Geology topics

Eric L. Bullock

Publications and source records attributed to Eric L. Bullock.

3 recordsLinked to original sources

USGS2022

A comprehensive assessment of mangrove species and carbon stock on Pohnpei, Micronesia

Mangrove forests are the most important ecosystems on Pohnpei Island, Federated States of Micronesia, as the island communities of the central Pacific rely on the forests for many essential services including protection from sea-level rise that is occurring at a greater pace than the global average. As part of a multi-component assessment to evaluate vulnerabilities of mangrove forests on Pohnpei, mangrove forests were mapped at two points in time: 1983 and 2018. In 2018, the island had 6,426 ha of mangrove forest. Change analysis indicated a slight (0.76%) increase of mangrove area between 1983 and 2018, contrasting with global mangrove area declines. Forest structure and aboveground carbon (AGC) stocks were inventoried using a systematic sampling of field survey plots and extrapolated to the island using k-nearest neighbor and random forest species models. A gridded or wall to wall approach is suggested when possible for defining carbon stocks of a large area due to high variability seen in our data. The k-nearest neighbor model performed better than random forest models to map species dominance in these forests. Mean AGC was 167 ± 11 MgC ha -1 , which is greater than the global average of mangroves (115 ± 7 MgC ha -1 ) but within their global range (37–255 MgC ha -1 ) Kauffman et al. (2020). In 2018, Pohnpei mangroves contained over 1.07 million MgC in AGC pools. By assigning the mean AGC stock per species per area to the map, carbon stock distributions were visualized spatially, allowing future conservation efforts to be directed to carbon dense stands.

Pohnpei Island↗

USGS2022

Medium spatial resolution mapping of global land cover and land cover change across multiple decades from Landsat

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

Frontiers in Remote Sensing↗

USGS2021

Carbon stock losses and recovery observed for a mangrove ecosystem following a major hurricane in Southwest Florida

Studies integrating mangrove in-situ observations and remote sensing analysis for specific sites often lack precise estimates of carbon stocks over time frames that include disturbance events. This study quantifies change in mangrove area from 1985 to 2018 with Landsat time series analysis, estimates above and belowground stored carbon using field data, and evaluates aboveground carbon stock changes after the 2004 Category 4, Hurricane Charley, in J.N. “Ding” Darling National Wildlife Refuge. Two allometric equation methods yielding similar results were used to estimate aboveground carbon content in three mangrove species found in the refuge. Aboveground carbon contained 67 (SE = 2) MgC ha −1 with a total refuge estimate of 74,504 MgC in 2018. Sediment contained 259 (SE = 28) MgC ha −1 for a total of 288,008 MgC in the refuge. The initial reduction in mangrove area caused by Hurricane Charley was between 0.6% and 5.3%, equivalent to between 427 MgC and 3,599 MgC under three different scenarios of carbon loss. As a result of the hurricane, approximately 61 ha of mangroves were disturbed, of which 24 ha had recovered by 2018, with 37 ha (~3% of the pre-hurricane mangrove area) still not recovered 14 years after the event. The 37 ha of mangroves that have not recovered are located in a tidally restricted area of the refuge. A longer recovery time in this area will likely result in a greater loss of carbon storage than in the rest of the refuge.

Florida↗