Southern California Disasters II

The USDA Forest Service (USFS) has multiple programs in place which primarily utilize Landsat imagery to produce burn severity indices for aiding wildfire damage assessment and mitigation. These indices provide widely-used wildfire damage assessment tools to decision makers. When the Hyperspectral Infrared Imager (HyspIRI) is launched in 2022, the sensor's hyperspectral resolution will support new methods for assessing natural disaster impacts on ecosystems, including wildfire damage to forests. This project used simulated HyspIRI data to study three southern California fires: Aspen, French, and King. Burn severity indices were calculated from the data and the results were quantitatively compared to the comparable USFS products currently in use. The final results from this project illustrate how HyspIRI data may be used in the future to enhance assessment of fire-damaged areas and provide additional monitoring tools for decision support to the USFS and other land management agencies

Groundwater discharge to the western Antarctic coastal ocean

Submarine groundwater discharge (SGD) measurements have been limited along the Antarctic coast, although groundwater discharge is becoming recognized as an important process in the Antarctic. Quantifying this meltwater path-way is important for hydrologic budgets, ice mass balances and solute delivery to the coastal ocean. Here, we estimate the combined discharge of subglacial and submarine groundwater to the Antarctic coastal ocean. SGD, including subglacial and submarine groundwater, is quantified along the WAP at the Marr Glacier terminus using the activities of naturally occurring radium isotopes (223Ra, 224Ra). Estimated SGD fluxes from a 224Ra mass balance ranged from (0.41 ± 0.14)×104 and (8.2 ± 2.3)×104m3 d−1. Using a salinity mass balance, we estimate SGD contributes up to 32% of the total freshwater to the coastal environment near Palmer Station. This study suggests that a large portion of the melting glacier may be infiltrating into the bedrock and being discharged to coastal waters along the WAP. Meltwater infiltrating as groundwater at glacier termini is an import-ant solute delivery mechanism to the nearshore environment that can influence biological productivity. More importantly, quantifying this meltwater pathway may be worthy of attention when predicting future impacts of climate change on retreat of tidewater glaciers

Toward a satellite-based system of sugarcane yield estimation and forecasting in smallholder farming conditions : a case study on Reunion Island

Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1) an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2) the Kumar-Monteith efficiency model, and (3) a forced-coupling method with a sugarcane crop model (MOSICAS) and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t.ha(-1)). Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency

Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation

The objective of this study was to assess the efficiency of the assimilation of the fraction of intercepted photosynthetically active radiation (fIPAR) data derived from Satellite Pour l'Observation de la Terre SPOT images into the MOSICAS sugarcane crop growth model for estimating the yield at field scale on Reunion Island. Over 3 years, time series of SPOT satellite imagery were used to estimate the daily evolution of NDVI for 60 plots located on two climatically contrasted farms. Ground measurements of the fIPAR were performed on 5 reference fields and used to calibrate a relationship with the corresponding NDVI values. Forced and not forced simulations were run and compared with respect to their ability to predict the final observed yield. Forcing MOSICAS with fIPAR values derived from SPOT images improved the accuracy of the model for the yield estimation (RMSE = 12.2 against 14.8 t ha(-1)) closer to the 1:1 line. However, underestimations of the yield by the forced model suggest that some of the model parameters were not optimal. The maximal radiation use efficiency parameter (RUEm) was optimised for each field, and an analysis of variance showed the significant effect of the ratoon number of the field, of its cultivar and of the farm where it is planted. Accordingly, the RUEm was recalibrated for each cultivar for the number of ratoons and farms. New RUEm values ranged from 3.09 to 3.77 gMJ(-1), and new computations were run using the optimised values of RUEm The results indicate that recalibrating the maximal radiation use efficiency according to the number of ratoons improved the yield estimation accuracy by as much as 10.5 t ha-1 RMSE. This study highlights the potential of time series of satellite images to enhance the estimation of the yield by a forced ecophysiological model and to obtain better knowledge about the ecophysiological processes that are involved in crop dynamics with the recalibration method

Capture, modeling, and recognition of expert technical gestures in wheel-throwing art of pottery

This research has been conducted in the context of the ArtiMuse project that aims at the modeling and renewal of rare gestural knowledge and skills involved in the traditional craftsmanship and more precisely in the art of wheel-throwing pottery. These knowledge and skills constitute intangible cultural heritage and refer to the fruit of diverse expertise founded and propagated over the centuries thanks to the ingeniousness of the gesture and the creativity of the human spirit. Nowadays, this expertise is very often threatened with disappearance because of the difficulty to resist globalization and the fact that most of those "expertise holders" are not easily accessible due to geographical or other constraints. In this article, a methodological framework for capturing and modeling gestural knowledge and skills in wheel-throwing pottery is proposed. It is based on capturing gestures using wireless inertial sensors and statistical modeling. In particular, we used a system that allows for online alignment of gestures using a modified Hidden Markov Model. This methodology is implemented into a human-computer interface, which permits both the modeling and recognition of expert technical gestures. This system could be used to assist in the learning of these gestures by giving continuous feedback in real time by measuring the difference between expert and learner gestures. The system has been tested and evaluated on different potters with rare expertise, which is strongly related to their local identity. © 2014 ACM