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 to 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.
•Accuracy of 30+ year annual land cover and land cover change assessed.•Reference class labels interpreted for annual data for nearly 25,000 pixels.•Overall accuracy temporally consistent and near 82.5%.•User's and producer's accuracies vary regionally.•Accuracy of annual change problematic motivating need for future improvement.
Science teams for rover-based planetary exploration missions like the Mars Science Laboratory Curiosity rover have limited time for analyzing new data before making decisions about follow-up ...observations. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and focus attention on the most promising or novel observations. Several novelty detection methods have been explored in prior work for three-channel color images and non-image datasets, but few have considered multispectral or hyperspectral image datasets for the purpose of scientific discovery. We compared the performance of four novelty detection methods—Reed Xiaoli (RX) detectors, principal component analysis (PCA), autoencoders, and generative adversarial networks (GANs)—and the ability of each method to provide explanatory visualizations to help scientists understand and trust predictions made by the system. We show that pixel-wise RX and autoencoders trained with structural similarity (SSIM) loss can detect morphological novelties that are not detected by PCA, GANs, and mean squared error autoencoders, but that the latter methods are better suited for detecting spectral novelties—i.e., the best method for a given setting depends on the type of novelties that are sought. Additionally, we find that autoencoders provide the most useful explanatory visualizations for enabling users to understand and trust model detections, and that existing GAN approaches to novelty detection may be limited in this respect.
•Generated several training datasets by integrating existing datasets.•Produced annual land cover maps for the Hawaiian Islands at 30-m resolution over 20 years with different training datasets and ...Change detection and classification (CCDC) algorithm.•Summarized the effect of training datasets (e.g., spatial coverage, quality) on accuracies for different land cover types.•Revealed annual land cover changes in Hawaiʻi.
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 athttps://doi.org/10.5066/P91E8M23.
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 1 year of underestimation and 6-year standard deviation of error between the reference data and the 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 the 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.
Sample-based estimates augmented by complete coverage land-cover maps were used to estimate area and describe patterns of annual land-cover change across the conterminous United States (CONUS) ...between 1985 and 2016. Most of the CONUS land cover remained stable in terms of net class change over this time, but a substantial gross change dynamic was captured by the annual and cumulative time intervals. The dominant types of changes can be grouped into natural resource cycles, increases in urbanization, and surface-water dynamics. The annual estimates over the 30-year time series showed a reduction in the rate of urban expansion after 2006, new growth in cropland after 2007, but a net overall decline in cropland since 1985, and two eras of net tree cover loss, the first one early in the time series and the second starting in 2012. Our study provides a holistic assessment of the CONUS land-cover conversion (class) change and can serve as a new benchmark for future research.
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.
•A modified approach to detect land cover change consistently in Landsat time series.•Change detection rates are more consistent across time and have increased accuracy.•Flexible approach to allow use of additional data sources.
Much of Mars' surface is mantled by bright dust, which masks the spectral features used to interpret the mineralogy of the underlying bedrock. Despite the wealth of near-infrared (NIR) and thermal ...infrared (TIR) data returned from orbiting spacecraft in recent decades, the detailed bedrock composition of approximately half of the Martian surface remains relatively unknown due to dust cover. To address this issue, and to help gain a better understanding of the bedrock mineralogy in dusty regions, Dust Cover Index results from the Mars Global Surveyor Thermal Emission Spectrometer (TES) and analysis of images from the Mars Reconnaissance Orbiter Mars Color Imager (MARCI) were used to identify 63 small localized areas within the classical bright dusty regions of Arabia Terra, Elysium Planitia, and Tharsis Montes as potential “windows” through the dust; that is, areas where the dust cover is thin enough to permit infrared remote sensing of the underlying materials. The mineralogy of each candidate window was inferred using spectra from the Mars Express Observatoire pour la Mineralogie, l'Eau, les Glaces et l'Activité (OMEGA) NIR spectrometer and, where possible, TES. Twelve areas of interest returned spectra that are consistent with mineral species expected to be present at the regional scale, such as high- and low-calcium pyroxene, olivine, and iron-bearing glass. Distribution maps were created using previously defined index parameters for each species present within an area. High-quality TES spectra, if present within an area of interest, were deconvolved to estimate modal mineralogy and to support NIR interpretations. OMEGA data from Arabia Terra and Elysium Planitia are largely similar and indicate the presence of high-calcium pyroxene with significant contributions of glass and olivine, while TES data suggest an intermediate between the established compositions of the southern highlands and Syrtis Major. Limited data from Tharsis are consistent with low-calcium pyroxene mixed with lesser amounts of glass and high-calcium pyroxene. TES data from southern Tharsis correlate well with the previously inferred compositions of the Aonium and Mare Sirenum highlands immediately to the south. Of particular note is the detection of iron-bearing glass as a significant component of all three analyzed regions, especially in Tharsis. Overall, the underlying compositions of the classically dust-covered regions of Mars appear consistent with the compositions of adjacent and other low-albedo (not dust covered) regions of the planet identified in previous studies, with the noted contribution from iron-bearing glass.
•Mineralogy of low-dust areas within the bright dusty regions of Mars is investigated.•Twelve areas exhibited spectra that are consistent with mafic mineralogy.•Near-infrared data reveal pyroxenes with significant contributions of glass and olivine.•Thermal infrared data largely agree with previous studies of dark regions.
•Pancam estimates the Lambert albedo at Gusev crater and Meridiani Planum, Mars.•Albedo varies on small spatial/temporal scales due to localized wind events.•Albedo measurements from Pancam, MOC, CTX ...and HiRISE agree to within 15%.
The Mars Exploration Rovers (MER) Spirit and Opportunity have systematically used their Panoramic Camera (Pancam) instruments to estimate the Lambert albedo of the surface across their traverses in Gusev crater and Meridiani Planum. The 360˚ “albedo pan” observations acquired with Pancam's broadband (739 ± 338 nm) L1 filter allow for quantitative estimates of the overall surface albedo and measurements of individual surface features. As of November 2016, over nearly six Mars years of the MER mission, Spirit acquired 20 albedo pans (over 7,730 m of traverse distance) and Opportunity acquired 117 albedo pans (over 42,368 m of traverse distance). For Spirit, this comprises the rover's complete dataset. The ranges of Pancam-derived albedos at Gusev crater (0.14–0.24) and at Meridiani Planum (0.11–0.22, with one anomalously high measurement of 0.27 during the July 2007 global dust storm) are consistent with large-scale albedos of the sites as previously determined by the Viking Orbiter Infrared Thermal Mapper (IRTM), Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES), MGS Mars Orbiter Camera (MOC), Mars Odyssey Thermal Emission Imaging System (THEMIS), Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) and MRO Mars Color Imager (MARCI) instruments. Through comparisons with atmospheric opacity measurements, temporal changes in Pancam albedo values provide insights into interactions between the Martian surface and atmosphere. Pancam observations are also used to “ground truth” measurements from orbit and validate radiometric calibrations, and we present comparisons across the full rover traverses to MOC, CTX, and MRO High Resolution Imaging Science Experiment (HiRISE) data. Albedo averages from the same regions observed by Pancam and all three orbital instruments generally agree to within ± 15%. The few instances found where cross-instrument comparisons exceed the estimated instrument calibration uncertainties can be attributed to atmospheric effects and/or differences in viewing geometries.
The increasing availability of high-quality remote sensing data and advanced
technologies has 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 a 30 m 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 products and five products related to land surface change. 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 30 m annual land change
datasets that include land cover, land cover change, and spectral change
spanning 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. The LCMAP
products introduced in this paper are freely available at
https://doi.org/10.5066/P9W1TO6E (LCMAP, 2021).
As part of the Phase 2 Bagnold Dune campaign at Gale Crater, Mars, constraints on the geochemistry, mineralogy, and oxidation state of pristine and disturbed linear sand ripples were made using ...visible/near‐infrared spectral observations for comparison to Phase 1 spectra of the barchan dunes to the north. Spectra acquired by the ChemCam and Mastcam instruments (400–1,000 nm) at four Phase 2 locations revealed similar overall spectral trends between the two regions, but most Phase 2 sands were redder in the visible wavelengths. The majority of targets exhibited lower red/infrared ratios, higher ~530‐nm band depths, and higher red/blue ratios than Phase 1 samples, suggesting a greater proportion of redder, fine‐grained, ferric sands in Phase 2 samples. This is consistent with the slightly greater proportion of hematite in Phase 2 samples as determined from CheMin analyses of the Ogunquit sands, which may reflect contamination from the surrounding hematite‐bearing Murray formation bedrock.
Plain Language Summary
The Mars Science Laboratory Curiosity rover visited the southern portion of the Bagnold Dunes to look for differences in the types of sand grains that comprised the dunes and ripples. The rover's cameras and spectrometers provided information about the color of the sands, which was used to infer the composition and types of minerals. Overall, the sands in this part of the Bagnold Dunes were a bit redder than those further to the north that were studied previously. We interpreted this to mean that the southern sands contained more oxidized (rusted) iron particles. Because the rocks surrounding these dunes were known to contain a fair amount of red, iron‐rich minerals, it is probable that the sands were mixed with a small amount of broken fragments from these rocks.
Key Points
Bagnold Phase 2 sands exhibit higher 535‐nm band depths and red/blue ratios and lower 600‐/700‐nm ratios than Bagnold Phase 1 sands
Phase 2 sands contain a greater amount of redder, ferric materials, likely owing to minor hematite contamination from nearby bedrock
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