Scaling Near-Surface Remote Sensing To Calibrate And Validate Satellite Monitoring Of Grassland Phenology

Phenology across the U.S. Great Plains has been modeled at a variety of field sites and spatial scales. However, combining these spatial scales has never been accomplished before, and has never been done across multiple field locations. We modeled phenocam Vegetation Indices (VIs) across the Great Plains Region. We used coupled satellite imagery that has been aligned spectrally, for each imagery band to align with one another across the phenocam locations. With this we predicted the phenocam VIs for each year over the six locations.Using our method of coupling the phenocam VIs and the meteorological data we predicted 38 years of phenocam VIs. This resulted in a coupled dataset for each phenocam site across the four VIs. Using the coupled datasets, we were able to predict the phenocam VIs, and examine how they would change over the 38 years of data. While imagery was not available for modeling the 38 years of weather data, we found weather data could act as an acceptable proxy. This means we were able to predict 38 years of VIs using weather data. A main assumption with this method, it that no major changes in the vegetation community took place in the 33 years before the imagery. If a large change did take place, it would be missed because of the data lacking to represent it. Using the phenocam and satellite imagery we were able to predict phenocam GCC, VCI, NDVI, and EVI2 and model them over a five-year period. This modeled six years of phenocam imagery across the Great Plains region and attempted to predict the phenocam VIs for each pixel of the satellite imagery. The primary challenge of this method is aggregating grassland predicted VIs with cropland. This region is dominated by cropland and managed grasslands. In many cases the phenology signal is likely driven by land management decisions, and not purely by vegetation growth characteristics. Future models that take this into account may provide a more accurate model for the region

Autumn Tree Phenology in Northern Wisconsin: Humans Versus Photographs

Ecosystem primary productivity halts when plants go dormant, and so the timing of dormancy as it relates to autumn phenology has been a focus of much interdisciplinary research. While monitoring plant phenology has its roots in directly observing specimens, digital sensors along with modern methods have also become a mainstay in phenology. Results from different methods often vary, so there is still a need to better understand how digital cameras record autumn phenology, especially in comparison with ground-based observations (Keenan et al. 2014). This study compared autumn phenology derived from direct ground observations with upward-facing fisheye photography, in the context of a larger research project (C.H.E.E.S.E.H.E.A.D.19), to precisely determine autumn tree phenology across 53 field sites in a heterogeneous temperate deciduous forest with over 220 individual trees and 1,000 digital photos sampled. Less-studied trees such as aspen (Populus spp.), birch (Betula spp.), and basswood (Tilia americana) were included in the project, as well as sugar maple and red maple (Acer spp.). The results show that inflection points from sigmoid curves and change point detection are in close agreement for critical transition dates including the start of leaf coloration (bias of change points later at i= -0.47 days) and end (i= -0.6), but with slightly less agreement for the start of leaf fall (bias of change points earlier at i= 3.8) and the end of leaf fall (bias of change points later at i= -3.39). While camera-derived transition dates correlated poorly with corresponding human-derived transition dates, the best relationship detected was between green chromatic coordinate (GCC) inflection points and leaf fall (when foliage is mostly absent from tree canopies). This work is intended as a pilot study for novel methodologies in the field of ground-based phenology

Remote Sensing of Savannas and Woodlands

Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome

Refining terrestrial biosphere feedbacks to climate change through precise characterization of terrestrial vegetation

Climate change is primarily driven by the human activities of fossil fuel combustion and land use change, which together result in the emissions of greenhouse gases such as carbon dioxide (CO₂). The terrestrial biosphere currently absorbs about a third of total anthropogenic CO₂ emissions, mostly through primary production by vegetation. The continued function of vegetation as a CO₂ sink is uncertain, as climate change has the potential to enhance or restrict the carbon uptake capacity of vegetation. Uncertainty in terrestrial vegetation function in the context of climate change, due in part to a lack of precise observations of leaf biochemistry and function with which to develop models, therefore limits the confidence of climate change projections. In its entirety, this thesis examines the potential for more precise observations of leaf function and their integration across a variety of models and observational scales. The first chapter provides an introductory overview of the subsequent four chapters and how each compliments the other. The second chapter demonstrates the role of the terrestrial biosphere in influencing the relationship between temperature change and cumulative CO₂ emissions. The third chapter provides adaptations to current radiative transfer modelling approaches to improve estimations of leaf biochemical constituents. The fourth chapter applies high spatiotemporal resolution observations of leaf phenology, the timing of leaf emergence and senescence, across North America to predict species-specific leaf phenology patterns under various emissions scenarios throughout the 21st century. The fifth chapter provides an approach to detect declines in ecosystem processes such as carbon uptake using observational leaf phenology networks. These chapter results indicate that 1) uncertainty in the land-borne fraction of carbon emissions contributes largely to uncertainty in the relationship between temperature change and emissions, 2) spectral subdomains and prior estimation of leaf structure improves leaf biochemistry estimations, 3) leaf senescence timing may diverge between boreal and temperate species under a high emissions scenario, and 4) declines in vegetational carbon uptake can be accurately detected using quantitative phenocam-based indicators. The fundamental and technical insights provided through this thesis will facilitate more reliable and functionally resolved projections of terrestrial biosphere feedbacks to climate change

Machine Learning in Sensors and Imaging

Machine learning is extending its applications in various fields, such as image processing, the Internet of Things, user interface, big data, manufacturing, management, etc. As data are required to build machine learning networks, sensors are one of the most important technologies. In addition, machine learning networks can contribute to the improvement in sensor performance and the creation of new sensor applications. This Special Issue addresses all types of machine learning applications related to sensors and imaging. It covers computer vision-based control, activity recognition, fuzzy label classification, failure classification, motor temperature estimation, the camera calibration of intelligent vehicles, error detection, color prior model, compressive sensing, wildfire risk assessment, shelf auditing, forest-growing stem volume estimation, road management, image denoising, and touchscreens

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed

Urban growth in Southern Africa : comparing 30 years of decadal imagery to census data

The total urban area of each study site was calculated for each time slice and the results were represented as maps depicting urban expansion. Graphs were also created depicting the total urban area vs. total population for each time slice (1970s, 1990s and 2000s)