Geology topics

Danika F. Wellington

Publications and source records attributed to Danika F. Wellington.

8 recordsLinked to original sources

USGS2026

Origins, evolutions, and future directions of Landsat science products for advancing global inland water and coastal ocean observations

In April 2020, the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center introduced a Level 2 provisional Aquatic Reflectance (AR) product for the Landsat 8 Operational Land Imager (OLI), marking the initial phase in developing a standardized global product for Landsat-derived surface water measurements. The goal of USGS EROS aquatic product research and development is to prepare for an operational processing architecture for Landsat Collection 3 in the late 2020s that will enable use of quality-controlled data for emerging Landsat aquatic science applications. To achieve this, we released a subset of the Landsat 8/9 provisional AR products (Crawford et al., 2025, https://doi.org/10.5066/P14MBBRM) and examined its general performance through the Science Algorithms to Operations (SATO) framework alongside quantitative assessment using community made inland water data records (GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments, GLORIA) and radiometric coastal validation platforms (NASA’s Ocean Color component of the Aerosol Robotic Network, AERONET-OC). Variability within the validation datasets indicate that the performance of the Landsat 8/9 provisional AR retrieval is highly context-dependent; errors are minimal in optically simple waters (e.g., clear to moderately turbid coastal waters) but increase considerably in optically complex waters where factors such as elevated levels of turbidity, chlorophyll (Chl a ) concentrations, or colored dissolved organic matter (CDOM) dominate the water column. Additionally, this paper examines key algorithmic considerations for atmospheric correction, highlighting factors that influence accuracy, scalability, and computational efficiency necessary for collection processing in the operational Landsat Product Generation System (LPGS). This paper is intended to communicate with aquatic scientists, satellite oceanographers, and the broader Earth observation community on the origins, requirements, challenges, successes, and future objectives for operationalizing global AR data products for Landsat satellite missions.

Earth System Science Data↗

USGS2023

Toward consistent change detection across irregular remote sensing time series observations

The use of remote sensing in time series analysis enables wall-to-wall monitoring of the land surface and is critical for assessing and understanding land cover and land use change and for understanding the Earth system as a whole. However, variability in remote sensing observation frequency through time and across space presents challenges for producing consistent change detection results throughout the available satellite record using approaches such as the Continuous Change Detection and Classification (CCDC) change detection methodology. Here we investigate new modifications to this methodology with the goal of improving accuracy and consistency in results and increasing flexibility for operational usage and future development. The modified method (Band-First Probability, or CCD-BFP) change detection procedure works by calculating a test for each band through time before summarizing between bands. We evaluate the CCD-BFP method compared to an existing implementation of CCDC using a variety of approaches, including a validation dataset of human-interpreted locations, comparison with data from fire events, use of simulated remote sensing data, and qualitative inspection of areas of interest. We find CCD-BFP improves consistency across time and space compared to the existing implementation of CCDC, with more similarity in rates of change across Landsat swath boundaries and before and after the launch of Landsat 7. Also, we find that CCD-BFP detects more of the change events in the validation dataset while reducing the overall number of change detections, indicating that it is able to more accurately capture the most notable land surface change events.

Remote Sensing of Environment↗

USGS2022

Tree regrowth duration map from LCMAP collection 1.0 land cover products in the conterminous United States, 1985–2017

Forest covers about one-third of the land area of the conterminous United States (CONUS) and plays an important role in offsetting carbon emissions and supporting local economies. Growing interest in forests as relatively cost-effective nature-based climate solutions, particularly restoration and reforestation activities, has increased the demand for information on forest regrowth and recovery following natural and anthropogenic disturbances (e.g., fire, harvest, or thinning). However, a wall-to-wall mapping of the CONUS tree regrowth duration at an annual time interval and 30-m resolution is still challenging. In this study, we utilized the annual land cover products to develop a dataset to quantify forest regrowth duration for CONUS over 1985–2017. The land cover data used to derive the tree regrowth duration map is from the primary land cover product in the U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) collection. The LCMAP product used all available Landsat images to detect disturbances over forest and classify Grass/Shrub to Tree Cover transitions on an annual basis. The average regrowth duration was then calculated for each pixel. The regrowth duration map was validated using human interpreted annual reference data that were collected independently. The validation results show one-year of underestimation and 6-year standard deviation of error between the reference data and regrowth duration map. In southeastern CONUS, where major tree regrowth activities have been observed, our map showed higher accuracy with less than one-year bias and 3.6 years standard deviation of error. Forest in the southeast took around 5 years to recover, which was faster than other regions of CONUS. Many pixels had multiple disturbances during the 33-year study period in the region. The spatial pattern of the tree regrowth indicated intense harvesting activities in this region. The Pacific Northwest coast region was the second main area of tree regrowth, but this region often took multiple decades to recover. Given increasing interest in forests as nature-based climate solutions, the tree regrowth duration map can be used to assess reforestation activities as well as forest recovery following natural disturbance and harvesting.

Conterminous United States↗

USGS2022

Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product

The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985-2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~ 25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5%. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future.

Earth System Science Data↗

USGS2022

Development of the LCMAP annual land cover product across Hawai'i

Following the completion of land cover and change (LCC) products for the conterminous United States (CONUS), the U.S. Geological Survey's (USGS’s) Land Change Monitoring, Assessment, and Projection initiative has broadened the capability of characterizing continuous historical land change across the full Landsat records for Hawaiʻi at 30-meter resolution. One of the challenges of implementing the LCMAP framework to process annual land cover maps in Hawaiʻi is to collect sufficient high-quality training data. Although multiple datasets depicting land cover information are available in Hawaiʻi, they covered limited time frames and were produced from various remote sensing sources with different, classification categories, spatial resolution, and mapping accuracies. No solo product is suitable to provide LCMAP training data labels on its own. In this paper, we focused on enhancing the LCMAP training datasets to generate land cover products from 2000 to 2019 in Hawaiʻi. A total of 200 independent reference data plots were generated and manually interpreted for validating the mapping results produced by the training datasets. The results revealed that using the appropriate filter of multiple products as training data pools improved the classification model performance. The effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types were summarized. The LCMAP land surface change products for Hawaiʻi are available at https://doi.org/10.5066/P91E8M23 .

Hawaii↗

USGS2021

Validation of the U.S. Geological Survey’s Land Change Monitoring, Assessment and Projection (LCMAP) collection 1.0 annual land cover products 1985–2017

The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) has released a suite of annual land cover and land cover change products for the conterminous United States (CONUS). The accuracy of these products was assessed using an independently collected land cover reference sample dataset produced by analysts interpreting Landsat data, high-resolution aerial photographs, and other ancillary data. The reference sample of nearly 25,000 pixels and the accompanying 33-year time series of annual land cover reference labels allowed for a comprehensive assessment of accuracy of the LCMAP land cover and land cover change products. Overall accuracy (± standard error) for the per-pixel assessment across all years for the eight land cover classes was 82.5% (±0.2%). Overall accuracy was consistent year-to-year within a range of 1.5% but varied regionally with lower accuracy in the eastern United States. User’s accuracy (UA) and producer’s accuracy (PA) for CONUS ranged from the higher accuracies of Water (UA=96%, PA=93%) and Tree Cover (UA=90%, PA=83%) to the lower accuracies of Wetland (UA=69%, PA=74%) and Barren (UA=43%, PA=57%). For a binary change / no change classification, UA of change was 13% (±0.5%) and PA was 16% (±0.6%) for CONUS when agreement was defined as a match by the exact year of change. UA and PA improved to 28% and 34% when agreement was defined as the change being detected by the map and reference data within a ±2-year window. Change accuracy was higher in the eastern United States compared to the western US. UA was 49% (±0.3) and PA was 54% (±0.3) for the footprint of change (defined as the area experiencing at least one land cover change from 1985–2017). For class-specific loss and gain when agreement was defined as an exact year match, UA and PA were generally below 30%, with Tree Cover loss being the most accurately mapped change (UA=25%, PA=31%). These accuracy results provide users with information to assess the suitability of LCMAP data and information to guide future research for improving LCMAP products, particularly focusing on the challenges of accurately mapping annual land cover change.

Remote Sensing of Environment↗

USGS2020

Evidence for a diagenetic origin of Vera Rubin Ridge, Gale Crater, Mars: Summary and synthesis of Curiosity's exploration campaign

This paper provides an overview of the Curiosity rover's exploration at Vera Rubin ridge (VRR) and summarizes the science results. VRR is a distinct geomorphic feature on lower Aeolis Mons (informally known as Mount Sharp) that was identified in orbital data based on its distinct texture, topographic expression, and association with a hematite spectral signature. Curiosity conducted extensive remote sensing observations, acquired data on dozens of contact science targets, and drilled three outcrop samples from the ridge, as well as one outcrop sample immediately below the ridge. Our observations indicate that strata composing VRR were deposited in a predominantly lacustrine setting and are part of the Murray formation. The rocks within the ridge are chemically in family with underlying Murray formation strata. Red hematite is dispersed throughout much of the VRR bedrock, and this is the source of the orbital spectral detection. Gray hematite is also present in isolated, gray‐colored patches concentrated toward the upper elevations of VRR, and these gray patches also contain small, dark Fe‐rich nodules. We propose that VRR formed when diagenetic event(s) preferentially hardened rocks, which were subsequently eroded into a ridge by wind. Diagenesis also led to enhanced crystallization and/or cementation that deepened the ferric‐related spectral absorptions on the ridge, which helped make them readily distinguishable from orbit. Results add to existing evidence of protracted aqueous environments at Gale crater and give new insight into how diagenesis shaped Mars' rock record.

Journal of Geophysical Research: Planets↗

USGS2020

Diagenesis of Vera Rubin ridge, Gale crater, Mars from Mastcam multispectral images

Images from the Mars Science Laboratory (MSL) mission of lacustrine sedimentary rocks of Vera Rubin ridge on “Mt. Sharp” in Gale crater, Mars, have shown stark color variations from red to purple to gray. These color differences crosscut stratigraphy and are likely due to diagenetic alteration of the sediments after deposition. However, the chemistry and timing of these fluid interactions is unclear. Determining how diagenetic processes may have modified chemical and mineralogical signatures of ancient Martian environments is critical for understanding the past habitability of Mars and achieving the goals of the MSL mission. Here we use visible/near-infrared spectra from Mastcam and ChemCam to determine the mineralogical origins of color variations in the ridge. Color variations are consistent with changes in spectral properties related to the crystallinity, grain size, and texture of hematite. Coarse-grained gray hematite spectrally dominates in the gray patches and is present in the purple areas, while nanophase and fine-grained red crystalline hematite are present and spectrally dominate in the red and purple areas. We hypothesize that these differences were caused by grain-size coarsening of hematite by diagenetic fluids, as observed in terrestrial analogs. In this model, early primary reddening by oxidizing fluids near the surface was followed during or after burial by bleaching to form the gray patches, possibly with limited secondary reddening after exhumation. Diagenetic alteration may have diminished the preservation of biosignatures and changed the composition of the sediments, making it more difficult to interpret how conditions evolved in the paleolake over time.

JGR Planets↗