Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Multivolume work

Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [WP-2 LAND USE AND LAND COVER]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: MONITORING LANDSCAPE LEVEL PROCESSES USING REMOTE SENSING OF LARGE PLOTS. Raymond L. Czaplewski

Document type:: Multivolume work

Structure type:: Chapter

MONITORING LANDSCAPE LEVEL PROCESSES USING REMOTE SENSING OF LARGE PLOTS Raymond L. Czaplewski Mathematical Statistician USDA Forest Service Research Rocky Mountain Forest and Range Experiment Station 240 W. Prospect Street Fort Collins, CO 80526 U.S.A. ABSTRACT Global and regional assessments require timely information on landscape level status (e.g., areal extent of different ecosystems) and processes (e.g., changes in land use and land cover). To measure and understand these processes at the regional level, and model their impacts, remote sensing is often necessary. However, processing massive volumes of remotely sensing data can be infeasible if high resolution data are required for very large regions. Remote sensing of sample plots, rather than a census of the entire area, can solve certain problems. Statistical aspects of remote sensing for large plots are described, concentrating on methods needed to produce sample estimates, combine time series of ancillary estimates from other sources, calibrate for misclassification bias, and combine remotely sensed data with model predictions. These methods might improve spatial and temporal accuracy, and test our understanding of processes that are captured in landscape level models. KEY WORDS: Kalman filter, composite estimator, classification error, calibration, landscape models, landscape models, spatial autocorrelation, spatial heterogeneity. 1. INTRODUCTION A simple example is used in the following discussion, where status is defined as the proportion forested area, and ground-based field work is used to determine if a point is truly forested. This hypothetical example uses the sampling frame and certain design features proposed by the U.S. Environmental Protection Agency as part of its Environmental Monitoring and Assessment Program (EMAP). This is a cooperative program among several agencies of the United States Government, including the USDA Forest Service. The EMAP sampling frame is composed of a triangular grid. Each 640 km 2 hexagon on this grid contains a 40 km 2 hexagon (i.e., a 1/16 sample by area) that is observed using Landsat data and high altitude aerial photography. However, the statistical models readily apply to sampling frames used in other programs, such as that proposed by the Food and Agricultural Organization of the United Nations for monitoring and assessment of the world’s tropical forests, or that proposed by Czaplewski et al. (1987) for updating forest inventory estimates made by the USDA Forest Service. Underlining statistical models for estimated status of the stratum and each sample unit are presented in Section 2. Section 3 gives a calibration model for measurement error that occurs when true status can not be perfectly classified with remotely sensing. Section 4 presents an illustration of the statistical composite estimator, which combines estimates from different sources. In Section 5, the composite estimator is used to combine calibrated, remotely sensed estimates from many sample units into an estimate of stratum status. The Kalman filter, which is a more general composite estimator, is introduced in Section 6. In Section 7, the Kalman filter is used to update estimates of status for each sample unit, and combine them into an estimate of status for the stratum. Section 8 uses the Kalman filter to combine ancillary estimates for aggregations of sample units. The cycle of landscape monitoring and process modeling is discussed in Section 9. 2. SAMPLE FRAME Consider a sampling frame composed of a grid of large sample units that are well suited for monitoring using remote sensing. The frame is confined to a contiguous, homogeneous geographic area, i.e., a stratum. Estimates for status of multiple strata could be summed for regional or global assessments if definitions for true status are shared among strata. 2.1 Example sampling frame Assume a large, homogeneous, geographically contiguous stratum is comprised of a known number of cells (n), e.g., 640 km 2 hexagons. A stratum might be the coastal plain in the southeastern United States, which is 320,000 km 2 in size; the number of cells n would be (320,000/640)=500. Each 640 km 2 cell is sampled with a single 40 km 2 sample unit, which is centered on the 640 km 2 cell. Each 40 km 2 sample unit is treated as a permanent plot, and each is periodically observed over time using remote sensing. 2.2 Statistical model for status The status of the stratum (e.g., proportion forest) is denoted as unknown nonrandom variable X. Since the stratum is assumed spatially homogeneous, status of each sample unit in the stratum is assumed equal to the stratum status X. Deviation of the observed status of sample unit i from the stratum status is the unknown random variable Wi . The statistical model for estimated status of sample unit i is Xi = X + K, for i = {1, 2, ... , n} (1) Xi is assumed an unbiased estimate of X, i.e., E[¥i] = 0. Variance of Wi, denoted var(ffi), is assumed heterogeneous among the n sample units in the stratum, i.e., var( Wi) does not necessarily equal the var( Wj ) for i not equal to j. Deviations among sample units are not assumed independent, i.e., Et&’i&'j] might be nonzero. No other distributional assumptions are made.

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