Changes in vegetation phenology and productivity in Alaska over the past two decades

Understanding trends in vegetation phenology and growing season productivity at a regional scale is important for global change studies, particularly as linkages can be made between climate shifts and the vegetation\u27s potential to sequester or release carbon into the atmosphere. Trends and geographic patterns of change in vegetation growth and phenology from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the state of Alaska over the period 2000 to 2018. Phenology metrics derived from the MODIS Normalized Difference Vegetation Index (NDVI) time-series at 250 m resolution tracked changes in the total integrated greenness cover (TIN), maximum annual NDVI (MAXN), and start of the season timing (SOST) date over the past two decades. SOST trends showed significantly earlier seasonal vegetation greening (at more than one day per year) across the northeastern Brooks Range Mountains, on the Yukon-Kuskokwim coastal plain, and in the southern coastal areas of Alaska. TIN and MAXN have increased significantly across the western Arctic Coastal Plain and within the perimeters of most large wildfires of the Interior boreal region that burned since the year 2000, whereas TIN and MAXN have decreased notably in watersheds of Bristol Bay and in the Cook Inlet lowlands of southwestern Alaska, in the same regions where earlier-trending SOST was also detected. Mapping results from this MODIS time-series analysis have identified a new database of localized study locations across Alaska where vegetation phenology has recently shifted notably, and where land cover types and ecosystem processes could be changing rapidly

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Remote Detection of Disturbance from Motorized Vehicle Use in Appalachian Wetlands

Wetland disturbance from motorized vehicle use is a growing concern across the Appalachian coalfields of southwestern Virginia and portions of adjacent states, particularly as both extractive industries and outdoor recreation development expand in regional communities. However, few attempts have been made in this region or elsewhere to adapt approaches that can assist researchers and land managers in remotely identifying and monitoring wetland habitats disturbed by motorized vehicle use. A comparative analysis of wetlands impacted and unimpacted by off-road vehicle activity at a public recreation area in Tazewell County, Virginia was conducted to determine if and how a common, satellite-derived index of vegetation health, normalized difference vegetation index (NDVI), can remotely detect wetland disturbance. NDVI values were consistently lower in wetlands impacted by several years of off-road vehicle use when compared to adjacent, unimpacted sites, with statistically-significant NDVI coldspots growing in size in impacted wetlands across the same time period. While considerations exist related to the resolution of data sources and the identification of specific modes of disturbance, NDVI and associated spatial analysis tools may provide a simple and cost-effective way for researchers and land managers to remotely monitor rates of wetland disturbance across mountainous portions of the eastern United States

Editorial for special issue: "Remote sensing of environmental changes in cold regions"

Cold regions, characterized by the presence of permafrost and extensive snow and ice cover, are significantly affected by changing climate. Of great importance is the ability to track abrupt and longer term changes to ice, snow, hydrology and terrestrial ecosystems that are occurring within these regions. Remote sensing allows for measurement of environmental variables at multiple spatial and temporal scales, providing key support for monitoring and interpreting the environmental changes occurring in cold regions. The recent advances in the application of remote sensing for the analysis of environmental changes in cold regions are documented in this Special Issue

Carbon fluxes in ecosystems of Yellowstone National Park predicted from remote sensing data and simulation modeling

<p>Abstract</p> <p>Background</p> <p>A simulation model based on remote sensing data for spatial vegetation properties has been used to estimate ecosystem carbon fluxes across Yellowstone National Park (YNP). The CASA (Carnegie Ames Stanford Approach) model was applied at a regional scale to estimate seasonal and annual carbon fluxes as net primary production (NPP) and soil respiration components. Predicted net ecosystem production (NEP) flux of CO₂is estimated from the model for carbon sinks and sources over multi-year periods that varied in climate and (wildfire) disturbance histories. Monthly Enhanced Vegetation Index (EVI) image coverages from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instrument (from 2000 to 2006) were direct inputs to the model. New map products have been added to CASA from airborne remote sensing of coarse woody debris (CWD) in areas burned by wildfires over the past two decades.</p> <p>Results</p> <p>Model results indicated that relatively cooler and wetter summer growing seasons were the most favorable for annual plant production and net ecosystem carbon gains in representative landscapes of YNP. When summed across vegetation class areas, the predominance of evergreen forest and shrubland (sagebrush) cover was evident, with these two classes together accounting for 88% of the total annual NPP flux of 2.5 Tg C yr^-1(1 Tg = 10¹²g) for the entire Yellowstone study area from 2000-2006. Most vegetation classes were estimated as net ecosystem sinks of atmospheric CO₂on annual basis, making the entire study area a moderate net sink of about +0.13 Tg C yr^-1. This average sink value for forested lands nonetheless masks the contribution of areas burned during the 1988 wildfires, which were estimated as net sources of CO₂to the atmosphere, totaling to a NEP flux of -0.04 Tg C yr^-1for the entire burned area. Several areas burned in the 1988 wildfires were estimated to be among the lowest in overall yearly NPP, namely the Hellroaring Fire, Mink Fire, and Falls Fire areas.</p> <p>Conclusions</p> <p>Rates of recovery for burned forest areas to pre-1988 biomass levels were estimated from a unique combination of remote sensing and CASA model predictions. Ecosystem production and carbon fluxes in the Greater Yellowstone Ecosystem (GYE) result from complex interactions between climate, forest age structure, and disturbance-recovery patterns of the landscape.</p

Using time series analysis to monitor deforestation dynamics in Miombo woodlands in Southern Highlands of Tanzania

Deforestation and forest fragmentation are threatening the Miombo woodlands in Southern Highlands of Tanzania. Miombo ecoregion is considered one of the world’s most valuable wilderness areas, providing livelihood for over 150 million people throughout the region, who are directly or indirectly depending on these ecosystem services. Monitoring deforestation process using satellite images enables the identification of ongoing changes and pressures facing the region, which is crucial for the sustainable management of the area. In this thesis the deforestation dynamics are analysed using LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) -time series algorithm. The algorithm uses temporal segmentation of spectral trajectories to extract change information from pixel time series derived from satellite images. The study is focused in miombo woodlands around a rural village of Mantadi, which is located in Tanzanian Southern Highlands. The capacity of LandTrendr algorithm to detect changes in Miombo woodland region is evaluated through the appliance of three spectral indices. The results are combined to examine the magnitude and spatial distribution of deforestation in the study area. The results show that to detect areas under any kind of disturbance, LandTrendr performs considerably well with all three indices. In more profound change magnitude detection, clear differences between the spectral indices can be noticed especially in finding subtler, low magnitude changes. The Normalized Burn Ratio (NBR) was found to be most stable index to detect changes in miombo woodlands. Combining the results from spectral indices increased the mapping accuracy by 10 %. The results indicate that 26,5 % of the whole study area has been under very high or high magnitude disturbance and 29,5 % under low or moderate magnitude disturbance between 1987 and 2018. This study proves that the LandTrendr algorithm is suitable for tracking long-term deforestation dynamics in Miombo woodland environments.Metsäkato ja metsien pirstoutuminen uhkaavat Tansanian eteläisten ylänköalueiden miombo-savanneja. Miombo-savannit muodostavat yhden maailman tärkeimmistä erämaa-alueista, tarjoten toimeentulon yli 150 miljoonalle ihmiselle, jotka ovat tavalla tai toisella riippuvaisia alueen ekosysteemipalveluista. Metsäkatoprosessien seuranta satelliittikuvien avulla mahdollistaa tapahtuvien muutosten ja paineiden tunnistamisen, mikä on elintärkeää alueen kestävälle hallinnalle. Tässä opinnäytetyössä metsien häviämisen dynamiikkaa analysoidaan LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) aikasarja-algoritmin avulla. Algoritmi hyödyntää spektraalisen kulkuradan ajallista segmentointia erottaakseen muutostiedot satelliittikuvien pikselikohtaisista aikasarjoista. Tutkimus kohdistuu miombo-metsäalueisiin Tansanian eteläisillä ylänköalueilla sijaitsevan Mantadi-kylän ympärillä. LandTrendr-algoritmin kykyä havaita muutoksia miombo-savanneilla arvioidaan kolmen spektraalisen indeksin avulla. Lopulta indeksien tulokset yhdistetään, jotta metsäkadon laajuutta ja alueellista jakautumista tutkimusalueella voitaisiin tutkia entistä tarkemmin. Tulokset osoittavat, että LandTrendr havaitsee metsissä tapahtuneet muutokset merkittävän hyvin kaikilla kolmella indeksillä. Perusteellisemmassa muutoksen voimakkuuden tarkastelussa havaitaan selviä eroja eri indeksien välillä, etenkin hienovaraisempien muutosten tunnistamisessa. Yksittäisistä indekseistä NBR (The Normalized Burn Ratio) osoittautui kaikkein vakaimmaksi miombo-savanneilla tapahtuvien muutosten tunnistamisessa. Kolmen spektraalisen indeksin tulosten yhdistäminen lisäsi kartoitustarkkuutta 10 %. Tulokset osoittavat, että 26,5 % tutkimusalueen metsistä on hävinnyt tai heikentynyt voimakkaasti ja 29,5 % on kokenut pieniä tai kohtalaisia häiriöitä vuosien 1987-2018 välillä. Tutkielma osoittaa, että LandTrendr algoritmin avulla metsäkatoa voidaan kartoittaa tehokkaasti Miombo-metsäympäristöissä pitkällä aikavälillä

Remote Sensing of Rapid Permafrost Landscape Dynamics

The global climate is warming and the northern high latitudes are affected particularly rapidly. Large areas of this region, or 24% of the northern hemisphere, are influenced by perennially frozen ground or permafrost. As permafrost is predominantly dependent on cold mean annual air temperatures, climate warming threatens the stability of permafrost. Since large amounts of organic carbon are stored within permafrost, its thaw would potentially release large amounts of greenhouse gases, which would further enhance climate warming (permafrost carbon feedback). Thermokarst and thermo-erosion are an indicator of rapid permafrost thaw, and may also trigger further disturbances in their vicinity. The vast Arctic permafrost regions and the wide distribution of thaw landforms makes the monitoring of thermokarst and thermo-erosion an important task to better understand the response of permafrost to the changing climate. Remote sensing is a key methodology to monitor the land surface from local to global spatial scales and could provide a tool to quantify such changes in permafrost regions. With the opening of satellite archives, advances in computational processing capacities and new data processing technology, it has become possible to handle and analyze rapidly growing amounts of data. In the scope of the changing climate and its influence of permafrost in conjunction with recent advances in remote sensing this thesis aims to answer the following key research questions: 1. How can the extensive Landsat data archive be used effectively for detecting typical land surface changes processes in permafrost landscapes? 2. What is the spatial distribution of lake dynamics in permafrost and which are the dominant underlying influencing factors? 3. How are key disturbances in permafrost landscapes (lake changes, thaw slumps and fire) spatially distributed and what are their primary influence factors? To answer these questions, I developed a scalable methodology to detect and analyze permafrost landscape changes in the ~29,000 km2 Lena Delta in North-East Siberia. I used all available peak summer data from the Landsat archive from 1999 through 2014 and applied a highly automated robust trend-analysis based on multi-spectral indices using the Theil-Sen algorithm. With the trends of surface properties, such as albedo, vegetation status or wetness, I was able identify local scale processes, such as thermokarst lake expansion and drainage, river bank erosion, and coastal inundation, as well as regional surface changes, such as wetting and greening at 30m spatial resolution. This method proved to be robust in indicating typical landscape change processes within an Arctic coastal lowland environment dominated by permafrost, which has been challenging for the application of optical remote sensing data. The scalability of the highly automated processing allows for further upscaling and advanced automated landscape process analysis. For a targeted analysis of well-known disturbances affecting permafrost (thermokarst lakes, retrogressive thaw slumps and wildfires), I used advanced remote sensing and image processing techniques in conjunction with the processed trend data. Here I combined the trend analysis with machine-learning classification and object based image analysis to detect lakes and to quantify their dynamics over a period from 1999 through 2014 within four different Arctic and Subarctic regions in Alaska and Siberia totaling 200,000 km². I found very strong precipitation driven lake expansion (+48.48 %) in the central Yakutian study area, while the study areas along the Arctic coast showed a slight loss of lake area (Alaska North Slope: -0.69%; Kolyma Lowland: -0.51%) or a moderate lake loss (Alaska Kobuk-Selawik Lowlands: -2.82%) due to widespread lake drainage. The lake change dynamics were characterized by a large variety of local dynamics, which are dependent on several factors, such as ground-ice conditions, surface geology, or climatic conditions. In an even broader analysis across four extensive north-south transects covering more than 2.3 million km², I focused on the spatial distribution and key factors of permafrost region disturbances. I found clear spatial patterns for the abundance of lakes (predominantly in ice-rich lowland areas), retrogressive thaw slumps (predominantly in ice-rich, sloped terrain, former glacial margin), and wildfires (boreal forest). Interestingly, apart from frequent drainage at the continuous-discontinuous permafrost interface, lake change dynamics showed spatial patterns of expansion and reduction that could not be directly related to specific variables, such as climate or permafrost conditions over large continental-scale transects. However, specific variables could get related to specific lake dynamics in within locally defined regions. Trend datasets of vegetation status (NDVI) were combined with high-resolution detailed geomorphological land-cover classification information and climate data to map tundra productivity in a heterogeneous landscape in northern Alaska. After decades of increasing productivity (greening), recently tundra vegetation showed a reverse trend of decreased productivity, which is predicted to continue with increasing temperatures and precipitation. In this thesis project I developed methods to analyze rapid landscape change processes of various scales in northern high latitudes with unprecedented detail by relying on spatially and temporally high resolution Landsat image time series analysis across very large regions. The findings allow a unique and unprecedented insight into the landscape dynamics of permafrost over large regions, even detecting rapid permafrost thaw processes, which have a small spatial footprint and thus are difficult to detect. The multi-scaled approach can help to support local-scale field campaigns to precisely prepare study site selection for expeditions, but also pan-arctic to global-scale models to improve predictions of permafrost thaw feedbacks and soil carbon emissions in a warming climate

Deriving Landscape-Scale Vegetation Cover and Aboveground Biomass in a Semi-Arid Ecosystem Using Imaging Spectroscopy

Environmental disturbances in semi-arid ecosystems have highlighted the need to monitor current and future vegetation conditions across the landscape. Imaging spectroscopy provide the necessary information to derive vegetation characteristics at high-spatial resolutions across large geographic areas. The work of this thesis is divided into two sections focused on using imaging spectroscopy to estimate and classify vegetation cover, and approximate aboveground biomass in a semi-arid ecosystem. The first half of this thesis assesses the ability of imaging spectroscopy to derive vegetation classes and their respective cover across large environmental gradients and ecotones often associated with semi-arid ecosystems. Optimal endmember selection and endmember bundling are coupled with classification and spectral unmixing techniques to derive vegetation species and abundances across Reynolds Creek Experimental Watershed (RCEW) in southwest Idaho at high spatial resolution (1 m). Results validated using field data indicated classification of aspen, Douglas fir, juniper, and riparian classes had an overall accuracy of 57.9% and a kappa coefficient of 0.43. Plant functional type classification, consisting of deciduous and evergreen trees, had an overall accuracy of 84.4% and a kappa coefficient of 0.68. Shrub, grass, and soil cover were predicted with an overall accuracy of 67.4% and kappa coefficient of 0.53. I conclude that imaging spectroscopy can be used to map vegetation communities in semi-arid ecosystems across large environmental gradients at high-spatial resolution and with high accuracy. The second half of this thesis focuses on monitoring the changes of aboveground biomass (AGB) from the 2015 Soda Fire, which burned portions of southwest Idaho and southeastern Oregon. Classifications derived in the first study are used to estimate AGB loss within a portion of RCEW, and these estimates are used to compare to gross estimates made over the full extent of the Soda Fire. I found that there was an AGB loss of 174M kg within RCEW and approximately 1.8B kg lost over the full extent of the Soda Fire. Additionally, a post-fire analysis was performed to provide insight into the amount of AGB that returned to both RCEW and the full extent of the Soda Fire. An estimated 2,100 – 208,000 kg of AGB had returned to the burned portion of RCEW one-year post fire, and approximately 3.2M kg of AGB had returned over the full extent of the Soda Fire. These AGB loss and re-growth estimates can be used by researchers and practitioners to monitor carbon flux across the Soda Fire and as baseline data for wildfires in semi-arid ecosystems

A review of carbon monitoring in wet carbon systems using remote sensing

Carbon monitoring is critical for the reporting and verification of carbon stocks and change. Remote sensing is a tool increasingly used to estimate the spatial heterogeneity, extent and change of carbon stocks within and across various systems. We designate the use of the term wet carbon system to the interconnected wetlands, ocean, river and streams, lakes and ponds, and permafrost, which are carbon-dense and vital conduits for carbon throughout the terrestrial and aquatic sections of the carbon cycle. We reviewed wet carbon monitoring studies that utilize earth observation to improve our knowledge of data gaps, methods, and future research recommendations. To achieve this, we conducted a systematic review collecting 1622 references and screening them with a combination of text matching and a panel of three experts. The search found 496 references, with an additional 78 references added by experts. Our study found considerable variability of the utilization of remote sensing and global wet carbon monitoring progress across the nine systems analyzed. The review highlighted that remote sensing is routinely used to globally map carbon in mangroves and oceans, whereas seagrass, terrestrial wetlands, tidal marshes, rivers, and permafrost would benefit from more accurate and comprehensive global maps of extent. We identified three critical gaps and twelve recommendations to continue progressing wet carbon systems and increase cross system scientific inquiry