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Shi Qiu

Publications and source records attributed to Shi Qiu.

3 recordsLinked to original sources

The accelerating loss and shifting dynamics of US tidal wetlands

Tidal wetlands are critical ecosystems for coastal sustainability, yet despite growing regulatory protection, they continue to decline globally. Their long-term resilience to interacting chronic stressors and extreme events remains uncertain, in part because comprehensive, high-frequency monitoring has been lacking. While direct land-use conversion has been substantially restricted in the United States, the true trajectory of these protected habitats has remained unclear. Here, we use four decades of high-resolution satellite records to analyze the shifting dynamics of US tidal wetlands. We reveal a widespread and previously unquantified acceleration in the rate of tidal wetland loss, amounting to a net loss of −1640 km 2 at the rate of −40.53 km 2 year −1 , accelerating by −0.73 km 2 year −2 , of which tidal marsh contributed the majority of this loss with a cumulative decline of 1567 km 2 . Furthermore, we show that the drivers of this decline are shifting: while chronic stressors like relative sea level rise have caused the largest cumulative loss (~60% of the total area loss), acute shocks from extreme weather now dominate (1.4 times that of the chronic stressors) the acceleration of that loss. By contrast, direct human activities were a minor driver, accounting for only 4% of total observed losses. These findings indicate that the resilience of these protected ecosystems is declining. It provides an urgent warning that existing conservation strategies, initially concerned with direct human impacts and increasingly focused on relative sea level rise as a slow-moving pressure, are ill-equipped for a future of increasing extreme weather events and highlights the need to redesign adaptation policies.

conterminous United States↗

Tracking mangrove condition changes using dense Landsat time series

Mangroves in tropical and subtropical coasts are subject to episodic disturbances, notably from severe storms, leading to potential widespread vegetation mortality. The ability of vegetation to recover varies, and with disturbances becoming more frequent and severe, it is vital to track and project vegetation responses to support management and policy decisions. Prior studies have largely focused on binary mangrove mapping (i.e., presence or absence), while tracking conditions and condition change have not received sufficient attention. In this paper, we demonstrate a method based on dense time series Landsat images for continuous monitoring of mangrove conditions, where we track three kinds of post-disturbance mangrove conditions, including disturbed (disturbed, with rebound to the previous state within one growing season), recovering (undergoing natural recovery in longer than one growing season), and declining (showing long-term decline after disturbance). The method starts with disturbance detection using the DEtection and Characterization Of the tiDal wEtland change (DECODE) algorithm, an existing dense time series model designed to detect disturbances in tidal wetlands with adaptation to tidal fluctuations. This algorithm is well suited for the detection of tidal wetland disturbances but does not provide satisfactory post-disturbance monitoring results, due to the substantial variability in post-disturbance Landsat observations. To better monitor post-disturbance conditions, a new time series fitting approach, DECODER (DECODE and Recovery), is proposed for the recovery stage. Additionally, for temporal segments divided by disturbance events, we built a random forest classifier with temporal-spectral variables derived from the time series model to characterize mangrove conditions. Employing this approach in Florida's mangroves, we generated condition maps, such as dieback and recovery, with an overall accuracy of approximately 97.96 ± 0.86- [95 % confidence intervals]. Comparing post-hurricane conditions in Florida revealed that the increased frequency and severity of disturbances are challenging mangrove resilience, potentially diminishing their ability to recover and sustain ecosystem functions.

Florida↗

Evaluation of Landsat image compositing algorithms

We proposed a new image compositing algorithm (MAX-RNB) based on the maximum ratio of Near Infrared (NIR) to Blue band (RNB), and evaluated it together with nine other compositing algorithms: MAX-NDVI (maximum Normalized Difference Vegetation Index), MED-NIR (median NIR band), WELD (conterminous United States Web-Enabled Landsat Data), BAP (Best Available Pixel), PAC (Phenology Adaptive Composite), WPS (Weighted Parametric Scoring), MEDOID (medoid measurement), COSSIM (cosine similarity), and NLCD (National Land Cover Database). Each algorithm was applied to time series of Landsat observations collected within two separate years at six locations around the world, to produce monthly (July 1 ± 15 days), seasonal (July 1 ± 45 days), and annual (July 1 ± 180 days) composite images free of cloud, cloud shadow, and snow/ice. By comparing the composite images to reference Landsat images acquired in the growing season (closest to July 1 within ±15 days) for each year, we evaluated the performance of the algorithms in preserving the spectral and spatial fidelity (hereafter referred to as spectral and spatial evaluation, respectively), as well as land cover classification and land change detection (hereafter referred to as application evaluation). The results demonstrated that no single algorithm outperformed all other algorithms in all the evaluations, but that performance depended on compositing intervals and cloud cover. For monthly composites, the MAX-RNB algorithm generally produced the best results in the spectral and application evaluations. For seasonal composites, the NLCD algorithm produced the best results in the spectral and application evaluations. For annual composites, the PAC algorithm produced the best results in the spectral evaluation and change detection, whereas BAP produced the best results in land cover classification. The BAP algorithm also produced the best results in the spatial evaluation for all the compositing periods. This study provides a comprehensive guidance for selecting the most appropriate image compositing algorithm for different Landsat-based applications.

Remote Sensing of Environment↗