LAND SURFACE PHENOLOGICAL RESPONSES TO LAND USE AND CLIMATE VARIATION IN A CHANGING CENTRAL ASIA

During the last few decades Central Asia has experienced widespread changes in land cover and land use following the socio-economic and institutional transformations of the region catalyzed by the USSR collapse in 1991. The decade-long drought events and steadily increasing temperature regimes in the region came on top of these institutional transformations, affecting the long term and landscape scale vegetation responses. This research is based on the need to better understand the potential ecological and policy implications of climate variation and land use practices in the contexts of landscape-scale changes dynamics and variability patterns of land surface phenology responses in Central Asia. The land surface phenology responses - the spatio-temporal dynamics of terrestrial vegetation derived from the remotely sensed data - provide measurements linked to the timing of vegetation growth cycles (e.g., start of growing season) and total vegetation productivity over the growing season, which are used as a proxy for the assessment of effects of variations in environmental settings. Local and regional scale assessment of the before and after the USSR collapse vegetation response patterns in the natural and agricultural systems of the Central Asian drylands was conducted to characterize newly emerging links (since 1991) between coupled human and natural systems, e.g., socio-economic and policy drivers of altered land and water use and distribution patterns. Spatio-temporal patterns of bioclimatic responses were examined to determine how phenology is associated with temperature and precipitation in different land use types, including rainfed and irrigated agricultural types. Phenological models were developed to examine relationship between environmental drivers and effect of their altitudinal and latitudinal gradients on the broad-scale vegetation response patterns in non-cropland ecosystems of the desert, steppe, and mountainous regional landscapes of Central Asia.The study results demonstrated that the satellite derived measurements of temporal cycles of vegetation greenness and productivity data was a valuable bioclimatic integrator of climatic and land use variation in Central Asia. The synthesis of broad-scale phenological changes in Central Asia showed that linkages of natural and human systems vary across space and time comprising complex and tightly integrated patterns and processes that are not evident when studied separately

Environmental Drivers of NDVI-Based Vegetation Phenology in Central Asia

Through the application and use of geospatial data, this study aimed to detect and characterize some of the key environmental drivers contributing to landscape-scale vegetation response patterns in Central Asia. The objectives of the study were to identify the variables driving the year-to-year vegetation dynamics in three regional landscapes (desert, steppe, and mountainous); and to determine if the identified environmental drivers can be used to explain the spatial-temporal variability of these spatio-temporal dynamics over time. It was posed that patterns of change in terrestrial phenology, derived from the 8 km bi-weekly time series of Normalized Difference Vegetation Index (NDVI) data acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites (1981–2008), can be explained through a multi-scale analysis of a suite of environmental drivers. Multiple linear stepwise regression analyses were used to test the hypotheses and address the objectives of the study. The annually computed phenological response variables or pheno-metricstime (season start, season length, and an NDVI-based productivity metric) were modeled as a function of ten environmental factors relating to soil, topography, and climate. Each of the three studied regional landscapes was shown to be governed by a distinctive suite of environmental drivers. The phenological responses of the steppe landscapes were affected by the year-to-year variation in temperature regimes. The phenology of the mountainous landscapes was influenced primarily by the elevation gradient. The phenological responses of desert landscapes were demonstrated to have the greatest variability over time and seemed to be affected by soil carbon content and year-to-year variation of both temperature regimes and winter precipitation patterns. Amounts and scales of observed phenological variability over time (measured through coefficient of variation for each pheno-metrictime) in each of the regional landscapes were interpreted in terms of their resistance and resilience capacities under existing and projected environmental settings

Satellite Time Series and Google Earth Engine Democratize the Process of Forest-Recovery Monitoring over Large Areas

Contemporary forest-health initiatives require technologies and workflows that can monitor forest degradation and recovery simply and efficiently over large areas. Spectral recovery analysis—the examination of spectral trajectories in satellite time series—can help democratize this process, particularly when performed with cloud computing and open-access satellite archives. We used the Landsat archive and Google Earth Engine (GEE) to track spectral recovery across more than 57,000 forest harvest areas in the Canadian province of Alberta. We analyzed changes in the normalized burn ratio (NBR) to document a variety of recovery metrics, including year of harvest, percent recovery after five years, number of years required to achieve 80% of pre-disturbance NBR, and % recovery the end of our monitoring window (2018). We found harvest areas in Alberta to recover an average of 59.9% of their pre-harvest NBR after five years. The mean number of years required to achieve 80% recovery in the province was 8.7 years. We observed significant variability in pre- and post-harvest spectral recovery both regionally and locally, demonstrating the importance of climate, elevation, and complex local factors on rates of spectral recovery. These findings are comparable to those reported in other studies and demonstrate the potential for our workflow to support broad-scale management and research objectives in a manner that is complimentary to existing information sources. Measures of spectral recovery for all 57,979 harvest areas in our analysis are freely available and browseable via a custom GEE visualization tool, further demonstrating the accessibility of this information to stakeholders and interested members of the public

Monitoring Hydro Temporal Variability in Alberta, Canada with Multi-Temporal Sentinel-1 SAR Data

Freely available high temporal and spatial resolution synthetic aperture radar (SAR) satellite data such as Sentinel-1 have made it possible for almost near-real time monitoring of surface water extent. We present a method to track temporal variability in surface water extent, hereafter hydro temporal variability (HTV) in Alberta, Canada. Multi-temporal Sentinel-1-C band SAR data was used to classify each pixel in a pixel stack across time into water or non-water. This dataset can tell the percent of time a given 10 m × 10 m pixel was seen as open water. HTV was then summarized by calculating the percentage of the total pixel stack, which was detected as water. Comparison to the waterbodies in the Government of Alberta Base Features Hydrography Polygon dataset shows that the HTV dataset is able to differentiate between permanent and recurring lakes as well as capture rivers with a width of over 30 m. It is anticipated that the methodology presented here will be further enhanced and refined with imagery available for 2018 and beyond, due to now operational Sentinel-1B satellite and future RADARSAT Constellation Mission (2018), which will both provide improved data opportunities

Monitoring Cumulative Effects of Human Activity on Alberta’s (Canada) Biodiversity

Due to its significant natural resource wealth, the province of Alberta in western Canada has experienced rapid expansion of related industrial activities (forestry, agriculture, and oil and gas exploration and development), as well as associated population growth, over recent decades. The resulting rate of conversion of natural ecosystems to support these activities led to increasing concerns regarding their cumulative effects on Alberta's biodiversity. As a result, in 2007, the Alberta Biodiversity Monitoring Institute (ABMI) was formally established to monitor the status and trends of Alberta's species, their habitats, as well as human footprint (HF). The ABMI is a not-for-profit scientific organization that operates at arm's length from government and industry. The goal of the ABMI is to provide relevant scientific information on the state of Alberta's biodiversity to support natural resource and land-use decision making in the province. To meet this goal, the ABMI employs a systematic grid of 1,656 site locations across the province, spaced 20 km apart, to collect biodiversity information on terrestrial and wetland sites. At each location, data and field samples are collected for a wide range of plant and animal species through on-the-ground measurements, and also using motion-sensitive camera traps and acoustic technology. Since 2007, over 480,000 specimens-data on over 3000 species have been collected and processed, many of which represent new scientific records for the province, sometimes new records for Canada, and even records new to science. Annually, a percentage of the total sites is surveyed, with the sites revisited approximately every 7 years to measure trend in species abundance. In addition to field surveys, Alberta's land cover and human footprint is monitored using remote sensing technology at two spatial scales. To report on patterns and trend in human footprint, the ABMI classifies human footprint into 115 feature types, which are then rolled up into the categories of energy, forestry, agriculture, residential and industrial, human-created water bodies, and transportation. The ABMI's accumulated biodiversity and HF database supports the creation of predictive species models that provide information on spatial distribution, habitat associations, responses to HF, and predicted relative abundance for over 800 species, including mammals, birds, soil mites, vascular plants, mosses (bryophytes), and lichens. The scale and depth of the ABMI's monitoring program and biodiversity data make it a unique program nationally, and a leader internationally. In addition to ongoing protocol development and data analysis, the ABMI is committed to deriving value from its data and information for a wide range of Alberta stakeholders through concerted knowledge translation and stakeholder engagement efforts.peerReviewe

Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning.

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands-a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage-in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management

Google Earth Engine, Open-Access Satellite Data, and Machine Learning in Support of Large-Area Probabilistic Wetland Mapping

Modern advances in cloud computing and machine-leaning algorithms are shifting the manner in which Earth-observation (EO) data are used for environmental monitoring, particularly as we settle into the era of free, open-access satellite data streams. Wetland delineation represents a particularly worthy application of this emerging research trend, since wetlands are an ecologically important yet chronically under-represented component of contemporary mapping and monitoring programs, particularly at the regional and national levels. Exploiting Google Earth Engine and R Statistical software, we developed a workflow for predicting the probability of wetland occurrence using a boosted regression tree machine-learning framework applied to digital topographic and EO data. Working in a 13,700 km2 study area in northern Alberta, our best models produced excellent results, with AUC (area under the receiver-operator characteristic curve) values of 0.898 and explained-deviance values of 0.708. Our results demonstrate the central role of high-quality topographic variables for modeling wetland distribution at regional scales. Including optical and/or radar variables into the workflow substantially improved model performance, though optical data performed slightly better. Converting our wetland probability-of-occurrence model into a binary Wet-Dry classification yielded an overall accuracy of 85%, which is virtually identical to that derived from the Alberta Merged Wetland Inventory (AMWI): the contemporary inventory used by the Government of Alberta. However, our workflow contains several key advantages over that used to produce the AMWI, and provides a scalable foundation for province-wide monitoring initiatives

The Synergistic Use of RADARSAT-2 Ascending and Descending Images to Improve Surface Water Detection Accuracy in Alberta, Canada

Large, e.g., provincial or national, scale near-real-time surface water monitoring is an ambitious task, which can be accomplished by using Synthetic Aperture Radar (SAR) satellite data. SAR has demonstrated the ability to distinguish water and land, but there are many common errors of commission and omission that arise due to the side-looking nature of SAR and due to some landcover types with similar backscatter like roads and pasture. A method is proposed to fix/mitigate these errors through the use of combined ascending/descending RADARSAT-2 image pairs and ancillary data. The results of a corrected water/land binary image were, on average, 99.4% accurate for the Boreal Forest Region (Utikuma) of Alberta, Canada, while for the Rocky Mountain Region (Westcastle) also in Alberta, the results proved to be 99.9% accurate when distinguishing water from land. These accuracies were achieved through the reduction of the water false positive rate and a slight reduction in the water true positive rate. These high accuracy values can be partially attributed to the relative low ratios of water to land in the study regions. We hope that these methods can be used and improved in order to move towards large scale dynamic surface water and wetland mapping

Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada

Advances in machine learning have changed many fields of study and it has also drawn attention in a variety of remote sensing applications. In particular, deep convolutional neural networks (CNNs) have proven very useful in fields such as image recognition; however, the use of CNNs in large-scale remote sensing landcover classifications still needs further investigation. We set out to test CNN-based landcover classification against a more conventional XGBoost shallow learning algorithm for mapping a notoriously difficult group of landcover classes, wetland class as defined by the Canadian Wetland Classification System. We developed two wetland inventory style products for a large (397,958 km2) area in the Boreal Forest region of Alberta, Canada, using Sentinel-1, Sentinel-2, and ALOS DEM data acquired in Google Earth Engine. We then tested the accuracy of these two products against three validation data sets (two photo-interpreted and one field). The CNN-generated wetland product proved to be more accurate than the shallow learning XGBoost wetland product by 5%. The overall accuracy of the CNN product was 80.2% with a mean F1-score of 0.58. We believe that CNNs are better able to capture natural complexities within wetland classes, and thus may be very useful for complex landcover classifications. Overall, this CNN framework shows great promise for generating large-scale wetland inventory data and may prove useful for other landcover mapping applications

Conceptual framework and uncertainty analysis for large-scale, species-agnostic modelling of landscape connectivity across Alberta, Canada

International audienceSustainable land-use planning should consider large-scale landscape connectivity. Commonly-used species-specific connectivity models are difficult to generalize for a wide range of taxa. In the context of multi-functional land-use planning, there is growing interest in species-agnostic approaches, modelling connectivity as a function of human landscape modification. We propose a conceptual framework, apply it to model connectivity as current density across Alberta, Canada, and assess map sensitivity to modelling decisions. We directly compared the uncertainty related to (1) the definition of the degree of human modification, (2) the decision whether water bodies are considered barriers to movement, and (3) the scaling function used to translate degree of human modification into resistance values. Connectivity maps were most sensitive to the consideration of water as barrier to movement, followed by the choice of scaling function, whereas maps were more robust to different conceptualizations of the degree of human modification. We observed higher concordance among cells with high (standardized) current density values than among cells with low values, which supports the identification of cells contributing to larger-scale connectivity based on a cut-off value. We conclude that every parameter in species-agnostic connectivity modelling requires attention, not only the definition of often-criticized expert-based degrees of human modification