Orbital radar studies of paleodrainages in the central Namib Desert

N. Lancaster; G. G. Schaber; J.T. Teller

doi:10.1016/s0034-4257(99)00088-7

Home/Archive/USGS

USGS · 70022935

Orbital radar studies of paleodrainages in the central Namib Desert

N. Lancaster·G. G. Schaber·J.T. Teller

View original source ↗Export metadata ↓

Abstract

Orbital radar images of the central Namib Desert show clearly the extent of relict fluvial deposits associated with former courses of the Tsondab and Kuiseb rivers. South of the Kuiseb River, radar data show the existence of a drainage network developed in calcrete-cemented late Tertiary fluvial deposits. The sand-filled paleovalleys are imaged as radar-dark tones in contrast to the radar-bright interfluves where the calcreted gravels occur. The drainage network developed as a result of local runoff from indurated gravels and channeled surface and subsurface flow to the sites of the many interdune lacustrine deposits found in the area. (C) Elsevier Science Inc., 2000.Orbital radar images of the central Namib Desert show clearly the extent of relict fluvial deposits associated with former courses of the Tsondab and Kuiseb rivers. South of the Kuiseb River, radar data show the existence of a drainage network developed in calcrete-cemented late Tertiary fluvial deposits. The sand-filled paleovalleys are imaged as radar-dark tones in contrast to the radar-bright interfluves where the calcreted gravels occur. The drainage network developed as a result of local runoff from indurated gravels and channeled surface and subsurface flow to the sites of the many interdune lacustrine deposits found in the area.

Explore related subjects

Remote Sensing of Environment Article

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

N. Lancaster, G. G. Schaber, J.T. Teller. 2000. Orbital radar studies of paleodrainages in the central Namib Desert. https://doi.org/10.1016/s0034-4257(99)00088-7

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Methodology for assessing the accuracy of long-term, annual land cover monitoring at national scale: Experiences from the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative

Annual land cover monitoring using medium-resolution satellite imagery represents a transition from periodic mapping campaigns to continuous observation systems. This transition introduces accuracy assessment challenges not encountered in one-time mapping efforts, particularly when the initial monitoring period is extended forward in time. These challenges are not addressed in existing guidance, which remains largely framed around the evaluation of maps of a single date or maps showing changes between two dates. We present a set of methodological adaptations to adjust to this evolving paradigm, using as a case study the U.S. Geological Survey (USGS) Land Change Monitoring, Assessment, and Projection (LCMAP) initiative, which generated annual land cover and land cover change products across the historical Landsat archive. Assessing the accuracy of these products was an essential component of LCMAP and resulted in an extensive reference sample dataset that may be used for accuracy estimation, area estimation, or support in the development of new algorithms or models. We review the LCMAP methodology and highlight challenges encountered and lessons learned that are transferable to other long-term, temporally dense monitoring programs. This framework demonstrates that statistically rigorous assessment can scale to operational annual land cover production, while the experiences of the project clarify where existing good practice guidance should be extended to support the needs of ongoing, continuous land cover monitoring.

Remote Sensing of Environment↗

USGS2026

On-demand global Landsat evapotranspiration product: Development, evaluation, and dissemination

Global actual evapotranspiration (ET) is one of the essential climate variables needed to understand and manage the relationships among food, energy, and water resources. The U.S. Geological Survey Earth Resources Observation and Science (EROS) Center launched a provisional ET product in 2020, offering on-demand, field-scale global coverage derived from Landsat data through the EROS Science Processing Architecture (ESPA) platform. The ESPA interface provides ET data for cloud-free Landsat overpasses starting in 1982 with Landsat 4 through the current Landsat 9. The ET data are delivered as a Provisional Level-3 Science product created using the Operational Simplified Surface Energy Balance (SSEBop) model. Landsat surface temperature and reference ET are the main model drivers along with vegetation index and net radiation for model parameterization. A large volume of Landsat-based ET orders (e.g., over 1,200,000 images from June 2020 through December 2025) around the world indicate increasing awareness and application of the ET data. The ESPA platform enables land and water resource managers and researchers to access a first-order ET product without requiring advanced knowledge of remote sensing technology or evapotranspiration modeling. We present the methodology and workflow of the on-demand Landsat ET product and its performance evaluations over diverse hydro-climatic settings. The product can help estimate field-scale consumptive water use and thus quickly and consistently assess historical water use, allocation, and budget to inform water management under changing environments. Future ET data aggregated to monthly and seasonal time scales are expected to enhance integration with decision-making tools and procedures.

Remote Sensing of Environment↗

USGS2026

A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping

Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD. LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics. Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60% overall agreement, compared to 71.1 ± 1.7% agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ± 1.22% agreement, compared to 84.6 ± 1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

Remote Sensing of Environment↗