JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1072 10.6339/22-JDS1072 Statistical Data Science Multiresolution Broad Area Search: Monitoring Spatial Characteristics of Gapless Remote Sensing Data https://orcid.org/0000-0003-1882-8239 WendelbergerLaura J.wendelberger1@llnl.gov12∗ https://orcid.org/0000-0003-4341-4353 GrayJosh M.34 https://orcid.org/0000-0003-1461-6212 WilsonAlyson G.1 https://orcid.org/0000-0002-3604-0747 HouborgRasmus5 https://orcid.org/0000-0002-5473-120X ReichBrian J.1 Department of Statistics, North Carolina State University, Raleigh, NC, USA Lawrence Livermore National Laboratory, Livermore, CA, USA (current affiliation) Department of Forestry, North Carolina State University, Raleigh, NC, USA Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA Planet Labs PBC, San Francisco, CA, USA Corresponding author. Email: wendelberger1@llnl.gov. 20223102022204545565 Supplementary Material

The construction of 2D Haar wavelets, a calculation for induced prior change probability, and sensitivity results are available in the Supplementary Material. The Jacksonville and Dubai dataset is proprietary, but code to generate and analyze a simulated dataset is included to demonstrate the algorithm.

31720222992022 2022 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2022 Open access article under the CC BY license.

Global earth monitoring aims to identify and characterize land cover change like construction as it occurs. Remote sensing makes it possible to collect large amounts of data in near real-time over vast geographic areas and is becoming available in increasingly fine temporal and spatial resolution. Many methods have been developed for data from a single pixel, but monitoring pixel-wise spectral measurements over time neglects spatial relationships, which become more important as change manifests in a greater number of pixels in higher resolution imagery compared to moderate resolution. Building on our previous robust online Bayesian monitoring (roboBayes) algorithm, we propose monitoring multiresolution signals based on a wavelet decomposition to capture spatial change coherence on several scales to detect change sites. Monitoring only a subset of relevant signals reduces the computational burden. The decomposition relies on gapless data; we use 3 m Planet Fusion Monitoring data. Simulations demonstrate the superiority of the spatial signals in multiresolution roboBayes (MR roboBayes) for detecting subtle changes compared to pixel-wise roboBayes. We use MR roboBayes to detect construction changes in two regions with distinct land cover and seasonal characteristics: Jacksonville, FL (USA) and Dubai (UAE). It achieves site detection with less than two thirds of the monitoring processes required for pixel-wise roboBayes at the same resolution.

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