Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale

Minimal training time in supervised retinal vessel segmentation

In this paper, we perform comparative analysis between different classifiers using the same experimental setup for supervised retinal vessel segmentation. The aim of this paper is to find supervised classifier that can obtain good segmentation accuracy with minimal training time. Minimizing the training time is essential when dealing with biomedical images. The more samples introduced to a learning model, the better it can adapt to the unseen data. The results indicate a trade-off between accuracy and training time can be obtained in a classifier trained by a Neural Network. When tested with a publicly available database, the learning model only requires less than 2 minutes in the learning phase and achieves overall accuracy of 94.54%

Classifying multisensor remote sensing data : Concepts, Algorithms and Applications

Today, a large quantity of the Earth’s land surface has been affected by human induced land cover changes. Detailed knowledge of the land cover is elementary for several decision support and monitoring systems. Earth-observation (EO) systems have the potential to frequently provide information on land cover. Thus many land cover classifications are performed based on remotely sensed EO data. In this context, it has been shown that the performance of remote sensing applications is further improved by multisensor data sets, such as combinations of synthetic aperture radar (SAR) and multispectral imagery. The two systems operate in different wavelength domains and therefore provide different yet complementary information on land cover. Considering the increase in revisit times and better spatial resolutions of recent and upcoming systems like TerraSAR-X (11 days; up to1 m), Radarsat-2 (24 days; up to 3 m), or RapidEye constellation (up to 1 day; 5 m), multisensor approaches become even more promising. However, these data sets with high spatial and temporal resolution might become very large and complex. Commonly used statistical pattern recognition methods are usually not appropriate for the classification of multisensor data sets. Hence, one of the greatest challenges in remote sensing might be the development of adequate concepts for classifying multisensor imagery. The presented study aims at an adequate classification of multisensor data sets, including SAR data and multispectral images. Different conventional classifiers and recent developments are used, such as support vector machines (SVM) and random forests (RF), which are well known in the field of machine learning and pattern recognition. Furthermore, the impact of image segmentation on the classification accuracy is investigated and the value of a multilevel concept is discussed. To increase the performance of the algorithms in terms of classification accuracy, the concept of SVM is modified and combined with RF for optimized decision making. The results clearly demonstrate that the use of multisensor imagery is worthwhile. Irrespective of the classification method used, classification accuracies increase by combining SAR and multispectral imagery. Nevertheless, SVM and RF are more adequate for classifying multisensor data sets and significantly outperform conventional classifier algorithms in terms of accuracy. The finally introduced multisensor-multilevel classification strategy, which is based on the sequential use of SVM and RF, outperforms all other approaches. The proposed concept achieves an accuracy of 84.9%. This is significantly higher than all single-source results and also better than those achieved on any other combination of data. Both aspects, i.e. the fusion of SAR and multispectral data as well as the integration of multiple segmentation scales, improve the results. Contrary to the high accuracy value by the proposed concept, the pixel-based classification on single-source data sets achieves a maximal accuracy of 65% (SAR) and 69.8% (multispectral) respectively. The findings and good performance of the presented strategy are underlined by the successful application of the approach to data sets from a second year. Based on the results from this work it can be concluded that the suggested strategy is particularly interesting with regard to recent and future satellite missions

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Historical Land use/Land cover classification and its change detection mapping using Different Remotely Sensed Data from LANDSAT (MSS, TM and ETM+) and Terra (ASTER) sensors: a case study of the Euphrates River Basin in Syria with focus on agricultural irrigation projects

This thesis deals spatially and regionally with the natural boundaries of the Euphrates River Basin (ERB) in Syria. Scientifically, the research covers the application of remote sensing science (optical remote sensing: LANDSAT-MSS, TM, and ETM+; and TERRA: ASTER); and methodologically, in Land Use/Land Cover (LULC) classification and mapping, automatically and/or semi-automatically; in LULC-change detection; and finally in the mapping of historical irrigation and agricultural projects for the extraction of differing crop types and the estimation of their areas. With regard to time, the work is based on the years 1975, 1987, 2005 and 2007. Initially, preprocessing of the satellite data (geometric- and radiometric- processing, image enhancement, best bands composite selection, transformation, mosaicing and finally subsetting) was carried out. Then, the Land Use/Land Cover Classification System (LCCS) of the Food and Agriculture Organization (FAO) was chosen. The following steps were followed in LULC- classification and change detection mapping: visual interpretation in addition to digital image processing techniques; pixel-based classification methods; unsupervised classification: ISODATA-method; and supervised classification and multistage supervised approaches using the algorithms: Maximum Likelihood Classifier (MLC), Neural Network classifier (NN) and Support Vector Machines (SVM). These were trialed on a test area to determine the optimized classification approach/algorithm for application on the whole study area (ERB) based on the available imagery. Pre- and post- classification change detection methods (comparison approaches) were used to detect changes in land use/land cover-classes (for the years 1975, 1987 and 2007) in the study area. The remote sensing methods show a high potential in mapping historical and present land use/land cover classes and its changes over time. Significant results are also possible for agricultural crop classification in relatively large regional areas (the ERB in Syria is almost 50,335 km²). Change trends in the study area and period was characterized by land-intensive agricultural expansion. The rapid, more labor- and capital- intensive growth in the agricultural sector was enabled by the introduction of fertilizer, improved access to rural roads and markets, and the expansion of the government irrigation projects. Irrigated areas increased 148 % in the past 32 years from 249,681 ha in 1975 to 596,612 ha in 2007

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

Polarimetric Synthetic Aperture Radar, Principles and Application

Demonstrates the benefits of the usage of fully polarimetric synthetic aperture radar data in applications of Earth remote sensing, with educational and development purposes. Includes numerous up-to-date examples with real data from spaceborne platforms and possibility to use a software to support lecture practicals. Reviews theoretical principles in an intuitive way for each application topic. Covers in depth five application domains (forests, agriculture, cryosphere, urban, and oceans), with reference also to hazard monitorin

A Human-Centered Framework for the Understanding of Synthetic Aperture Radar Images

The limited usage of SAR data in the end-user community and in applicative contexts testified the failure of the recent literature, in which the research privileged the automatic extraction of information at the expense of users' experience with data. The development of new products and processing frameworks providing user-friendly representations and extraction of the physical information is a necessary condition for the full exploitation of SAR sensors. In this Book, the necessity to restore users’ centrality in remote sensing data analysis is analyzed and achieved through the introduction of two new classes of RGB SAR products obtained via multitemporal processing, whose principal characteristics are the ease of interpretation and the possibility to be processed with simple, end-user-oriented technique. These proposed approach aims to definitely fill the gap between the academy and the applications. The rationale is to provide ready-to-use images, in which the technical expertise with electromagnetic models, SAR imaging and image processing has been absorbed in the products formation phase. In such way, the idea that SAR images are too complicated to be interpreted and processed without a high technical expertise in order to extract physical information is overcame