Measuring Similarity of Deforestation Patterns in Time and Space across Differences in Resolution

Abstract

This article demonstrated an easily applicable method for measuring the similarity between a pair of point patterns, which applies to spatial or temporal data sets. Such a measurement was performed using similarity-based pattern analysis as an alternative to conventional approaches, which typically utilize straightforward point-to-point matching. Using our approach, in each point data set, two geometric features (i.e., the distance and angle from the centroid) were calculated and represented as probability density functions (PDFs). The PDF similarity of each geometric feature was measured using nine metrics, with values ranging from zero (very contrasting) to one (exactly the same). The overall similarity was defined as the average of the distance and angle similarities. In terms of sensibility, the method was shown to be capable of measuring, at a human visual sensing level, two pairs of hypothetical patterns, presenting reasonable results. Meanwhile, in terms of the method′s sensitivity to both spatial and temporal displacements from the hypothetical origin, the method is also capable of consistently measuring the similarity of spatial and temporal patterns. The application of the method to assess both spatial and temporal pattern similarities between two deforestation data sets with different resolutions was also discussed.

Evaluating Visible–Infrared Imaging Radiometer Suite Imagery for Developing Near-Real-Time Nationwide Vegetation Cover Monitoring in Indonesia

“…(CDFs) of various geometric features across thresholds ranging from 50 to 130. This a ysis, as reported in [25], confirmed that a threshold near 100 was optimal.…”

“…The study conducted a sensitivity and sensibility analysis utilizing probability density functions (PDFs) and cumulative distribution functions (CDFs) of various geometric features across thresholds ranging from 50 to 130. This analysis, as reported in [25], confirmed that a threshold near 100 was optimal.…”

“…This approach involves systematically analyzing all new VIIRS data available and has the potential to be operationalized every 8 days to quickly identify vegetation changes across Indonesia. The previous study [25] established an NDOAI threshold for detecting changes in vegetation cover. It compared the spatial and temporal pattern similarities between deforestation datasets from MODIS and Landsat-8 OLI satellite imagery, which have different spatial and temporal resolutions.…”

“…It compared the spatial and temporal pattern similarities between deforestation datasets from MODIS and Landsat-8 OLI satellite imagery, which have different spatial and temporal resolutions. The study conducted a sensitivity and sensibility analysis utilizing probability density functions (PDFs) and cumulative distribution functions The previous study [25] established an NDOAI threshold for detecting changes in vegetation cover. It compared the spatial and temporal pattern similarities between deforestation datasets from MODIS and Landsat-8 OLI satellite imagery, which have different spatial and temporal resolutions.…”

“…The development of an early warning system for vegetation cover changes, which utilized Terra-MODIS with a resolution of 500 m, has resulted in the ability to provide information on vegetation changes every 8 days [24]. However, the use of this change detection method in Indonesia's territory is limited by the spatial resolution of the data, particularly in areas such as Java Island, where transient land cover changes may result in a feature size that is less than the minimum detectable extent or complex land cover patterns that affect the threshold value [25]. Despite its limitations in detecting vegetation changes within an area of ±25 ha, the system remains a valuable tool for monitoring vegetation changes in Indonesia due to its ability to provide timely information.…”