Land Use/Land Cover (LULC) Identification for Pascagoula, MS

The second module for GIS4035 Remote Sensing/Photo Interpretation introduces the USGS Land Use/Land Cover (LULC) classification system. Originally compiled by James R. Anderson and associates, A Land Use and Land Cover Classification System for Use with Remote Sensor Data was published by the United States Government Printing Office in 1976. There are four levels in the hierarchy, with Level I categorizing LULC on air photos with small scale and low spatial resolution. As Levels increase, so does the detail, corresponding with increases in spatial and spectral resolution and larger scale.

With additional increases in resolution and scale, LULC Level III further distinguishes features from the broader categories. This can be correlated to analyzing data at the city level as opposed to countywide. The numerical system of LULC Classification starts with the first number of code. The small scale categories for Level I are as follows:

1. Urban or Built-up Land
2. Agricultural Land
3. Rangeland
4. Forest Land
5. Water
6. Wetland
7. Barren Land
8. Tundra
9. Perennial Snow or Ice

Representing a subcategory of Level I, Level II utilizes a second digit. For Urban or Built-up Land, 11 represents Residential areas, 12 Commercial and Services, 13 for Industrial areas, etc. Level III expounds classifications in Level II into more distinct categories. So for LULC 11 for Residential, LULC 111 is Single-family units (single family homes), 112 is Multi-family Units (duplexes, townhomes), 113 is Apartment Buildings.

Generally code information for Level I and II is readily available on the internet, starting with the 1976 Anderson Classification System document. The Modified Anderson LULC Classification used for the USGS National Land Cover Dataset however changes some of the verbiage used in the Level I and Level II classes while introducing an additional code set. This results in some confusion, as determining the final LULC codes, especially for Level III and especially Level IV becomes more tedious.

LULC Classification Codes for Level IV can vary, with some states setting their own code structure. Researching codes for Level III and Level IV revealed some of the differences between sets use for Florida, New Jersey and Oregon. Ultimately it appeared that the New Jersey classification scheme seemed to provide the most detailed Level IV data, which provides codes for discrete land types such as cemeteries or athletic fields for schools, areas that may be visually identified at the city level of an air photo.

The lab for this week visually interprets an air photo of western reaches of Moss Point and Pascagoula along the East Pascagoula River in the Mississippi Gulf Coast. The resolution of the air photo was 16 square feet based upon the Stateplane coordinate system used. Based upon this the scale was set at 1:5,000. However after a good discussion during virtual office hours, the Minimum Mapping Unit (MMU) should have been 2 to 4 times greater than the 16 square foot cell size.

With the MMU selected, consistency should be followed. Since I had already analyzed 100% of the map by the time MMU was better explained, I opted to leave the Level III and IV classification polygons I derived from the larger scale.

Part of my analysis with more detail comes from years of studying aerial photography as a map researcher for Mapsource, Universal Map and AARoads. So it was acknowledged that skill sets for air photo interpretation can vary from individual to individual, and that my level of detail was still acceptable for this project.

With the LULC analysis complete, the next task was ground-truthing collection. Since the area of Jackson County, MS is not readily accessible for the class, imagery from Google Maps Street View (GMSV) and other sources of high-resolution aerial photography supplants the in-situ data collection.

Cross referencing the air photo with the historical imagery slider on Google Earth revealed that the photography was conducted in February 2007. This provided the temporal resolution for the ground-truthing exercise. GMSV went online in 2007, and the bulk of the coverage in Moss Point and Pascagoula dates back to 2008.

The majority of the sampling locations corresponded to readily accessible GMSV imagery. There were a few exceptions where some further interpretation was necessary. As for the sampling selection, bias was introduced due to the fact that around one third of the air photo covers areas of open waters or wetland areas outside of the GMSV range. So the extent used for the "create random points tool" in ArcGIS Pro focused on areas mostly inland. A tolerance was set at 16 feet, to provide a minimum distance between sampling locations.

Attempting to use the error matrix discussed in lecture, the LULC accuracy for the 30 points sampled was 93%. The goal of the exercise was general land use and land cover, and my selection of some discrete land use such as schools and churches, added some error potential to the overall accuracy forumlae.

Remote Sensing - Visual Image Interpretation Basics

A short two days following the completion of the Final Project for GIS4043, I am delving into Photo Interpretation and Remote Sensing (GIS4035). The first lab provides an overview on elements of visual image interpretation, with historical black and white air photos of Pensacola Airport and Pensacola Beach in Northwest Florida.

The first aspect of aerial photography interpretation references the tone, or the shades of gray from light/white to dark/black. Referencing the course textbook Remote Sensing of the Environment - An Earth Resource Perspective, tone is a function of the amount of light reflected. Consequently, the greater the absorption of the incident red light by forest stands results in a darker tone.

Large grassy areas, such as those within the Pensacola Airport grounds or for the runway safety areas, appear on the aerial below with a lighter tones. The soil in Escambia County is very sandy, and sand appears in a light tone. Areas by the airport where grading appeared to be underway at the time appear with a very light tone.

Texture is defined in the course textbook as the characteristic placement and arrangement of repetitions of tone or color in an image. With aerial photography, texture aids in identifying land areas populated by similar groups of objects. The definitions of texture range from fine/smooth, where an area is uniform or homogeneous, to intermediate/mottled, and rough/coarse where the contents of an area are heterogeneous.

Some of the examples identified in the Pensacola aerial included fine areas of smooth surface water in Escambia Bay and swatches of flat grassland. Texture increases with variation on the ground cover, such as areas within Pensacola Airport, to coarse areas of timber land located toward the bay front. The roughest areas of texture include subdivisions with the mixture of house footprints and tree canopies.

Next to consider when it comes to identifying features on an air photo are aspects of shape, size, pattern, shadows and association. Shape can be a dead give away in some instances, such as the Pensacola Beach fishing pier (one long since replaced due to hurricanes), with its linear appearance on the following aerial.

Shadows often provide insight into what an object may be, such as the Pensacola Beach Water Tower. Looking closer, smaller objects are indefinable based upon their shadow, such as palm trees because of the distinct shape of their fronds.

Like many things in life, appearances on the ground often result in a pattern, or a series of patterns. Striping for a parking lot creates a pattern of linear or angled spaces. A subdivision usually has some uniformity in the placement of houses and their orientation to each street.

Depending upon the area and prior knowledge, a more difficult element of visual interpretation is association. Association is highly variable and references the related surrounding of an object or activity.

Located north of the water tower, the association of the two linear buildings, adjacent parking areas and a swimming pool in between conveys that collectively the site is a motel. A likely restaurant is identified at the north end of the aerial photo based upon the association with the large parking lot and assorted vegetation immediately surrounding the building.

Lastly for this week, we make a comparison of a True Color aerial photograph and False Color or Near Infrared (NIR) aerial photograph.

The True Color imagery of the University of West Florida campus and points north along the Escambia River shows the landscape under natural light, or what is visible with the naked eye. False Color is sensitive to near infrared and shows areas where more infrared energy is reflected with shades of red. By separating green, red and NIR bands, and applying a unique color for each, this allows one to more readily distinguish types of vegetation.