Imagery Systems for Lunar Exploration Part 1-Down Selecting an Imagery System

Coastal communities in Alaska are frequently subjected to storm surges that erode shorelines and riverbanks, which increases flood risks around critical village infrastructure. Coastal erosion in western Alaska may be occurring currently at rates as high as 15 m per year, but the region has not been mapped in enough detail to accurately assess recent shoreline and inland riverbank erosion rates. To update the mapping of changes in all of SWAlaska’s coastal margins, Landsat 8 satellite imagery was analyzed to detect trends in the normalized difference water index collected from 2013 to 2023. The normalized difference water index values range from -1 to 1, with negative values indicating majority land that includes soil and live vegetation cover and positive values indicating majority water coverage. Results showed marked coastal shoreline erosion at Chagvan Bay, Hagemeister Island, and Nanvak Bay over the past decade. Additional locations of recent bank erosion were detected on the Kuskokwim River, from its upriver mouth past the city of Bethel. Riverbank erosion was also detected in northern Nushagak Bay, and extensive surface-wetting trends around wetlands and riverbanks were detected near the village of Togiak. Moreover, many villages on the west coast of Alaska and in the Kuskokwim River Delta have recently documented climate change case studies highlighting bank erosion, indicating that a variety of related ecological disturbances are ongoing across the region.

Imagery↗

NASA NTRS2016

DeepSAT: A Deep Learning Approach to Tree-Cover Delineation in 1-m NAIP Imagery for the Continental United States

High resolution tree cover classification maps are needed to increase the accuracy of current land ecosystem and climate model outputs. Limited studies are in place that demonstrates the state-of-the-art in deriving very high resolution (VHR) tree cover products. In addition, most methods heavily rely on commercial softwares that are difficult to scale given the region of study (e.g. continents to globe). Complexities in present approaches relate to (a) scalability of the algorithm, (b) large image data processing (compute and memory intensive), (c) computational cost, (d) massively parallel architecture, and (e) machine learning automation. In addition, VHR satellite datasets are of the order of terabytes and features extracted from these datasets are of the order of petabytes. In our present study, we have acquired the National Agriculture Imagery Program (NAIP) dataset for the Continental United States at a spatial resolution of 1-m. This data comes as image tiles (a total of quarter million image scenes with ~60 million pixels) and has a total size of ~65 terabytes for a single acquisition. Features extracted from the entire dataset would amount to ~8-10 petabytes. In our proposed approach, we have implemented a novel semi-automated machine learning algorithm rooted on the principles of "deep learning" to delineate the percentage of tree cover. Using the NASA Earth Exchange (NEX) initiative, we have developed an end-to-end architecture by integrating a segmentation module based on Statistical Region Merging, a classification algorithm using Deep Belief Network and a structured prediction algorithm using Conditional Random Fields to integrate the results from the segmentation and classification modules to create per-pixel class labels. The training process is scaled up using the power of GPUs and the prediction is scaled to quarter million NAIP tiles spanning the whole of Continental United States using the NEX HPC supercomputing cluster. An initial pilot over the state of California spanning a total of 11,095 NAIP tiles covering a total geographical area of 163,696 sq. miles has produced true positive rates of around 88 percent for fragmented forests and 74 percent for urban tree cover areas, with false positive rates lower than 2 percent for both landscapes.

Imagery↗

NASA NTRS1993

Unmixing of Spectral Components Affecting AVIRIS Imagery of Tampa Bay

Models have been developed for use with hyperspectral remote-sensing reflectance data collected just above the air-sea interface for the West Florida Shelf 1,2,3,4,. They respond to variations in pigment, detrital, and gelbstoff absorption, chlorophyll a and gelbstoff flourescence, water Raman Scattering, Backscattering by water and particulate, and bottom depth and albedo.

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