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A review of methods for scaling remotely sensed data for spatial pattern analysis

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Abstract

Context

Landscape ecologists have long realized the importance of scale when studying spatial patterns and the need for a science of scaling. Remotely sensed data, a key component of a landscape ecologist’s toolbox used to study spatial patterns, often requires scaling to meet study requirements.

Objectives

This paper reviews methods for scaling remote sensing-based data, with a specific focus on spatial pattern analysis, and distills the numerous approaches based on data type. It also discusses knowledge gaps and future directions.

Methods

Key papers were identified through a systematic review of the literature. Trends, developments, and key methods for scaling remotely sensed data and spatial products derived from these data were identified and synthesized to detail the general progression of a science of scaling in landscape ecology.

Results

Upscaling both continuous and categorical data can oversimplify data, creating challenges for spatial pattern analysis. Object-based and neighborhood approaches can help, and since patch boundaries are more likely to align with objects than pixels, these may be better options for landscape ecologists. Many downscaling methods exist, but these approaches are not being widely employed for spatial pattern analysis.

Conclusions

A diverse range of scaling methods are available to landscape ecologists, but work remains to integrate them into spatial pattern analysis. Moving forward, advances in computer science and engineering should be explored and cross-disciplinary research encouraged to further the science of scaling remotely sensed data.

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Fig. 1
Timeline from 1970s to 2020s. Scaling methods: 1981 Spectral Mixture Analysis. 1982 Hue-Intensity-Saturation Method. 1989 Fractal-based analysis. 1997. Taylor Series Expansion Method and Object specific upscaling. 2002 Fractals. 2004 Area to point knifing. 2008 Point centered distance-weighted. 2016 Downscaling with random forest. Technological landmarks and events: 1972 Landsat. 1981 ArcInfo (formerly Arc/INFO). 1982 ERDAS. 1984 GRASS GIS. 1986 IALE-North America. 1991 ArcView 1.0. 1992. DigitalGlobe/Maxar Technologies. 1993. R Programming Language. 1995 FRAGSTATS. 1999 ArcGIS Desktop. 2002 QGIS. 2008 Open Landsat data policy. 2010 Google Earth Engine and Planet. 2011 NEON. 2014 Sentinel missions. 2016 Federal Aviation Administration Part 107 rules. 2021 Landsat 9. Foundational work & reviews: 1982 Allen and Starr. 1987 Meentemeyer and Box. 1989 Weins. 1989 Turner. 1992 Levin. 1997 Quattrochi and Goodchild. 2002 Wu and Hobbs. 2004 Wu. 2009 Wu and Li*. 2013 Atkinson. 2019 Ge. 2020 Lei and Liu. 2021 Javan.
Fig. 2
Schematic of landscape modeling and analysis showing the scales, measurements, spatial data, and scaling direction. There is region, with measurements taken about ecoregion, and biomes, spatial data is greater than 1 kilometer such as AVHRR and VIIRS. Then there is the landscape scale, with measurements of land use and land cover, spatial data is greater than 100 meters but smaller than 500 meters such as MODIS and MERIS. Stand is the third scale and measurements might be of biomass, leaf area, basal area, and this spatial data is greater than 5 meters but less than 100 meters such as Landsat, Sentinel and LISS imagery, and ASTER/SRTM-DEM. Fourth is the plot/tree scale, measurements are crown, tree height, and species, and spatial data is less than 5 meters such as commercial imagery (IKONOS, WorldView 3, etc.) LiDAR, and drone imagery. Finally there is leaf, measurements might be of chlorophyll, shape, arrangement, and the spatial data is in centimeters, such as LiDAR, drone imagery, and spectral signatures.
Fig. 3
Three panels of DEM are shown. In all three panels a box of identical size is outlined. In the first panel, there are 100 pixels in total in the box and the resolution is 30 meters. The minimum value is 44.1 meters and the maximum value is 48.5 meters. In the second panel the total number of pixels in the box is 4, the resolution is 150 meters, the minimum value is 44.7 meters, and the maximum value is 46.2 meters. In the third panel the total number of pixels in the box is 1, the resolution is 300 meters, the minimum and maximum value is 45.4 meters.
Fig. 4
Three panels of VIIRS data are shown. The same size box is outlined in all three panels. The first panel shows the state of North Carolina along the Atlantic Ocean and an arrow is pointing to the box. The image in the first panel has a resolution of 1,000 meters. In the second panel, the box has been divided into four and the pixel value is 1.56, there are four pixels in total, and the resolution is 500 meters. In the final panel the box has been divided into 16, the pixel value is 1.56, there are 16 pixels in total, and the resolution is 250 meters.
Fig. 5
Flow chart for selecting a scaling method. Relative ease of implementation is broken down into simple, moderate, and complex with a default of moderate. Upscaling methods of categorical data include majority rules aggregation (simple), random rule-based which maintains land cover proportions, point spread function, and point-centered distance-weighted which recognizes spatial correlation. Upscaling continua to categorical methods include object specific upscaling (simple) and OSU with Moran’s Index, central pixel resampling (simple), mean and median aggregation (simple). Upscaling continua to continua methods include moving window data aggregation with recognizes spatial correlation, central pixel resampling (simple), fractals, which can be challenging with many inputs, and mean and median aggregation (simple). Downscaling categorical data methods include general additive models and multinomial logistic regression, which is used for landscape metrics. Downscaling continua rot categorical methods include super resolution mapping and deep learning super resolution mapping (complex). Downscaling continua to continua methods include bilinear and cubic resampling (simple) which does not require ancillary data, Pansharpening methods such as the hue-intensity saturation method (simple) which preserves color, and methods that require training data like Bayesian methods and deep learning techniques (both complex). Other downscaling continua methods are Taylor series expansion methods, Kalman filter (complex), spatial area-to-point kriging, geographically weight area to point kriging, physical scaling method (complex), multi scale geographically weighted regression kriging, and machine learning.

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Funding

K. Markham is supported by a UGA Graduate School Research Assistantship with the Department of Geography. A.E. Frazier is supported by U.S. National Science Foundation Grant #1934759. K. K. Singh is supported by the Millennium Challenge Corporation #95332418T0011.

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Authors and Affiliations

  1. Center for Geospatial Research, Department of Geography, University of Georgia, Athens, GA, 30602, USA

    Katherine Markham & Marguerite Madden

  2. Spatial Analysis Research Center and Center for Global Discovery and Conservation Science, School of Geographical Science and Urban Planning, Arizona State University, Tempe, AZ, 85281, USA

    Amy E. Frazier

  3. AidData, Global Research Institute, William & Mary, 427 Scotland Street, Williamsburg, VA, 23185, USA

    Kunwar K. Singh

  4. Center for Geospatial Analysis, William & Mary, 400 Landrum Drive, Williamsburg, VA, 23185, USA

    Kunwar K. Singh

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  1. Katherine Markham
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  2. Amy E. Frazier
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  3. Kunwar K. Singh
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  4. Marguerite Madden
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All authors contributed to the conceptualization, research, writing, and editing of the manuscript.

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Markham, K., Frazier, A.E., Singh, K.K. et al. A review of methods for scaling remotely sensed data for spatial pattern analysis. Landsc Ecol 38, 619–635 (2023). https://doi.org/10.1007/s10980-022-01449-1

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