Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

IMPLICATIONS OF MODULATING GLACIERS AND SNOW COVER IN MONGOLIA

Mongolia’s cryosphere (glaciers and snow cover) drives ecosystem services and in turn, supports emerging economies in the water-restricted country. However, as Mongolia experiences long-term drought conditions and an increase in annual air temperatures at twice the global rate, the potential adverse effects of the changing cryosphere during a period of climate uncertainty will have cascading implications to water availability and economic development. Using several data sources and methods, I partitioned my dissertation into two components to determine the hydrologic and economic implications of modulations in Mongolia’s cryosphere. The first component is an examination of glacier recession in Mongolia’s Altai Mountains, where I identified the major drivers of glacier recession and the role of glaciers in the regional hydrology. In the second component we created novel techniques to detect snowmelt events and to determine their role in large annual livestock mortality across Mongolia. In chapter 2 we identified a rate of glacier recession of 6.4 ± 0.4 km2 yr-1 from 1990-2016, resulting in an overall decrease in glacier area of 43%, which were comparable to rates of recession in mountain ranges across Central Asia. In chapter 3 we found that glaciers contributed up to 22% of the regional hydrology in the glaciated Upper Khovd River Basin (UKRB) and glacier melt contributions began to decrease after 2016, suggesting an overall depletion of accumulation zones. In chapter 4, we developed a novel approach to detect snow melt events in Alaska, USA – due to its high satellite coverage, climate monitoring network, and previous existing studies – and produced a gridded geospatial data product. In chapter 5, we expanded on the novel methods developed in chapter 4 to determine the spatio-temporal role of snowmelt events on large annual livestock mortality in Mongolia. Results showed strong correlations between snowmelt events and mortality in the southern Gobi during the fall and the central and western regions during the spring. As Mongolia continues to develop climatically vulnerable economic industries, future modulations in Mongolia’s cryosphere will likely decrease regional water-availability and amplify annual livestock mortality

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

A review of machine learning applications in wildfire science and management

Artificial intelligence has been applied in wildfire science and management since the 1990s, with early applications including neural networks and expert systems. Since then the field has rapidly progressed congruently with the wide adoption of machine learning (ML) in the environmental sciences. Here, we present a scoping review of ML in wildfire science and management. Our objective is to improve awareness of ML among wildfire scientists and managers, as well as illustrate the challenging range of problems in wildfire science available to data scientists. We first present an overview of popular ML approaches used in wildfire science to date, and then review their use in wildfire science within six problem domains: 1) fuels characterization, fire detection, and mapping; 2) fire weather and climate change; 3) fire occurrence, susceptibility, and risk; 4) fire behavior prediction; 5) fire effects; and 6) fire management. We also discuss the advantages and limitations of various ML approaches and identify opportunities for future advances in wildfire science and management within a data science context. We identified 298 relevant publications, where the most frequently used ML methods included random forests, MaxEnt, artificial neural networks, decision trees, support vector machines, and genetic algorithms. There exists opportunities to apply more current ML methods (e.g., deep learning and agent based learning) in wildfire science. However, despite the ability of ML models to learn on their own, expertise in wildfire science is necessary to ensure realistic modelling of fire processes across multiple scales, while the complexity of some ML methods requires sophisticated knowledge for their application. Finally, we stress that the wildfire research and management community plays an active role in providing relevant, high quality data for use by practitioners of ML methods.Comment: 83 pages, 4 figures, 3 table

Characterisation of dust sources in Central Asia using remote sensing

Central Asian deserts are a significant source of dust in the middle latitudes, where economic activity and the health of millions of people are affected by dust storms. Detailed knowledge of sources of dust, controls on their activity, seasonality and atmospheric pathways are of crucial importance but to date, these data are limited. This thesis presents a detailed database ofsources ofdust emissions in Central Asia, from western China to the Caspian Sea, obtained by a multi-scale analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data. The multi-scale approach consists of the following steps: 1) MODIS Deep Blue Aerosol Optical Depth (DB AOD) at 10 km resolution, acquired between 2003 and 2014, is used to investigate the spatiotemporal distribution ofdust hotspots. 2) A dust enhancement algorithm was employed to obtain two composite images (Dust Enhancement Product, DEP) per day at 1 km resolution from MODIS Terra/Aqua acquisitions between 2003 and 2012, from which dust point sources (DPS) were detected by visual analysis of dust plumes and recorded in a database together with meteorological variables at each DPS location derived from the ERA-Interim reanalysis dataset. In all, more than 13500 DPS were identified. Using this multi-scale approach we provided a high resolution inventory of dust sources at sub-basin scale for Central Asia. Our analysis revealed several active source regions, the most active of which are the eastern part ofthe Taklmakan desert. An important finding was an increase in dust activity in the newly-formed desert ofthe Aralkum. Several ofthe identified dust source regions were not previously identified (e.g. sources in northern Afghanistan) or were not widely discussed in literature before (e.g. the Pre-Aral region in western Kazakhstan). Investigation of land surface characteristics and meteorological conditions at each source region revealed mechanisms for the formation of dust sources, including rapid desiccation of water bodies (e.g. Aral Sea), deflation of dust from fluvial sources (e.g. the Upper Amudarya region) and post-fire wind erosion (e.g. Pre-Aral and Lake Balkhash basins). Different seasonal patterns of dust emissions were observed as well as inter-annual trends. Comparison of DB AOD and DPS revealed a noticeable spatial bias in the AOD-based methods for detection of dust sources which is attributed to the fact that the highest atmospheric dust loadings are not always observed over the dust point sources