing plant physiological status and yield losses. This study investigates the potential of hyperspectral techniques for detecting leaves at the stages of freezing and post-thawing injury, and for quantifying the impacts of freezing injury on leaf water and pigment contents. Four experiments were carried out to acquire hyperspectral reflectance and biochemical parameters for oilseed rape plants subjected to freezing treatment. Principal component analysis and support vector machines were applied to raw reflectance, first and second derivatives (SDR), and inverse logarithmic reflectance to differentiate freezing and the different stages of post-thawing from the normal leaf state. The impacts on biochemical retrieval using particular spectral domains were also assessed using a multivariate analysis. Results showed that SDR generated the highest classification accuracy (> 95.6%) in the detection of post-thawed leaves. The optimal ratio vegetation index (RVI) generated the highest predictive accuracy for changes in leaf water content, with a cross validated coefficient of determination (R2cv) of 0.85 and a cross validated root mean square error (RMSEcv) of 2.4161 mg/cm2. Derivative spectral indices outperformed multivariate statistical methods for the estimation of changes in pigment contents. The highest accuracy was found between the optimal RVI and the change in carotenoids content (R2CV = 0.70 and RMSECV = 0.0015 mg/cm2). The spectral domain 400–900 nm outperformed the full spectrum in the estimation of individual pigment contents, and hence this domain can be used to reduce redundancy and increase computational efficiency in future operational scenarios. Our findings indicate that hyperspectral remote sensing has considerable potential for characterizing freezing injury in oilseed rape, and this could form a basis for developing satellite remote sensing products for crop monitoring