/pdf/remote-sensing-of-the-environment-4yfxjaaswe.pdf

Remote sensing of the environment

Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery

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

Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization

Accurate land cover information is required for science, monitoring, and reporting. Land cover changes naturally over time, as well as a result of anthropogenic activities. Monitoring and mapping of land cover and land cover change in a consistent and robust manner over large areas is made possible with Earth Observation (EO) data. Land cover products satisfying a range of science and policy information needs are currently produced periodically at different spatial and temporal scales. The increased availability of EO data—particularly from the Landsat archive (and soon to be augmented with Sentinel-2 data)—coupled with improved computing and storage capacity with novel image compositing approaches, have resulted in the availability of annual, large-area, gap-free, surface reflectance data products. In turn, these data products support the development of annual land cover products that can be both informed and constrained by change detection outputs. The inclusion of time series change in the land cover mapping process provides information on class stability and informs on logical class transitions (both temporally and categorically). In this review, we present the issues and opportunities associated with generating and validating time-series informed annual, large-area, land cover products, and identify methods suited to incorporating time series information and other novel inputs for land cover characterization.

Optical remotely sensed time series data for land cover classification: A review

Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing

Increasing population, new development in lake and river recreation areas, and growing towns all translate into increasing impervious surface areas across Minnesota. As urban stormwater runoff from impervious areas can have profound negative impacts to receiving waters, it is a critical new component of statewide stormwater education and management efforts. This paper describes the methods and results of Landsat classification and mapping of impervious surface area for the entire state of Minnesota, including approximately 500 cities and high priority sensitive lake watersheds for 1990 and 2000 census periods. From 1990 to 2000 the estimated amount of impervious area for the state increased 145,830 ha, from 1.2 to 1.9% of the total land area. The Landsat-based processing and classification provides critically important, consistent and multi-date, data for any area of Minnesota. It is envisioned that this data and updates, will be the foundation of Minnesota’s stormwater management efforts.

/pdf/estimation-mapping-and-change-analyis-of-impervious-surface-4lufld1hoz.pdf

Estimation, mapping and change analyis of impervious surface area by landsat remote sensing

This document contains metadata and description of land-cover classification of approximately 5.1 million acres of land bordering Minnesota, U.S.A. and Ontario, Canada. The classification focused on the separation and identification of specific forest-cover types. Some separation of the nonforest classes also was performed. The classification was derived from multi-temporal Landsat TM/ETM+ imagery (collected from the years 1999-2001) using eCognition segmentation software to identify stands and the kNN algorithm to perform the spectral classification. Reference data sites were obtained for Minnesota from the Forest Inventory and Monitoring program dataset and for Ontario from the Forest Resource Inventory program. This dataset was created as an input layer in the LANDIS-II forest landscape model for simulation of future forest disturbance and compositional change over the Border Lakes landscape.

Border Lakes land-cover classification

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Brian C. Loeffelholz

Papers

Land cover classification and change analysis of the Twin Cities (Minnesota) Metropolitan Area by multitemporal Landsat remote sensing

Estimation, mapping and change analyis of impervious surface area by landsat remote sensing

Border Lakes land-cover classification

Cloquet Forestry Center 2005 Covertype Mapping Project