This paper deals with the study of Holocene geomorphic evolution of rivers around Songshan Mountain in relation to human frequentation in Prehistoric periods. The investigations were performed by means of an integration of GIS data processing; field surveys and particle size analysis. In 8000–3000 aBP; in the Songshan Mountain Region, large-scale river sedimentation occurred. This increased the elevation of river beds that were higher than today. After 3000 aBP; the upper reaches of the rivers experienced a down cut; while the lower reaches experienced continuing sedimentation. The data on the elevation of prehistoric settlements above the river levels were obtained from Digital Elevation Models (DEMs). These data were corrected according to the evolutionary features of fluvial landforms in order to obtain synchronous elevations above river levels of prehistoric settlements. The relationship between sediment distribution and the Holocene geomorphic evolution was investigated through the statistical analysis of the elevation above the river levels. Outputs from our analyses enabled us to differentiate three evolutionary stages. During the first one, related to Peiligang culture (9000–7500 aBP), populations mainly settled on both hilly relief and high plateaus depending on their agriculture production modes. During the second stage, from Yangshao (7500–5000 aBP) to the Longshan period (5000–4000 aBP), settlements were mainly distributed on mountainous areas and hilly lands to avoid flooding and to develop agriculture. Finally, during the Xiashang culture (4000–3000 aBP), a large number of settlements migrated to the plain area to facilitate trade of goods and cultural exchanges. Full article

With the development of Synthetic Aperture Radar (SAR) in terms of multi-band, multi-polarization and high-resolution data, space radar remote sensing for archaeology has become a potential field for research. Nevertheless, the archaeological detection capability of this technology has so far not been fully assessed. This paper is a pioneering effort to assess the potential of satellite SAR X-band data in the detection of archaeological marks. We focus on the results obtained from a collaborative contribution jointly carried out by archaeologists and remote sensing experts in order to test the use of COSMO-SkyMed data in different contexts and environmental conditions. The methodological approaches we adopted are based on two different feature-enhancement procedures: (i) multi-temporal analysis performed to reduce noise and highlight archaeological marks; (ii) single-date analysis to assess the ability of the single SAR scene to detect archaeological features like with optical remote sensing. Results from multi-temporal data analysis, conducted using 40 scenes from COSMO-SkyMed X-band Stripmap data (27 February to 17 October 2013), enable us to detect unknown archaeological crop, soil, and shadow marks representing Luoyang city, dating from the Eastern-Han to Northern-Wei Dynasties. Single-date analyses were conducted using COSMO-SkyMed Spotlight scenes acquired for Sabratha (Libya) and Metapontum (southern Italy). These case studies were selected because they are characterized by diverse superficial conditions (desert and Mediterranean area) and archaeological marks (crop, soil and shadow). The results we obtained for both of them show that even a single SAR X-band acquisition is a feasible and effective approach for archaeological prospection. Overall, the methodological approach adopted demonstrated that both multi-temporal and single-date analysis are suitable for the enhancement of archaeological and palaeoenvironmental features. Full article

Synthetic aperture radar (SAR) has been an unparalleled tool in cloudy and rainy regions as it allows observations throughout the year because of its all-weather, all-day operation capability. In this paper, the influence of Wenchuan Earthquake on the Sichuan Giant Panda habitats was evaluated for the first time using SAR interferometry and combining data from C-band Envisat ASAR and L-band ALOS PALSAR data. Coherence analysis based on the zero-point shifting indicated that the deforestation process was significant, particularly in habitats along the Min River approaching the epicenter after the natural disaster, and as interpreted by the vegetation deterioration from landslides, avalanches and debris flows. Experiments demonstrated that C-band Envisat ASAR data were sensitive to vegetation, resulting in an underestimation of deforestation; in contrast, L-band PALSAR data were capable of evaluating the deforestation process owing to a better penetration and the significant coherence gain on damaged forest areas. The percentage of damaged forest estimated by PALSAR decreased from 20.66% to 17.34% during 2009–2010, implying an approximate 3% recovery rate of forests in the earthquake impacted areas. This study proves that long-wavelength SAR interferometry is promising for rapid assessment of disaster-induced deforestation, particularly in regions where the optical acquisition is constrained. Full article

Expert knowledge is a combination of prior information and subjective opinions based on long-experience; as such it is often not sufficiently objective to produce convincing results in animal habitat suitability index mapping. In this study, an animal habitat assessment method based on a learning neural network is proposed to reduce the level of subjectivity in animal habitat assessments. Based on two hypotheses, this method substitutes habitat suitability index with apparent density and has advantages over conventional ones such as those based on analytical hierarchy process or multivariate regression approaches. Besides, this method is integrated with a learning neural network and is suitable for building non-linear transferring functions to fit complex relationships between multiple factors influencing habitat suitability. Once the neural network is properly trained, new earth observation data can be integrated for rapid habitat suitability monitoring which could save time and resources needed for traditional data collecting approaches through extensive field surveys. Giant panda (Ailuropoda melanoleuca) natural habitat in Ya’an prefecture and corresponding landsat images, DEM and ground observations are tested for validity of using the methodology reported. Results show that the method scores well in key efficiency and performance indicators and could be extended for habitat assessments, particularly of other large, rare and widely distributed animal species. Full article