Exploiting satellite SAR for archaeological prospection and heritage site protection

Optical and Synthetic Aperture Radar (SAR) remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications, yet further advances are viable through the exploitation of novel sensor data and imaging modes, big data and high-performance computing, advanced and automated analysis methods. This paper showcases the main research avenues in this field, with a focus on archaeological prospection and heritage site protection. Six demonstration use-cases with a wealth of heritage asset types (e.g. excavated and still buried archaeological features, standing monuments, natural reserves, burial mounds, paleo-channels) and respective scientific research objectives are presented: the Ostia-Portus area and the wider Province of Rome (Italy), the city of Wuhan and the Jiuzhaigou National Park (China), and the Siberian “Valley of the Kings” (Russia). Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite (e.g. Copernicus Sentinels and ESA Third Party Missions) and aerial (e.g. Unmanned Aerial Vehicles, UAV) platforms, as well as field-based evidence and ground truth, auxiliary topographic data, Digital Elevation Models (DEM), and monitoring data from geodetic campaigns and networks. The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes, identify threats to cultural heritage assets due to ground instability and urban development in large metropolises, and monitor post-disaster impacts in natural reserves

Informing action for United Nations SDG target 8.7 and interdependent SDGs: Examining modern slavery from space

This article provides an example of the ways in which remote sensing, Earth observation, and machine learning can be deployed to provide the most up to date quantitative portrait of the South Asian ‘Brick Belt’, with a view to understanding the extent of the prevalence of modern slavery and exploitative labour. This analysis represents the first of its kind in estimating the spatiotemporal patterns in the Bull’s Trench Kilns across the Brick Belt, as well as its connections with various UN Sustainable Development Goals (SDGs). With a principal focus on Sustainable Development Goal Target 8.7 regarding the effective measures to end modern slavery by 2030, the article provides additional evidence on the intersections that exist between SDG 8.7 and those relating to urbanisation (SDG 11, 12), environmental degradation and pollution (SDG 3, 14, 15), and climate change (SDG 13). Our findings are then used to make a series of pragmatic suggestions for mitigating the most extreme SDG risks associated with brick production in ways that can improve human lives and human freedom

Investigating the relationship between land use/land cover change and land surface temperature using Google Earth Engine; case study: Melbourne, Australia

The rapid alteration to land cover, combined with climate change, results in the variation of the land surface temperature (LST). This LST variation is mainly affected by the spatiotemporal changes of land cover classes, their geospatial characteristics, and spectral indices. Melbourne has been the subject of previous studies of land cover change but often over short time periods without considering the trade-offs between land use/land cover (LULC) and mean daytimes summer season LST over a more extended period. To fill this gap, this research aims to investigate the role of LULC change on mean annual daytime LST in the hot summers of 2001 and 2018 in Melbourne. To achieve the study’s aim, LULC and LST maps were generated based on the cost-effective cloud-based geospatial analysis platform Google Earth Engine (GEE). Furthermore, the geospatial and geo-statistical relationship between LULC, LST, and spectral indices of LULC, including the Normalised Difference Built-up Index (NDBI) and the Normalised Difference Vegetation Index (NDVI), were identified. The findings showed that the mean daytime LST increased by 5.1 °C from 2001 to 2018. The minimum and maximum LST values were recorded for the vegetation and the built-up area classes for 2001 and 2018. Additionally, the mean daytime LST for vegetation and the built-up area classes increased by 5.5 °C and 5.9 °C from 2001 to 2018, respectively. Furthermore, both elevation and NDVI were revealed as the most influencing factors in the LULC classification process. Considering the R2 values between LULC and LST and their NDVI values in 2018, grass (0.48), forest (0.27), and shrubs (0.21) had the highest values. In addition, urban areas (0.64), bare land (0.62), and cropland (0.61) LULC types showed the highest R2 values between LST regarding their NDBI values. This study highlights why urban planners and policymakers must understand the impacts of LULC change on LST. Appropriate policy measures can be proposed based on the findings to control Melbourne’s future development

Unmixing-based Spatiotemporal Image Fusion Based on the Self-trained Random Forest Regression and Residual Compensation

Spatiotemporal satellite image fusion (STIF) has been widely applied in land surface monitoring to generate high spatial and high temporal reflectance images from satellite sensors. This paper proposed a new unmixing-based spatiotemporal fusion method that is composed of a self-trained random forest machine learning regression (R), low resolution (LR) endmember estimation (E), high resolution (HR) surface reflectance image reconstruction (R), and residual compensation (C), that is, RERC. RERC uses a self-trained random forest to train and predict the relationship between spectra and the corresponding class fractions. This process is flexible without any ancillary training dataset, and does not possess the limitations of linear spectral unmixing, which requires the number of endmembers to be no more than the number of spectral bands. The running time of the random forest regression is about ~1% of the running time of the linear mixture model. In addition, RERC adopts a spectral reflectance residual compensation approach to refine the fused image to make full use of the information from the LR image. RERC was assessed in the fusion of a prediction time MODIS with a Landsat image using two benchmark datasets, and was assessed in fusing images with different numbers of spectral bands by fusing a known time Landsat image (seven bands used) with a known time very-high-resolution PlanetScope image (four spectral bands). RERC was assessed in the fusion of MODIS-Landsat imagery in large areas at the national scale for the Republic of Ireland and France. The code is available at https://www.researchgate.net/proiile/Xiao_Li52

Improved detection of abrupt change in vegetation reveals dominant fractional woody cover decline in Eastern Africa

While cropland expansion and demand for woodfuel exert increasing pressure on woody vegetation in East Africa, climate change is inducing woody cover gain. It is however unclear if these contrasting patterns have led to net fractional woody cover loss or gain. Here we used non-parametric fractional woody cover (WC) predictions and breakpoint detection algorithms driven by satellite observations (Landsat and MODIS) and airborne laser scanning to unveil the net fractional WC change during 2001-2019 over Ethiopia and Kenya. Our results show that total WC loss was 4-times higher than total gain, leading to net loss. The contribution of abrupt WC loss (59%) was higher than gradual losses (41%). We estimated an annual WC loss rate of up to 5% locally, with cropland expansion contributing to 57% of the total loss in the region. Major hotspots of WC loss and degradation corridors were identified inside as well as surrounding protected areas, in agricultural lands located close to agropastoral and pastoral livelihood zones, and near highly populated areas. As the dominant vegetation type in the region, Acacia-Commiphora bushlands and thickets ecosystem was the most threatened, accounting 69% of the total WC loss, followed by montane forest (12%). Although highly outweighed by loss, relatively more gain was observed in woody savanna than in other ecosystems. These results reveal the marked impact of human activities on woody vegetation and highlight the importance of protecting endangered ecosystems from increased human activities for mitigating impacts on climate and supporting sustainable ecosystem service provision in East Africa.Peer reviewe

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

ISLAND: Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator

Cloud occlusion is a common problem in the field of remote sensing, particularly for thermal infrared imaging. Remote sensing thermal instruments onboard operational satellites are supposed to enable frequent and high-resolution observations over land; unfortunately, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from Earth's surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable thermal images for downstream applications, making it impractical to perform intricate time-series analysis of land surface temperature (LST). In this paper, we introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator. Our approach uses thermal infrared images from Landsat 8 (at 30 m resolution with 16-day revisit cycles) and the NLCD land cover dataset. Inspired by Tobler's first law of Geography, ISLAND predicts occluded brightness temperature and LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and surface land cover conditions, and with a high spatio-temporal resolution. We provide a public dataset of 20 U.S. cities with pre-computed ISLAND thermal infrared and LST outputs. Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.Comment: 22 pages, 9 figure

Land Use and Land Cover Mapping in a Changing World

It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classification systems