Detecting trend and seasonal changes in satellite image time series

https://www.fs.fed.us/pnw/pubs/journals/pnw_2010_kennedy002.pdf

Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms

The appetite for up-to-date information about earth’s surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

Change detection from remotely sensed images: From pixel-based to object-based approaches

Continuous change detection and classification of land cover using all available Landsat data

https://www.fs.fed.us/pnw/pubs/journals/pnw_2012_wulder.pdf

Opening the archive: How free data has enabled the science and monitoring promise of Landsat

Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances

MODIS time-series imagery for forest disturbance detection and quantification of patch size effects

Effects of forest ownership and change on forest harvest rates, types and trends in northern Maine

A forest change detection map was developed to document forest gains and losses during the decade of the 1990s. The effectiveness of the Landsat imagery and methods for detecting Maine forest cover change are indicated by the good accuracy assessment results: forest-no change, forest loss, and forest gain accuracy were 90, 88, and 92% respectively, and the good correlation of mapped areas of forest cover loss with forest inventory analysis (FIA) tree size class decrease at the county level. The combination of annually collected permanent FIA plots with accurate statewide forest change maps offers a complementary perspective of Maine's forest resource.

https://www.fs.fed.us/ne/newtown_square/publications/other_publishers/OCR/ne_2005_sader001.pdf

Perspectives of Maine Forest Cover Change from Landsat Imagery and Forest Inventory Analysis (FIA)

A land cover map (1993) was combined with an updated forest change detection map (1991‐2000) to examine forest harvest activity, mostly on private commercial forest lands. Landsat change detection methods indicated that industrial forest owners harvested a higher percentage of forest than non-industrial owners in a northern Maine study area. In the 1980s, the percentage of forest harvested across all ownership classes (five) was higher, the mean harvest patch size was larger, patches were more compact, and the mean perimeter to area ratios were smaller compared to data from the 1990s. For all patch metrics, there was a significant time period effect but there was no effect among landowners. Larger harvest patch size in the 1980s may be partially explained by extensive salvage logging that occurred in the wake of a massive spruce budworm infestation in the 1970s. Softwood types were dominant (> 80%) in regeneration stands approximately 15‐25 years old on all ownerships. Medium spatial resolution Landsat imagery shows promise as a landscape level tool to monitor forest change patterns and trends across multiple ownerships.

/pdf/exploratory-analysis-of-forest-harvest-and-regeneration-1cnqjxmayu.pdf

Suming Jin

Papers

Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances

MODIS time-series imagery for forest disturbance detection and quantification of patch size effects

Effects of forest ownership and change on forest harvest rates, types and trends in northern Maine

Perspectives of Maine Forest Cover Change from Landsat Imagery and Forest Inventory Analysis (FIA)

Exploratory analysis of forest harvest and regeneration pattern among multiple landowners