Application of Landsat Imagery to Kaolin Mining Operations in Southeastern US

Introduction In 1977, the US Bureau of Mines awarded a grant to the state of South Carolina to explore the possibility of using Landsat multispectral scanner (MSS) data as an aid in monitoring surface mining activities in selected counties of the state. Work had been done in Aiken County prior to the grant by USBM and state personnel at the EROS Data Center using both the GE Image 100 and ESL IDIMS systems. The initial work on the 1:100 consisted of locating and measuring the areas of various kaolin mines. The results were excellent on location, identification, and accurate area measurements of several mines as compared with aerial photos of each mine. The effort also included locating and measuring the size of all mines (including sand and gravel) within two US Geological Survey (USGS) 7 % - min quadrangle maps. Line printer overlays using the IDIMS equipment and software resulted in the location of all known mines within an estimated error less than zt 91.4 m. Mine acreage was calculated by counting each symbol (which represented one pixel) and multiplying by 1.1 (the number of acres per pixel). [Fig. 1] shows a portion of one quadrangle overlay. [Table 1] gives a comparison of acreage measured for several mines from the 1-100 and IDIMS system vs. aerial photo measurements. The success of the preliminary work resulted in the South Carolina Land Resources Conservation Commission submitting a proposal to USBM for funding to expand the scope of work to other mineral mining operations within the state and determine if Landsat Imagery could be used to accurately locate and measure mining and reclamation activity on a continuing basis. Three separate tasks were proposed for selected kaolin mines in Aiken County: Task 1, to measure changes in active mine developments over the period 1974-77; Task 2, to measure reclamation activity, and, if time permitted, seasonal changes in mine signatures over the same period of time; and Task 3, to classify one complete mining operation (i.e., differentiate between active, spoil, overburden, water, reclaimed portions and surrounding terrain. The major effort was centered around J.M. Hubeis Richardson mine. Mining operations were progressing in a northerly direction and included three areas of reclamation: one prior to 1974; one started during early 1974; and one reclaimed during 1976. A discussion of all work done under the grant is available in the March 1979 Final Report listed under References. Computer compatible tapes (CCTs) containing MSS data from Landsat Path 18, Row 37, were purchased from the National Aeronautics and Space Administration (NASA) covering the period February 1974 through January 1977. The MSS data were processed on the General Electric Image 100 using parallelepiped classification at the EROS Data Center's Data Analysis Laboratory, Sioux Falls, SD, and at the Stanford Remote Sensing Laboratory, Stanford University, Palo Alto, CA, utilizing both an unsupervised classification (ISOMIX) and a supervised classification (sequential discriminant analysis). Studies at Stanford University Remote Sensing Lab (SRSL). In Feburary 1977, initial work was begun at Stanford using the STANSORT computer program running on a DEC PDP-10 (Honey, Prelat, and Lyon, 1974). This software is fully interactive and designed to study Landsat CCT data at high resolution (small areas) on a pixel-by-pixel basis (Lyon, 1977). Unsupervised Classification. The initial step was to locate the Richardson mine on the positive print of the full Landsat scene from CCT 1644-15252, dated 6/17/74. This was relatively easy due to a large abandoned kaolin mine and a long narrow pond clearly visible in the scene. Two unsupervised classifications of the Richardson mine were run using ISOMIX clustering (Honey, Prelat and Lyon, 1974). (ISOMIX essentially follows the interactive clustering procedures of ISOCLS Kan, Holley, and Parker. 1973). Although the classifications were successfully accomplished, it was apparent from the TV display that the results did not clearly define the mine areas. A supervised classification would have to be made in order to clearly separate each portion of the mine. Supervised Classification. This classification was done using the Stepwise Discriminant Program (BMDO7M) which is part of the BIOMED package (Dixon, 1970) and involved the following steps: Training group populations were selected and the digital