Temporal optimisation of image acquisition for land cover classification with random forest and MODIS time-series

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8–10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

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

Deep Learning for mapping retrogressive thaw slumps across the Arctic

Retrogressive thaw slumps (RTS) are typical landscape processes of thawing and degrading permafrost. To this point, their distribution and dynamics are almost completely undocumented across many regions in the permafrost domain, partially due to the lack of data and monitoring techniques in the past. We are tackling this shortcoming by creating a deep learning based semantic segmentation framework to detect RTS, using multi-spectral PlanetScope, derived topographic (ArcticDEM) and multi-temporal Landsat Trend data. We created a highly automated processing pipeline, which is designed to create reproducible results and to be flexible for multiple input features. The processing workflow is based on the pytorch deep-learning framework and includes a variety of different segmentation architectures (UNet, UNet++, DeepLabV3), backbones and includes common data transformation techniques such as augmentation or normalization. We tested (training, validation) our DL based model in six different regions of 100 to 300 km² size across Canada (Banks Island, Tuktoyaktuk, Horton, Herschel Is.), and Siberia (Kolguev, Lena). We performed a regional cross-validation (5 regions training, 1 region validation) to test the spatial robustness and transferability of the algorithm. Furthermore, we tested different architectures backbones and loss-function to identify the best performing and most robust parameter sets. For training the models we created a training database of manually digitized and validated RTS polygons. The resulting model performance varied strongly between different regions with maximum Intersection over Union (IoU) scores between 0.15 and 0.58. The strong regional variation emphasizes the need for sufficiently large training data, which is representative for the massive variety of RTS. However, the creation of good training data proved to be challenging due to the fuzzy definition and delineation of RTS, particularly on the lower part. We have recently expanded our analysis to several RTS-rich regions across the Arctic (Fig.X) for the year 2021 and annual analysis (2018-2021) for RTS hot-spots, e.g. Banks Island, Peel Plateau and others. First model inference runs are promising for detecting RTS, but are still strongly overestimating the number and area of RTS, due to an excessive number of false positives. Model performance however, varies strongly between regions. Due to the strong variability of landscapes with RTS, we expect an improvement in model performance with an increase in the number and spatial distribution of training datasets. The community driven formation of the IPA Action Group RTSIn, which aims to create standardized RTS digitization protocols and training datasets for deep/machine-learning purposes will be a great boost for our purpose. With our standardized processing pipeline (preprocessing, training, inference), which allows to add more features based on user interest and data availability,, we tested our workflow for surface water and pingos with a mixture of publically available (Jones et al) and digitized data (Grosse pingos, Nitze water). These tests produced very good results and showed that the designed workflow is transferrable beyond the segmentation of RTS only. In the near future, we are aiming to integrate the community based training data and further gradually improve our training database. Within the framework of the ML4Earth project, we will create a temporal and pan-arctic monitoring system for RTS based on our highly automated processing chain. This will enable us to better understand pan-arctic RTS dynamics, their influencing factors, and consequences. Combining these spatial-temporal datasets with volumetric change information and carbon stock information will enable us to better quantify the consequences of thaw slumping across the permafrost domain

Continental-scale drivers of lake drainage in permafrost regions

Lakes are ubiquitous with high-latitude ecosystems, covering up to 60 percent of the land surface in some regions. Due to their influence on an array of key biogeophysical processes, the recent decline in lake area (via gradual and abrupt) observed across permafrost ecosystems may hold significant implications for shifting carbon and energy dynamics. Since lakes are often highly dynamic, understanding the main drivers of lake area change may ultimately enable the prediction of lake persistence in a warmer climate; key to anticipating future carbon-climate feedbacks from Arctic ecosystems. Here we conducted a data-driven analysis of >600k lakes across four continental-scale transects (Alaska, E Canada, W Siberia, E Siberia), combining remote sensing-derived lake shape parameters and spatial dynamics with other ecosystem datasets, such as ground temperatures, climate, elevation/geomorphology, and permafrost landscape parameters. We grouped our lake-change dataset into non-drained, partially and completely drained lakes (25-75 %, >75% loss) and used the RandomForest Feature Importance to calculate the relative importance of each parameter. Furthermore we predicted the probability of lake drainage under current environmental conditions and changing permafrost temperatures. Initial results suggest a strong importance of ground temperatures, lake shape, and local geomorphology on lake drainage. Spatially coarser datasets of permafrost and thermokarst properties did not reveal correlations with the result. Our drainage prediction results show distinct spatial patterns, which are matching regional lake drainage patterns. Our model estimated ground temperature as one of the main impact factors, with an increased drainage likelihood in permafrost regions from -5 to 0 °C. Going forward, we will further test for short term influences, such as extreme weather events and wildfire on widespread lake drainage. As this analysis is purely data-driven, a comparison or combination with physics-based models and predictions will help to better validate our analysis

Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska

Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km2) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling