Uncertainty quantification for probabilistic machine learning in earth observation using conformal prediction

Unreliable predictions can occur when using artificial intelligence (AI) systems with negative consequences for downstream applications, particularly when employed for decision-making. Conformal prediction provides a model-agnostic framework for uncertainty quantification that can be applied to any dataset, irrespective of its distribution, post hoc. In contrast to other pixel-level uncertainty quantification methods, conformal prediction operates without requiring access to the underlying model and training dataset, concurrently offering statistically valid and informative prediction regions, all while maintaining computational efficiency. In response to the increased need to report uncertainty alongside point predictions, we bring attention to the promise of conformal prediction within the domain of Earth Observation (EO) applications. To accomplish this, we assess the current state of uncertainty quantification in the EO domain and found that only 20% of the reviewed Google Earth Engine (GEE) datasets incorporated a degree of uncertainty information, with unreliable methods prevalent. Next, we introduce modules that seamlessly integrate into existing GEE predictive modelling workflows and demonstrate the application of these tools for datasets spanning local to global scales, including the Dynamic World and Global Ecosystem Dynamics Investigation (GEDI) datasets. These case studies encompass regression and classification tasks, featuring both traditional and deep learning-based workflows. Subsequently, we discuss the opportunities arising from the use of conformal prediction in EO. We anticipate that the increased availability of easy-to-use implementations of conformal predictors, such as those provided here, will drive wider adoption of rigorous uncertainty quantification in EO, thereby enhancing the reliability of uses such as operational monitoring and decision making

Remote Sensing Applications in Monitoring of Protected Areas

Protected areas (PAs) have been established worldwide for achieving long-term goals in the conservation of nature with the associated ecosystem services and cultural values. Globally, 15% of the world’s terrestrial lands and inland waters, excluding Antarctica, are designated as PAs. About 4.12% of the global ocean and 10.2% of coastal and marine areas under national jurisdiction are set as marine protected areas (MPAs). Protected lands and waters serve as the fundamental building blocks of virtually all national and international conservation strategies, supported by governments and international institutions. Some of the PAs are the only places that contain undisturbed landscape, seascape and ecosystems on the planet Earth. With intensified impacts from climate and environmental change, PAs have become more important to serve as indicators of ecosystem status and functions. Earth’s remaining wilderness areas are becoming increasingly important buffers against changing conditions. The development of remote sensing platforms and sensors and the improvement in science and technology provide crucial support for the monitoring and management of PAs across the world. In this editorial paper, we reviewed research developments using state-of-the-art remote sensing technologies, discussed the challenges of remote sensing applications in the inventory, monitoring, management and governance of PAs and summarized the highlights of the articles published in this Special Issue

Mapping historical forest biomass for stock-change assessments at parcel to landscape scales

Understanding historical forest dynamics, specifically changes in forest biomass and carbon stocks, has become critical for assessing current forest climate benefits and projecting future benefits under various policy, regulatory, and stewardship scenarios. Carbon accounting frameworks based exclusively on national forest inventories are limited to broad-scale estimates, but model-based approaches that combine these inventories with remotely sensed data can yield contiguous fine-resolution maps of forest biomass and carbon stocks across landscapes over time. Here we describe a fundamental step in building a map-based stock-change framework: mapping historical forest biomass at fine temporal and spatial resolution (annual, 30m) across all of New York State (USA) from 1990 to 2019, using freely available data and open-source tools. Using Landsat imagery, US Forest Service Forest Inventory and Analysis (FIA) data, and off-the-shelf LiDAR collections we developed three modeling approaches for mapping historical forest aboveground biomass (AGB): training on FIA plot-level AGB estimates (direct), training on LiDAR-derived AGB maps (indirect), and an ensemble averaging predictions from the direct and indirect models. Model prediction surfaces (maps) were tested against FIA estimates at multiple scales. All three approaches produced viable outputs, yet tradeoffs were evident in terms of model complexity, map accuracy, saturation, and fine-scale pattern representation. The resulting map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting and verification frameworks, and prioritizing parcels for protection or enrollment in improved management programs.Comment: Manuscript: 24 pages, 7 figures; Supplements: 12 pages, 5 figures; Submitted to Forest Ecology and Managemen

Landsat-based monitoring of annual wetland change in the main-stem Willamette River floodplain of Oregon, USA from 1972 to 2012

Despite holding substantial ecological value, wetlands in the United States have experienced a significant decline in both area and function over the past century with the majority of freshwater wetland loss attributed to agricultural conversion. Agriculture is the second largest industry in the State of Oregon and the State places substantial emphasis in its land use planning goals on the preservation of agricultural land. Oregon’s Willamette Valley accounts for the majority of agricultural output with 53% of the valley bottom classified as agricultural land. Additionally, the valley houses 70% of the state's population. The valley was once comprised of extensive wet prairies and abundant riparian forests along the Willamette River floodplain, but native ecosystems have been reduced to a fraction of their original distribution since Euro-American settlement in the mid 1800s. The few wetlands that remain are at high risk to loss and degradation from agricultural activity. Following national wetland conservation policies, Oregon has since attempted to monitor and regulate losses due to disturbance and modification of the State's remaining wetlands through a "no-net-loss" policy aiming to decrease wetland losses and replace disturbed wetlands through mitigation. The National Wetlands Inventory (NWI) was designed to produce detailed maps and status reports of the characteristics and extent of the nation's wetlands and help determine the efficacy of no-net-loss policy implementation on the nation’s wetlands. In some cases, the NWI has been found to have low categorical and spatial accuracy and coarse temporal resolution, with some maps over two decades old. Although Landsat satellite imagery was originally found to lack the needed spatial resolution for classification detail and wetness designation that aerial photography provided, Landsat has 40 years of freely available, high quality annual imagery and should be explored for use in annual wetland change detection. Our objectives were to: (1) Quantify and characterize spatial and ecological trends in annual wetland change through gain, loss, and conversion in the Willamette Valley; (2) Evaluate the effect of the no-net-loss federal wetland conservation policy change enacted in 1990 on trends in net wetland area; and (3) Describe a new methodology that reaches back through the over 40-year Landsat archive to map fine scale wetland and related land-use changes from 1972-2012. We used annual Landsat MSS and TM/ETM+ images from 1972 to 2012 to manually interpret loss, gain, and type conversion of wetland area in the two-year inundation floodplain of the Main-Stem Willamette River using TimeSync, Google Earth, and ArcMap. By creating Tasseled Cap Brightness, Greenness, and Wetness indices for MSS data that visually match TM/ETM+ Tasseled Cap images, we were able to construct a complete and consistent annual time series and utilize the entire Landsat archive. Additionally, with an extended time series, we were able to compare trends of annual net change in wetland area before and after the no-net-loss policy established under Section 404 of the Clean Water Act in 1990. We found that wetlands experienced annual loss, gain, and type conversion across the entire study period. Vegetated wetlands (emergent and riparian wetlands) experienced a 314 ha net loss of wetland area across the 40 year study period whereas non-vegetated wetlands (lacustrine and riverine wetlands) experienced a 393 ha net gain. All wetland types combined saw a 79 ha net increase in wetland area across the full study period. The majority of both gain and loss in the study area was attributed to and from agricultural conversion followed by urban land use. Time series analysis of the rate of change of net wetland area was calculated using the Theil-Sen (TS) Slope estimate analysis. For annual change of wetland area before and after 1990 no-net-loss policy implementations, the rate of annual wetland area lost slowed for riparian wetlands and reversed into trends of annual net gain in area of emergent wetlands. The rate of annual net area gained for lacustrine wetlands was slowed post-policy. Accuracy assessment of land use change polygons in the field was only able to capture 12% of our interpretations due to access restrictions associated with private land. In spite of a low sample size (n=45), overall accuracy of land use classification through wetland change polygons was at 80%. This accuracy increased to 91.1% when land use classes were aggregated to either wetland or upland categories, indicating that our methodology was more accurate at distinguishing between general upland and wetland than finer categorical classes

Climate Change Impacts in Virginia: Status of Natural Resource Data Records as Tools to Assess Continuing Trends

As scientists we pose the hypothesis that climate change over the past decades has left a signal in natural resource status and productivity in Virginia. This signal exists subsumed in a variety of data from crop and forestry production, to fishery landings, to spatial distribution of numerous plant and animal species of interest. Description of these signals in concert with known changes in climate descriptors (temperature, rainfall and more) provide a basis for hind-casting possible cause and effect relationships. If such relationships exist, and we hypothesize that they do, then projections of climate descriptors (temperature, rainfall and more) provide the basis for projections of impacts on defined natural resources, with obvious economic and societal impacts. Before a comprehensive analysis of extant data can occur we must start with a simple inventory of available data. As simplistic as this may sound we can find no single database that describes the general status of natural resources in Virginia over the past decades. Indeed, we suggest that the majority of such data exists as unpublished (in peer review, and in some instances even technical reports) compilations spread among the various state and federal natural resource agencies active in Virginia. This body of work had several goals; 1) to assess the scope of natural resource descriptive data available in the Commonwealth of Virginia, 2) to assemble an index of such data, and 3) develop a bibliography to serve as a resource for more comprehensive analyses in the future

Long-term Prairie Wetlands Extraction and Change Detection with Multi-spatial and Multi-temporal Remote Sensing Data

Prairie wetlands, also called “potholes”, provide both ecological and hydrological functions and have experienced dramatic change over the past century. This research aims to: 1) compare the capacity of Landsat and SPOT in mapping open water and wet areas with advanced classification methods; 2) monitor and quantify the changes in wetlands and drainage channels, between 1948 and 2009, with aerial photography; and 3) evaluate Landsat’s ability to extract historical wetland coverage data across seasons using a variety of methods. Results indicate that Landsat is capable for mapping open water, wet areas and other LULC types in PPR; however only 48.5% of wetland areas are identified as compared with air photos. Historical analysis of air photo generated wetland and drainage channels show that the whole basin’s wetlands rapidly decreased from 1958 to 1990 (24% to 13%) and slowly decreased from 1990 to 2009 (13% to 10%) with the least reduction in sub basin 1. Drainage channels slowly increased from 1958 to 1990 (119 km to 269 km) and dramatically increased from 1990 to 2009 (269 km to 931km). Wetland area is highly correlated with accumulated snowfall in the previous three years in sub basin 2 (r=0.91, p<0.05) due to its memory effect to previous water conditions. For the full basin, however, there were not enough years of data to prove this correlation. Even though the minimum distance algorithm in early spring is optimal for mapping wetlands in the Prairie Pothole Region (PPR), comparing with air photos, SPOT imagery underestimated wetlands smaller than 1200 m2, while Landsat imagery is not able to detect wetlands smaller than 900 m2 and underestimates areas smaller than 1600 m2. Although free-archived Landsat can detect water bodies larger than 900 m2, its ability to detect prairie wetland is limited due to missing numerous small-scale wetlands and misclassification of seasonal wetlands.

The Benefits of Time: Characterizing Intra-and Inter-Annual Variability in Oregon Wetland Ecosystems Using the Landsat Spectral-Temporal Domain

The use of Landsat data has historically been constrained to spectral and spatial information derived from a carefully selected image or set of images. However, free and open access to Landsat imagery combined with advances in data storage and computing are revolutionizing how the Landsat temporal domain is used to map and monitor land surface properties and land cover change. Since the opening of the USGS archives in 2008, many different time series analysis approaches have been developed without a unified framework for characterizing information extracted from dense time series of Landsat imagery. In Chapter 1, we define Spectral-Temporal Features (STFs) as discrete or continuous features derived from time series of remotely sensed observations. Like spectral indices, STFs represent a transformation of the original image data and can provide new information about land surface properties and other biophysical parameters. STFs offer a number of improvements over conventional spectral or spatial inputs, including seamless coverage over large extents, more consistent and stable feature sets for classification through time, and new information on both spectral and temporal variability in reflectance that can be related to biophysical parameters. To demonstrate how STFs can be applied in practice, we present a series of case studies spanning a range of geographic locations within different ecosystem types, and study objectives. These case studies illustrate relationships between different STFs and various biophysical parameters and yield insight into the specific ecological metrics that can be discovered and characterized with the spectral-temporal domain. With the release of collection-style Landsat products and continued advances in pre-processing algorithms, as well as availability of tiled Analysis Ready Data and improved access to cloud- and cluster-based computing resources such as Google Earth Engine, the Australian Data Cube, and Sentinel Hub, time series approaches are becoming increasingly prevalent. We argue that STFs provide new information on both spectral and temporal variability in reflectance in different ecosystems that can be related to biophysical parameters. Thus, there is a critical need to continue to review and standardize the discussion and application of STFs for locally-accurate mapping and monitoring of forested ecosystem dynamics. In Chapter 2, we test the utility of primary STFs derived from time series of all available Landsat TM/ETM+ observations, including Global Surface Water (GSW) features, for discriminating among wetlands at different categorical resolutions within the National Wetlands Inventory (NWI) classification taxonomy. We examine two key types of primary STFs, 1) reflectance STFs, which characterize reflectance values of spectral indices used, and 2) day of year (DOY) STFs, which quantify the timing of their associated reflectance STFs. As an exploratory measure, we also abstract and evaluate spatial-temporal climate features, such as the per-pixel annual maximum of daily maximum temperature, to yield insight into potential drivers of wetland characterization. Using an NWI reference dataset from two Oregon ecoregions in distinctly different eco-hydrological climate zones, we test classification agreement and examine relative performance of different classification inputs across ecoregions and wetland categories. We test an array of classifications that use consistent training and testing datasets, but vary the features and feature-sets used as model inputs. Beyond classification performance, we also explore categorical-level agreement and the importances of different features for differentiation within different wetland categories. Our aim is not to estimate the accuracy of the reference NWI map or monitor wetland change over time and space, but rather to build a framework for multi-level wetland classification across climate gradients. We found that STFs feature-sets consistently produced high overall accuracies and were able to accurately delineate wetland habitats across climate gradients and wetland categorical resolutions even further when combined with other features. Additionally, accuracies decreased with increasing categorical resolution in both energy-and water-limited ecosystems. Evaluation of individual feature importance for distinguishing between different wetland habitats showed that different features are more important for different climate gradients and categorical resolutions. However, GSW Occurrence was consistently valuable for both ecoregions across all categorical resolutions, exemplifying the value of utilizing the GSW dataset for wetland classification. Further, although not all types of features were found to be important in overall classification, in quantifying correlations between individual features and individual wetland habitat classification probabilities, we found that all feature types were had and strong positive and negative correlations with individual habitats. This indicates the importance of using the various features as inputs for wetland classification. In Chapter 3, we use all available Landsat imagery from 1985 - 2017 to explore how Pacific Northwest wetland ecosystems are changing over time in different climate zones and at varying categorical resolutions. Additionally, we investigate the long term changes in abstracted Landsat spectral-temporal features that are closely associated with different aspects of wetland hydro-ecological processes. We found that our annual classification model built from Landsat spectral-temporal features, climate-temporal features, and ancillary datasets performs well in showing change in wetland habitat. Individual STFs also display distinct changes in intra-annual wetland dynamics in the context of wetland land use change. In terms of long term wetland change, Willamette Valley wetlands are trending toward more non-vegetated wetlands, fewer vegetated wetlands, and extreme annual-conditions with the lower extrema occurring earlier in the year. In addition to other drivers, this change may be attributed to increased precipitation and increased temperature. In contrast North Basin wetlands are trending towards more vegetated wetlands, fewer non-vegetated wetlands, and extreme annual-conditions with the lower extrema occurring earlier in the year, except for max TCW which is trending towards later annual occurrence. Timing and persistence are key for wetland habitats and this study begins the work to examine change in both occurrence of wetland habitat type and timing of key hydrologic and phenological features and ecosystem drivers

A Knowledge-based approach of satellite image classification for urban wetland detection

Title from PDF of title page, viewed on July 30, 2014Thesis advisor: Wei JiVitaIncludes bibliographical references (pages 85-93)Thesis (M. S.)--Dept. of Geosciences. University of Missouri--Kansas City, 2014It has been a technical challenge to accurately detect urban wetlands with remotely sensed data by means of pixel-based image classification. This is mainly caused by inadequate spatial resolutions of satellite imagery, spectral similarities between urban wetlands and adjacent land covers, and the spatial complexity of wetlands in human-transformed, heterogeneous urban landscapes. Knowledge-based classification, with great potential to overcome or reduce these technical impediments, has been applied to various image classifications focusing on urban land use/land cover and forest wetlands, but rarely to mapping the wetlands in urban landscapes. This study aims to improve the mapping accuracy of urban wetlands by integrating the pixel-based classification with the knowledge-based approach. The study area is the metropolitan area of Kansas City, USA. SPOT satellite images of 1992, 2008, and 2010 were classified into four classes -- wetland, farmland, built-up land, and forestland -- using the pixel-based supervised maximum likelihood classification method. The products of supervised classification are used as the comparative base maps. For our new classification approach, a knowledge base is developed to improve urban wetland detection, which includes a set of decision rules of identifying wetland cover in relation to its elevation, spatial adjacencies, habitat conditions, hydro-geomorphological characteristics, and relevant geostatistics. Using ERDAS Imagine software's knowledge classifier tool, the decision rules are applied to the base maps in order to identify wetlands that are not able to be detected based on the pixel-based classification. The results suggest that the knowledge-based image classification approach can enhance the urban wetland detection capabilities and classification accuracies with remotely sensed satellite imageryAbstract -- List of illustrations -- List of tables -- Acknowledgements -- Introduction -- Literature review -- Methodology -- Findings and analysis -- Discussion and conclusion -- Reference lis

Past and Future Land Use Impacts of Canadian Oil Sands and Greenhouse Gas Emissions

The Canadian oil sands underlie 142,000 km2 of the boreal forest in northeastern Alberta. Oil sands production greenhouse gas (GHG) emissions increased from 15 million tonnes (Mt) to 55 Mt between 1990 and 2011. Their production represents the fastest-growing source of GHG emissions in Canada. A large body of studies show that oil sands industries have large environmental impacts, including effects on climate, land, water, and air quality but GHG emissions from oil sands land use disturbance and future land use impacts have yet to be examined in detail and the associated literature is scarce and incomplete. Our paper examines the historical and potential land use change and GHG emissions associated with oil sands development in Canada. Disturbance occurred between 1985 and 2009 from oil sands development were identified using remote sensing technique and mapped onto spatially explicit soil, biomass and peatlands carbon maps. We found that land use and GHG disturbance of oil sands production, especially in-situ technology that will be the dominant technology of choice for future oil sands development, are greater than previously reported. We estimate additional 500 km2 and 2,400 km2 of boreal forest including carbon-rich peatlands would be disturbed from surface mining and in-situ production, respectively, between 2012 and 2030; releasing additional 107–182 million tonnes of GHG from land use alone. Future efforts to monitor land use impacts of in-situ production are needed to reduce landscape impacts and associated GHG emissions. In addition, land reclamation after oil sands projects needs to be enforced for broad ecological benefits together with GHG benefits

Tree species mapping around reclaimed oil and gas wells sites using hyperspectral and Light Detection and Ranging (LiDAR) remote sensing

Oil and gas activities in Alberta require disturbing forested lands, among other ecosystems, in order to extract resources. Due to the number of oil and gas sites requiring reclamation, monitoring can be problematic. Remote sensing provides cost-effective, timely, and repeatable data of these areas in support of monitoring efforts. Support Vector Machine (SVM) and Multiple Endmember Spectral Mixture Analysis (MESMA) were tested in order to identify tree species around reclaimed and abandoned well sites near Cold Lake, Alberta using CHRIS satellite imagery with and without airborne LiDAR data. A hierarchical classification approach was employed, which achieved an accuracy of 83.4 % when using SVM together with CHRIS imagery and LiDAR. This positive result indicates the ability of remote sensing to support reclamation management and monitoring objectives within Alberta’s forested areas.Natural Science and Engineering Research Council of Canada (NSERC) CREATE scholarship (Advanced Methods, Education and Training in Hyperspectral Science and Technology; AMETHYST). Alberta Terrestrial Imaging Centre (ATIC). TECTERRA. Oil Sands Research and Information Network (OSRIN). Alberta Environment and Sustainable Resource Development (ESRD