In this paper, we developed an integrated methodology for assessing asset damage, production capacity loss, and inter-sector ripple loss using the depth-damage curve, Cobb-Douglas production function and Input-Output model. We applied this methodology to the detailed individual manufacturing firms in Shanghai under an extreme storm floods scenario to simulate the disaster impact propagation from local individual firms to the entire industrial system and comprehensively estimate the resulting economic losses and their spatial distribution. Our results show that given no floodwall protection, a 1000-year storm flood scenario would cause direct asset damage of US $21 billion to the Shanghai manufacturing industry, including fixed asset damage of US $12 billion and inventory damage of US $9 billion. Due to the shortage of input productive factors of asset and labor, it would further lead to production capacity loss of US $24 billion. In addition, affected manufacturing industry would indirectly result in ripple loss of US $60 billion among dependent sectors, which has a significant amplifier effect. Our results have important implications for reasonable cost-benefit analysis of structural flood control measures in coastal areas, as well as for manufacturing firm location planning and resilience strategy decision-making.

Chlorophyll-a (Chl-a) concentration is considered as a key indicator of the eutrophic status of inland water bodies. Various algorithms have been developed for estimating Chl-a in order to improve the accuracy of predictive models. The objective of this study is to assess the potential of hyperspectral multi-band indices to estimate the Chl-a concentration in Dianshan Lake, which is the largest lake in Shanghai, an international metropolis of China. Based on field spectral measurements and in-situ Chl-a concentration collected on 7–8 September 2010, hyperspectral multi-band indices were calibrated to estimate the Chl-a concentration with optimal wavelengths selected by model tuning. A three-band index accounts for 87.36% (R² = 0.8736) of the Chl-a variation. A four-band index, which adds a wavelength in the near infrared (NIR) region, results in a higher R² (0.8997) by removing the absorption and backscattering effects of suspended solids. To test the applicability of the proposed indices for routinely monitoring of Chl-a in inland lakes, simulated Hyperion and real HJ-1A satellite data were selected to estimate the Chl-a concentration. The results show that the explanatory powers of these satellite hyperspectral multi-band indices are relatively high with R² = 0.8559, 0.8945, 0.7969, and 0.8241 for simulated Hyperion and real HJ-1A satellite data, respectively. All of the results provide strong evidence that hyperspectral multi-band indices are promising and applicable to estimate Chl-a in eutrophic inland lakes.

The present study constructed a spatial‐temporal statistical model to identify the risk and protective factors for haemorrhagic disease (HD) in white‐tailed deer in the five states of Alabama, Georgia, South Carolina, North Carolina and Tennessee. The response variable was binary, indicating the presence or absence of HD in an individual county, measured annually from 1983 to 2000. Predictor variables included climatic factors of temperature, rainfall, wind speed and dew point, remotely sensed data of normalised difference vegetation index (NDVI) and land surface temperature derived from archived remotely sensed advanced very‐high‐resolution radiometer (AVHRR) satellite data, elevation, a spatial autocorrelation (SA) term and a temporal autocorrelation term. This study first applied principal component factor analysis to reduce the volume of climatic data and remotely sensed data. Then, a generalised linear mixed model framework (GLMM) was used to develop a spatial‐temporal statistical model. The results showed that the area under receiver operating characteristic curve (ROC) was 0.728, indicating a good overall fit of the model. The total prediction accuracy over the 18 year period with optimal cut‐off probability was 67 per cent. The prediction accuracy for individual years ranged from 48 to 75 per cent.

Time series remote sensing images can be used to monitor the dynamic changes of forest lands. Due to consistent cloud cover and fog, a single sensor typically provides limited data for dynamic monitoring. This problem is solved by combining observations from multiple sensors to form a time series (a satellite image time series). In this paper, the pixel-based multi-source remote sensing image fusion (MulTiFuse) method is applied to combine the Landsat time series and Huanjing-1 A/B (HJ-1 A/B) data in the Fuling district of Chongqing, China. The fusion results are further corrected and improved with spatial features. Dynamic monitoring and analysis of the study area are subsequently performed on the improved time series data using the combination of Mann-Kendall trend detection method and Theil Sen Slope analysis. The monitoring results show that a majority of the forest land (60.08%) has experienced strong growth during the 1999–2013 period. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracies.