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

TaLAM: Mapping Land Cover in Lowlands and Uplands with Satellite Imagery

End-of-Project ReportThe Towards Land Cover Accounting and Monitoring (TaLAM) project is part of Ireland’s response to creating a national land cover mapping programme. Its aims are to demonstrate how the new digital map of Ireland, Prime2, from Ordnance Survey Ireland (OSI), can be combined with satellite imagery to produce land cover maps

Upland vegetation mapping using Random Forests with optical and radar satellite data

Uplands represent unique landscapes that provide a range of vital benefits to society, but are under increasing pressure from the management needs of a diverse number of stakeholders (e.g. farmers, conservationists, foresters, government agencies and recreational users). Mapping the spatial distribution of upland vegetation could benefit management and conservation programmes and allow for the impacts of environmental change (natural and anthropogenic) in these areas to be reliably estimated. The aim of this study was to evaluate the use of medium spatial resolution optical and radar satellite data, together with ancillary soil and topographic data, for identifying and mapping upland vegetation using the Random Forests (RF) algorithm. Intensive field survey data collected at three study sites in Ireland as part of the National Parks and Wildlife Service (NPWS) funded survey of upland habitats was used in the calibration and validation of different RF models. Eight different datasets were analysed for each site to compare the change in classification accuracy depending on the input variables. The overall accuracy values varied from 59.8% to 94.3% across the three study locations and the inclusion of ancillary datasets containing information on the soil and elevation further improved the classification accuracies (between 5 and 27%, depending on the input classification dataset). The classification results were consistent across the three different study areas, confirming the applicability of the approach under different environmental contexts

The Use of Meteorlogical Data to Improve Contrail Detection in Thermal Imagery over Ireland.

Aircraft induced contrails have been found to have a net warming influence on the climate system, with strong regional dependence. Persistent linear contrails are detectable in 1 Km thermal imagery and, using an automated Contrail Detection Algorithm (CDA), can be identified on the basis of their different properties at the 11 and 12 m w av.el enTgthshe algorithm s ability to distinguish contrails from other linear features depends on the sensitivity of its tuning parameters. In order to keep the number of false identifications low, the algorithm imposes strict limits on contrail size, linearity and intensity. This paper investigates whether including additional information (i.e. meteorological data) within the CDA may allow for these criteria to be less rigorous, thus increasing the contrail-detection rate, without increasing the false alarm rate

The importance of connected and interspersed urban green and blue space for biodiversity: A case study in Cork City, Ireland

Urban green and blue space (UGBS) is becoming increasingly important for supporting biodiversity, with the spatial configuration of these landscapes essential to supporting a range of taxa. The role of UGBS for supporting biodiversity is well established, but there remains a lack of consensus on the importance of the overall landscape configuration and the scale at which these configurations are analyzed. Moreover, statistical models are often compounded by coarse representations of UGBS that ignore ‘invisible’ spaces (i.e., gardens and brownfield sites). Using Sentinel-2 satellite data and a maximum likelihood classification, a comprehensive landcover map of Cork City, Ireland was produced with reliable accuracy. FRAGSTATS was then used to capture landscape metrics regarding the spatial configuration of the study area, at a city scale and at three spatial extents for each field site. Field surveys at 72 locations captured data on bird species richness and abundance, before generalized linear models (GLMs) were parameterized between biodiversity metrics and the landscape metrics at 50, 100, and 200 m scales. The UGBS classification revealed that two-thirds of the city is composed of green and blue space. The field surveys recorded 62 species in the city, while GLMs revealed that green space was a significant driver in increasing species richness and abundance, while blue space produced inversions in coefficient estimates, suggesting a more nuanced relationship. The edge effect phenomenon was suggested to play a key role in increasing bird diversity, with a diversified and varied urban landscape important. The impact of scale also affected how blue space was viewed as a connective network within the city, particularly in relation to biodiversity metrics. Overall, this study has demonstrated that UGBS is intrinsically linked to bird diversity. Moreover, 38% of the species recorded are listed as species of conservation concern in Ireland, highlighting how urban spaces can provide habitats for vulnerable species and should inform discussion on the role of geography within the implementation of conservation and planning initiatives for urban environs

Application of repeat-pass TerraSAR-X staring spotlight interferometric coherence to monitor pasture biophysical parameters: limitations and sensitivity analysis

This paper describes the potential and limitations of repeat-pass synthetic aperture radar interferometry (InSAR) to retrieve the biophysical parameters of intensively managed pastures. We used a time series of eight acquisitions from the TerraSAR-X Staring Spotlight (TSX-ST) mode. The ST mode is different from conventional Stripmap mode; therefore, we adjusted the Doppler phase correction for interferometric processing. We analyzed the three interferometric pairs with an 11-day temporal baseline, and among these three pairs found only one gives a high coherence. The results show that the high coherence in different paddocks is due to the cutting of the grass in the month of June, however the temporal decorrelation in other paddocks is mainly due to the grass growth and high sensitivity of the X-band SAR signals to the vegetation cover. The InSAR coherence (over coherent paddocks) shows a good correlation with SAR backscatter (R2dB=0.65, p<0.05) and grassland biophysical parameters (R2Height=0.55, p<0.05, R2Biomass=0.75,p<0.05). It is thus possible to detect different management practices (e.g., grazing, mowing/cutting) using SAR backscatter (dB) and coherence information from high spatial short baseline X-band imagery; however, the rate of decorrelation over vegetated areas is high. Initial findings from the June pair show the possibility of change detection due to the grass growth, grazing, and mowing events by using InSAR coherence information. However, it is not possible to automatically categorize different paddocks undergoing these changes based only on the SAR backscatter and coherence values, due to the ambiguity caused by tall grass flattened by the wind

The contribution of cloud cover to the radiation budget of the Greenland ice sheet

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