Mapping urban-rural gradients of settlements and vegetation using Sentinel-2 spectral-temporal metrics and regression-based unmixing with synthetic training data
Creators: name: Schug, Franz
name: Frantz, David
name: Okujeni, A.
name: Van Der Linden, 
name: Hostert, P.
Publisher: name: Zenodo
Year: 2021
Subjects: Urban
Sentinel-2
Unmixing
Land Cover Fractions
Spectral-Temporal Metrics

description: The impact of human activity on the Earth’s surface has rapidly increased over the last decades. Human activity includes agriculture and forestry, but is mainly driven by the presence of settlements. Knowing that future population growth will be important (from 7.4 billion in 2015 to 9.2 billion in 2040), there is an urgent need to understand spatial-temporal patterns of settlements and related processes on broad scales and along a rural-urban gradient. In contrast to classification approaches, subpixel mapping has been introduced to meet the requirements of heterogeneous areas with small object sizes. Here, we examine the robustness of spectral-temporal metrics (STM) from optical time series imagery as an input to machine-learning regression-based unmixing with synthetically mixed training data. While STM are robust with regard to phenology and data gaps, synthetically mixed training data requires few pure spectra and minimizes the need for training data collection. We map subpixel fractions of four land cover classes (<em>built-up/infrastructure</em>, <em>woody vegetation</em>, <em>non-woody vegetation</em> and <em>soil</em>) from Sentinel-2A/B for Germany and Austria on a 10m resolution. We use all cloud-free image acquisitions from 2017 and 2018 and use STM for regression-based unmixing. We study the performance of different feature set inputs for large area mapping and if specific feature sets stand out. The study area comprises 440,000km² and a population of 91.1 M. We used 15,398 acquisitions from 86 Sentinel-2 scenes. We compute 18 different STM and NDVI metrics and train 1,275 Kernel Ridge Regression models per class with randomly different input metrics sets. Training data is drawn from input features at 534 reference locations, selected based on a hierarchical classification scheme. For synthetic mixing, we adapt a parameter setup used in regional studies before and create 1200 inter- and intra-class artificial mixtures of pure spectra at different randomized binary and ternary mixtures as regression input. Validation used stratified random sampling with four population density classes (from GPW4) and four imperviousness classes (from Copernicus Imperviousness) in order to cover the range of the rural-urban gradient. We labelled 36,000 validation points and calculated reference fractions for 640 pixels at 160 sample locations. Model performance was evaluated based on a weighted score accounting for different regression qualities across all classes. We find that the use of STM for synthetic training data generation leads to robust regression models for large area fraction mapping. Model qualities at 20m resolution showed a compact range of MAE values from 0.11 to 0.20 (<em>built-up/infrastructure</em>), 0.15 to 0.23 (<em>woody veg</em>.), 0.13 to 0.22 (<em>non-woody veg</em>.) and 0.02 to 0.24 (<em>soil</em>). Model slope and intercept showed a similarly compact distribution with larger between-class differences. Median slope/intercept for built-up/infrastructure is 0.73/0.14. The overall performance of soil predictions was rather poor. Regarding model selection, we showed that maximum model quality was reached with three to four input STM. 660 models with less than five input STM were systematically compared using a weighted quality score. Three quality metrics (MAE, Slope, R²) for each class and each model were transformed using a logarithmic transform function that disproportionally penalizes values that are not among the high-performance models. This score was computed for all class qualities except soil and summed. The new ranking rewards models with good and balanced quality across the classes. We select a model using the first, second and third reflectance quartile and a maximum NDVI to map fractions across Germany and Austria (c.f. Figure 1). Its performance (MAE 0.13 / Slope 0.73 / R² 0.74) is comparable to that reached in similar studies at lower resolutions. Little difference in quality can be observed in different areas of the study region. This study uses machine learning regression with synthetically mixed training data for land cover fraction mapping on a national scale in a temperate environment. We showed that Sentinel-2 STM are robust input features for large area multi-class land cover subpixel mapping at a spatial resolution of 20m. A good overall quality of many different models implies a high model robustness, being less susceptible to varying feature quality and availability. Historic and spatial transferability of the models might benefit from this robustness. The approach is likely to yield good results for nations with similar environmental and architectural settings in Europe. Future research will particularly look into the transferability of the applied method to other regions and in a historic context. The integration of the results into large area products of land cover mapping or the benefit of additional data sources (e.g. Sentinel-1) is a further topic to be examined.