Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Uncertainty analysis of object-based land cover classification using time series of Sentinel-2 data

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification

The Impact of Urbanization on Farmland Productivity: Implications for China’s Requisition–Compensation Balance of Farmland Policy

The rapid growth of China’s economy since the reform in 1978 should be largely attributed to urbanization. Nonetheless, in terms of farmland productivity, urbanization may lead to perverse incentives and thus threaten food security. On the one hand, the requisition−compensation balance of farmland (RCBF) policy could reduce farmland productivity because of a “superior occupation and inferior compensation”; on the other hand, urbanization promotes the transfer of the younger labor force and thus reduces the productivity of the agricultural labor force. To investigate the undesirable effects, based on some stylized facts, this study selects 29,415 county-level samples in a Chinese county from 2000−2014 to construct an empirical model. With a new stochastic frontier analysis method that eliminates the classical econometric issues of endogeneity and heterogeneity, the empirical results show that there is a U-shaped relationship between the farmland use efficiency (productivity) and urbanization rate, indicating that only when the urbanization rate is relatively low would urbanization decrease the farmland use efficiency; in contrast, when the urbanization rate is relatively high, technical progress would obviously be accompanied by urbanization, and thus, the undesirable effects are fully offset. Furthermore, the U-shaped relationship is robust after considering the endogeneity of the urbanization rate and total-factor farmland use efficiency. With these findings, recommendations to implement sustainable management and conservation policies regarding farmland resources are made

Living up to the hype of hyperspectral aquatic remote sensing: science, resources and outlook

Intensifying pressure on global aquatic resources and services due to population growth and climate change is inspiring new surveying technologies to provide science-based information in support of management and policy strategies. One area of rapid development is hyperspectral remote sensing: imaging across the full spectrum of visible and infrared light. Hyperspectral imagery contains more environmentally meaningful information than panchromatic or multispectral imagery and is poised to provide new applications relevant to society, including assessments of aquatic biodiversity, habitats, water quality, and natural and anthropogenic hazards. To aid in these advances, we provide resources relevant to hyperspectral remote sensing in terms of providing the latest reviews, databases, and software available for practitioners in the field. We highlight recent advances in sensor design, modes of deployment, and image analysis techniques that are becoming more widely available to environmental researchers and resource managers alike. Systems recently deployed on space- and airborne platforms are presented, as well as future missions and advances in unoccupied aerial systems (UAS) and autonomous in-water survey methods. These systems will greatly enhance the ability to collect interdisciplinary observations on-demand and in previously inaccessible environments. Looking forward, advances in sensor miniaturization are discussed alongside the incorporation of citizen science, moving toward open and FAIR (findable, accessible, interoperable, and reusable) data. Advances in machine learning and cloud computing allow for exploitation of the full electromagnetic spectrum, and better bridging across the larger scientific community that also includes biogeochemical modelers and climate scientists. These advances will place sophisticated remote sensing capabilities into the hands of individual users and provide on-demand imagery tailored to research and management requirements, as well as provide critical input to marine and climate forecasting systems. The next decade of hyperspectral aquatic remote sensing is on the cusp of revolutionizing the way we assess and monitor aquatic environments and detect changes relevant to global communities

Assesing the spatial transferability of Random Forest models trained on the basis of the WSF3D dataset for cities

The main objective of this research is to evaluate if the Random Forest models trained on the basis of spatial metrics derived solely from the WSF3D dataset can be transferred from one city to another. Though with the increased availability of remotely sensed data, new machine learning techniques are constantly emerging for land use mapping, the challenges of collecting validation data and spatial transferability are yet to be addressed. The WSF3D dataset and the technique of "Dissimilarity Index" are used to address these challenges. The main factors allowing model transferability; the association between prediction accuracies and transferability of cities; and the morphological similarities existing between transferable cities are analysed. The "Area of Applicability" is identified, to make assessments for successfully transferring a model to areas where validation data is not available

Fine Land-Cover Mapping in China Using Landsat Datacube and an Operational SPECLib-Based Approach

Fine resolution land cover information is a vital foundation of Earth science. In this paper, a novel SPECLib-based operational method is presented for the classification of multi-temporal Landsat imagery using reflectance spectra from the spatial-temporal spectral library (SPECLib) for 30 m land-cover mapping for the whole of China. Firstly, using the European Space Agency (ESA) Climate Change Initiative Global Land Cover (CCI_LC) product and the MODIS Version 6 Nadir bidirectional reflectance distribution function adjusted reflectance (NBAR) product (MCD43A4), a global SPECLib with a spatial resolution of 158.85 km (equivalent to 1.43° at the equator) and a temporal resolution of eight days was developed in the sinusoidal projection. Then, the Landsat datacube covering the whole of China was developed using all available observations of Landsat OLI imagery in 2015. Thirdly, the multi-temporal random forest method based on SPECLib was presented to produce an annual land-cover map with 22 land-cover types using the Landsat datacube. Finally, the annual China land-cover map was validated by two different validation systems using approximately 11,000 interpretation points. The mapping results achieved the overall accuracy of 71.3% and 80.7% and the kappa coefficient of 0.664 and 0.757 for the level-2 validation system (19 land-cover types) and the level-1 validation system (nine land-cover types), respectively. Therefore, the case study in China indicates that the proposed SPECLib method is an operational and accurate method for regional/global fine land-cover mapping at a spatial resolution of 30 m