Change detection using multi-scale convolutional feature maps of bi-temporal satellite high-resolution images

ABSTRACTChange detection in high-resolution satellite images is essential to understanding the land surface (e.g. agriculture and urban change) or maritime surface (e.g. oil spilling). Many deep-learning-based change detection methods have been proposed to enhance the performance of the classical techniques. However, the massive amount of satellite images and missing ground-truth images are still challenging concerns. In this paper, we propose a supervised deep network for change detection in bi-temporal remote sensing images. We feed multi-level features from convolutional networks of two images (feature-extraction) into one architecture (feature-difference) to have better shape and texture properties using a dual attention module We also utilize a multi-scale dice coefficient error function to decrease overlapping between changed and background pixel. The network is applied to public datasets (ACD, SYSU-CD and OSCD). We compare the proposed architecture with various attention modules and loss functions to verfiy the performance of the proposed method. We also compare the proposed method with the stateof-the-art methods in terms of three metrics: precision, recall and F1-score. The experimental outcomes confirm that the proposed method has good performance compared to benchmark methods

River basin salinization as a form of aridity

Soil-salinization affects, to a different extent, more than one-third of terrestrial river basins (estimate based on the Food and Agriculture Organization Harmonized World Soil Database, 2012). Among these, many are endorheic and ephemeral systems already encompassing different degrees of aridity, land degradation, and vulnerability to climate change. The primary effect of salinization is to limit plant water uptake and evapotranspiration, thereby reducing available soil moisture and impairing soil fertility. In this, salinization resembles aridity and—similarly to aridity—may impose significant controls on hydrological partitioning and the strength of land–vegetation–atmosphere interactions at the catchment scale. However, the long-term impacts of salinization on the terrestrial water balance are still largely unquantified. Here, we introduce a modified Budyko’s framework explicitly accounting for catchment-scale salinization and species-specific plant salt tolerance. The proposed framework is used to interpret the water-budget data of 237 Australian catchments—29% of which are already severely salt-affected—from the Australian Water Availability Project (AWAP). Our results provide theoretical and experimental evidence that salinization does influence the hydrological partitioning of salt-affected watersheds, imposing significant constraints on water availability and enhancing aridity. The same approach can be applied to estimate salinization level and vegetation salt tolerance at the basin scale, which would be difficult to assess through classical observational techniques. We also demonstrate that plant salt tolerance has a preeminent role in regulating the feedback of vegetation on the soil water budget of salt-affected basins

The Generalized Additive Model for the Assessment of the Direct, Diffuse, and Global Solar Irradiances Using SEVIRI Images, With Application to the UAE

Generalized additive models (GAMs) can model the nonlinear relationship between a response variable and a set of explanatory variables through smooth functions. GAM is used to assess the direct, diffuse, and global solar components in the United Arab Emirates (UAE), a country which has a large potential for solar energy production. Six thermal channels of the spinning enhanced visible and infrared imager (SEVIRI) instrument onboard Meteosat second generation (MSG) are used as explanatory variables along with the solar zenith angle, solar time, day number, and eccentricity correction. The proposed model is fitted using reference data from three ground measurement stations for the full year of 2010 and tested on two other stations for the full year of 2009. The performance of the GAM model is compared to the performance of the ensemble of artificial neural networks (ANNs) approach. Results indicate that GAM leads to improved estimates for the testing sample when compared to the bagging ensemble. GAM has the advantage over ANN-based models that we can explicitly define the relationships between the response variable and each explanatory variable through smooth functions. Attempts are made to provide physical explanations of the relations between irradiance variables and explanatory variables. Models in which the observations are separated as cloud-free and cloudy and treated separately are evaluated along with the combined dataset. Results indicate that no improvement is obtained compared to a single model fitted with all observations. The performance of the GAM is also compared to the McClear model, a physical-based model providing estimates of irradiance in clear sky conditions

Regional frequency analysis at ungauged sites using a two-stage resampling generalized ensemble framework.

Regional frequency analysis (RFA) deals with the estimation of hydrological characteristics at sites where little or no data is available. Recently, machine learning applications to RFA have received considerable attention in terms of their flexibility in modeling as well as superior generalization ability compared to conventional approaches. The proper application of machine learning techniques, however, requires good understanding of the available information about system's dynamics, i.e. system variables. This paper presents two contributions to the literature. First, novel ensemble architecture, using a unique two-stage resampling approach, is proposed. The objective of the proposed ensemble model is to promote diversity within the individual learners and reduce over fitting. Second, the application of the proposed model is demonstrated in RFA case study to obtain improved regional flood quantile estimates at ungauged sites. A jackknife validation procedure is used for the evaluation of the model's performance. The method is applied to data from the province of Quebec, Canada. The model showed similar performance and generalization compared to major ensemble models in the literature, which were investigated in previous studies using the same data set. The proposed model confirmed the diversity requirement in ensemble modeling and, in the same time, validated the proposed model adherence to ensemble learning theory

Inferring Species Diversity and Variability over Climatic Gradient with Spectral Diversity Metrics

Filling in the void between forest ecology and remote sensing through monitoring biodiversity variables is of great interest. In this study, we utilized imaging spectroscopy data from the ISRO–NASA Airborne Visible InfraRed Imaging Spectrometer—Next Generation (AVIRIS-NG) India campaign to investigate how the measurements of biodiversity attributes of forests over wide areas can be augmented by synchronous field- and spectral-metrics. Three sites, Shoolpaneshwar Wildlife Sanctuary (SWS), Vansda National Park (VNP), and Mudumalai Tiger Reserve (MTR), spread over a climatic gradient (rainfall and temperature), were selected for this study. Abundant species maps of three sites were produced using a support vector machine (SVM) classifier with a 76–80% overall accuracy. These maps are a valuable input for forest resource management. Convex hull volume (CHV) is computed from the first three principal components of AVIRIS-NG spectra and used as a spectral diversity metric. It was observed that CHV increased with species numbers showing a positive correlation between species and spectral diversity. Additionally, it was observed that the abundant species show higher spectral diversity over species with lesser spread, provisionally revealing their functional diversity. This could be one of the many reasons for their expansive reach through adaptation to local conditions. Higher rainfall at MTR was shown to have a positive impact on species and spectral diversity as compared to SWS and VNP. Redundancy analysis explained 13–24% of the variance in abundant species distribution because of climatic gradient. Trends in spectral CHVs observed across the three sites of this study indicate that species assemblages may have strong local controls, and the patterns of co-occurrence are largely aligned along climatic gradient. Observed changes in species distribution and diversity metrics over climatic gradient can help in assessing these forests’ responses to the projected dynamics of rainfall and temperature in the future

Fundamentals and Applications of Chitosan

International audienceChitosan is a biopolymer obtained from chitin, one of the most abundant and renewable material on Earth. Chitin is a primary component of cell walls in fungi, the exoskeletons of arthropods, such as crustaceans, e.g. crabs, lobsters and shrimps, and insects, the radulae of molluscs, cephalopod beaks, and the scales of fish and lissamphibians. The discovery of chitin in 1811 is attributed to Henri Braconnot while the history of chitosan dates back to 1859 with the work of Charles Rouget. The name of chitosan was, however, introduced in 1894 by Felix Hoppe-Seyler. Because of its particular macromolecular structure, biocompatibility, biode-gradability and other intrinsic functional properties, chitosan has attracted major scientific and industrial interests from the late 1970s. Chitosan and its derivatives have practical applications in food industry, agriculture, pharmacy, medicine, cos-metology, textile and paper industries, and chemistry. In the last two decades, chito-san has also received much attention in numerous other fields such as dentistry, ophthalmology, biomedicine and bio-imaging, hygiene and personal care, veterinary medicine, packaging industry, agrochemistry, aquaculture, functional textiles and cosmetotextiles, catalysis, chromatography, beverage industry, photography, wastewater treatment and sludge dewatering, and biotechnology. Nutraceuticals and cosmeceuticals are actually growing markets, and therapeutic and biomedical products should be the next markets in the development of chitosan. Chitosan is also the N. Morin-Crini (*) · Laboratoire Chrono-environnement, UMR 6249, UFR Sciences et Techniques