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Coastal mangrove forests provide numerous ecosystem services, which can be disrupted by natural disturbances, mainly hurricanes. Canopy height (CH) is a key parameter for estimating carbon storage. Airborne Light Detection and Ranging (LiDAR) is widely viewed as the most accurate method for estimating CH but data are often limited in spatial coverage and are not readily available for rapid impact assessment after hurricane events. Hence, we evaluated the use of systematically acquired space-based Synthetic Aperture Radar (SAR) and optical observations with airborne LiDAR to predict CH across expansive mangrove areas in South Florida that were severely impacted by Category 3 Hurricane Irma in 2017. We used pre- and post-Irma LiDAR-derived canopy height models (CHMs) to train Random Forest regression models that used features of Sentinel-1 SAR time series, Landsat-8 optical, and classified mangrove maps. We evaluated (1) spatial transfer learning to predict regional CH for both time periods and (2) temporal transfer learning coupled with species-specific error correction models to predict post-Irma CH using models trained by pre-Irma data. Model performance of SAR and optical data differed with time period and across height classes. For spatial transfer, SAR data models achieved higher accuracy than optical models for post-Irma, while the opposite was the case for the pre-Irma period. For temporal transfer, SAR models were more accurate for tall trees (>10 m) but optical models were more accurate for short trees. By fusing data of both sensors, spatial and temporal transfer learning achieved the root mean square errors (RMSEs) of 1.9 m and 1.7 m, respectively, for absolute CH. Predicted CH losses were comparable with LiDAR-derived reference values across height and species classes. Spatial and temporal transfer learning techniques applied to readily available spaceborne satellite data can enable conservation managers to assess the impacts of disturbances on regional coastal ecosystems efficiently and within a practical timeframe after a disturbance event. 

Extreme rainfall, induced by severe weather events, such as hurricanes, impacts wetlands because rapid water-depth increases can lead to flora and fauna mortality. This study developed an innovative algorithm to detect significant water-depth increases (SWDI, defined as water-depth increases above a threshold) in wetlands, using Sentinel-1 SAR backscatter. We used Hurricane Irma as an example that made landfall in the south Florida Everglades wetlands in September 2017 and produced tremendous rainfall. The algorithm detects SWDI for during- and post-event SAR acquisition dates, using pre-event water-depth as a baseline. The algorithm calculates Normalized Difference Backscatter Index (NDBI), using pre-, during-, and post-event backscatter, at a 20-m SAR resolution, as an indicator of the likelihood of SWDI, and detects SWDI using all NDBI values in a 400-m resolution pixel. The algorithm successfully detected large SWDI areas for the during-event date and progressive expansion of non-SWDI areas (water-depth differences less than the threshold) for five post-event dates in the following two months. The algorithm achieved good performance in both ‘herbaceous dominant’ and ‘trees embedded within herbaceous matrix’ land covers, with an overall accuracy of 81%. This study provides a solution for accurate mapping of SWDI and can be used in global wetlands, vulnerable to extreme rainfall. 

The thermal stability and decomposition pathway of formamidinium iodide (FAI, HC(NH 2 ) 2 I) in contact with NiO and TiO 2 are investigated by combined experimental studies and density functional theory (DFT) calculations. Based on the decomposition temperature, we find that the stability decreases as FAI ∼ FAI + TiO 2 > FAI + NiO. Moreover, FAPbI 3 in contact with NiO and TiO 2 shows similar thermal stability behaviour to FAI. The bulk decomposition of FAI occurs via the formation of sym -triazine, and can also produce HCN, and NH 4 I at ∼280 °C, which further decomposes to NH 3 and HI above 300 °C. When FAI comes into contact with NiO, the interfacial reaction triggers decomposition at a much lower temperature (∼200 °C), resulting in the formation of NiI 2 as the solid product while releasing NH 3 and H 2 O into the gas phase; sym -triazine and HCN are observed near the FAI bulk decomposition temperature. In contrast, when FAI comes into contact with TiO 2 , the decomposition temperature is similar to bulk FAI; however, HCN is released at a lower temperature (∼260 °C) compared to sym -triazine. The difference in the degradation behavior of FAI with NiO and TiO 2 is elucidated using DFT calculations. Our results show that the interfacial reaction between the organic component of perovskite material and NiO occurs similarly for MA and FA, which thereby can induce device instability.