No! Each series was created to be used together. Every date of the Version 2.2 series is different from every date of the Version 2.3 series.

Our Agricultural Field category includes areas that are under agricultural uses such as crop production and/or active pasture. It is also likely to include some abandoned agricultural areas that have not undergone conversion to woody vegetation.  The USDA NRCS “Natural Resources Inventory” is similar, in that is based on Landsat imagery, but since the NRI is a national survey it is created by statistical sampling, with a larger likelihood of error than CLEAR’s processing of each and every pixel.  CCL and NRI data are nonetheless fairly compatible, if you make allowances for differences in how the major land cover categories are defined.  The USDA Agricultural Census data is collected via field office reports, mostly gathered via survey of farmers, and thus is not comparable to our research methods.  Also, these data include all land included on farmsteads, not only agricultural fields but also forest, developed lands, and other land uses; thus it predictably quotes larger acreage statistics for comparable time periods.

The purpose of these maps is to show areas that went from one of the “undeveloped” land cover classes (other grasses, agricultural field, deciduous forest, coniferous forest, non-forested wetland, tidal wetland, etc.) to the “developed footprint” classes of developed or turf & grass. Turf & grass is included because, most of the time, it is closely associated with development.

The two types of change map both show the same areas, as defined above, that have changed from undeveloped to developed during the 1985 – 2015 period. However, the color schemes of the two maps show slightly different aspects of the change - what it went to (change to) and what it came from (change from).

In this kind of research, there are three ways to measure change as described in the table below. The percent change reported in the data tables the Your Town and Your Basin page is the Relative rate of change. This means that if there is a small area to begin with (little area in time 1, or 1985), then the relative rate is likely to be high (or high percentage). If there is a large area to being with (lots of area in time 1, or 1985), then the relative rate is likely to be low (a small percentage).

T stands for a time period, so T1 would be an earlier date than T2. In most data tables on this website, T1 is 1985 and T2 is 2006.

The land cover maps were produced by interpreting Landsat satellite images, which have a ground resolution of 30 meters or approximately 100 feet. At this resolution, the satellite sensor "sees" areas that are about a quarter of an acre in size. Within any quarter acre, there may be a number of different landscape features and what the satellite ends up "seeing" is the largest feature or the largest group of similar features. Chances are your house, especially in forested rural areas, will be "dwarfed" by the surrounding trees. This will cause the area to be classified as forest rather than developed. However, if your house is in a neighborhood where houses, driveways, sidewalks, etc. are the predominant landscape features, then it will fall into a "developed" land cover class. Thus, what gets mapped depends on what the predominant landscape features are.

The land cover data were produced from Landsat satellite images using a computer application called image processing. Landsat images are made up of millions of small squares called pixels. Each pixel represents an area on the ground that measures 30 meters by 30 meters. For each pixel, the Landsat image records the amount of reflected energy in 6 narrow bands of the electromagnetic spectrum (red, green and blue visible light, a near-infrared and two mid-infrared bands). Because landscape features reflect light differently, we can use reflectance data to identify areas of deciduous forest, coniferous forest, water, etc. This is where the image processing software is so helpful. Rather than you or me having to analyze data for each pixel, the software can do it much faster, and with the help of image analysts, can assign pixels to land cover classes based on differences and/or similarities in reflectance values.

Of course we'd like to think that the datasets are error free but the reality is that there are misclassification errors. A great deal of time was spent to avoid and correct errors. A variety of ancillary data sources such as USGS topographic maps and orthophotos, were used to aid the classification process. An accuracy assessment is under way so that we can quantitatively assess the accuracy of each land cover map. However, as of December 2008, we have not yet completed the accuracy assessment.

There are several reasons why we chose Landsat imagery for the Land Cover Project.

1. A Landsat image is about 185 kilometers on each side and one image covers almost the entire state. One image, acquired on a cloud free day, would provide the bulk of the data for each of the five dates.
2. Landsat imagery is relatively inexpensive or free.
3. 30-meter resolution Landsat imagery was first collected in 1982 making it possible to create historic land cover data for change studies.

High resolution datasets were not used for the regional analysis for these reasons:

• Unmanageable file size.

• Too many scenes. Images captured from high resolution satellites have much smaller footprints (area covered on the earth) than Landsat images. It would be difficult to create a seamless dataset from so many images.
• Limited historical archive. Commercial high resolution imagery has only been available since the late 1990's. The limited archive would prohibit meeting the project objective - to derive a time series of land cover over a 20+ year period.
• Likelihood of seasonal and temporal variability. Because so many scenes would be required to cover the study area, it is likely that images would be captured on many different days, introducing artifacts such as variations in vegetation phenology and atmospheric effects (haze).
• Classification Methodology. Classification techniques applied to the Landsat images in this project do not work well on high resolution imagery. New techniques and software would need to be used. Currently, the software does not adequately handle extremely large datasets.
• High Cost. High resolution satellites are operated commercially and not by the government, they are expensive, especially for the huge number of images necessary to cover the area.
• Project Objectives. The objective of the project was to create a regional assessment. Detail available in high resolution images actually makes it more difficult to provide regional information.

The first Landsat satellite was launched in 1972. However, it acquired imagery at 80-meter resolution, which is too coarse for this project, and in different bands of the electro-magnetic spectrum. Since we wanted to create land cover data sets that were comparable, we need to use spatially and spectrally consistent imagery. The first Landsat satellite to acquire 30-meter resolution imagery was launched in 1982.

No. With Landsat imagery it is possible to consistently and accurately determine what is on the land's surface but it's not possible to determine how the land is being used. For example, at 30-meter resolution, a group of homes, a group of farm buildings or a group of small offices may reflect light similarly. It's possible to classify the group as developed land but it's difficult, if not impossible, to determine reliably how the buildings are being used.

The land cover data certainly show an increase in developed land between any two time periods. However, it is likely that isolated or small development, especially within forested areas, may be missed due to the spatial resolution of the Landsat imagery. Therefore, we consider this to be a conservative measure of development. It's also worth mentioning that for the developed land cover class, the land cover data only show the actual developed potion of any particular site. In other words, if a 100 acre parcel is developed as a business park, only that portion that includes buildings, parking lots, roads etc. will be depicted as developed. Any vacant portion of such a site would be classified based on the predominant land cover - forest, non-forested wetland, turf & grass, etc.

Any of the land cover data can be downloaded. All the data are in Connecticut State Plane Coordinates, NAD 83 and are in units of feet. To help CLEAR researchers understand how the land cover data are being used and to notify users via e-mail of new derived data products, we are requesting that you register prior to downloading the data. A simple easy-to-complete on-line form is provided for this purpose.

The maps and information included in this project (except for the summary maps) are organized by the watershed level known as Hydrologic Unit Code 12, or “HUC-12,” the smallest of the available basins delineated by the US Geological Survey.  There are 195 HUC-12 basins within the study area, averaging about 30 square miles (19,256 acres) in area. They range from just over 3.5 square miles (2346 acres) to 62 square miles (39,655 acres).

HUCs Explained: The United States Geological Survey created a hierarchical system of hydrologic units originally called regions, sub-regions, accounting units, and cataloging units. Each unit was assigned a unique Hydrologic Unit Code (HUC). As of 2010 there are six levels in the hierarchy, represented by hydrologic unit codes from 2 to 12 digits long, called regions, subregions, basins, sub-basins, watersheds, and sub-watersheds.