Introduction | ConclusionsMethodology
| Acknowledgements | Results & Discussion
| References
| |
INTRODUCTION
The High Plains region of the United States is characterized by
relatively flat topography, a subhumid climate, and shortgrass
prairie--see Figure 1. One of
the most distinguishing features of the High Plains
is that much of it is underlain by the High Plains aquifer, a vast
underground reservoir that has transformed the High Plains from
a grazing and dryland farming economy to one where irrigated
agriculture plays a substantial role (Kromm and White, 1990, 1992).
Because of the region's sensitivity to changes in groundwater,
climate, and economic and policy factors, it is essential to
both inventory and monitor the nature of its land use. The
purpose of this study was to develop a reliable, repeatable,
and economically feasible protocol for mapping land use/land
cover (LULC) within one of the most intensively developed and
economically significant areas of the High Plains region --
Finney County in southwestern Kansas.
There have been a number of large-area LULC mapping projects for regional and statewide areas, primarily using single-date imagery. These include, for example, statewide maps for South Dakota (Tessar et al., 1975), Ohio (Baldridge et al., 1975), Alaska (Fitzpatrick-Lins et al., 1987), Maryland (EOSAT, 1992), and Georgia (ERDAS, 1992). Nearly all of the land cover maps being produced for the National Biological Service GAP project were, or are being, produced using a single-date classification approach. The use of single-data imagery, while cost-effective from an image acquisition standpoint, necessitates making compromises in thematic detail and classification accuracy (Whistler et al., 1995).
Studies using multi-date satellite data to map LULC, although less common, in most cases have reported improved classification accuracies over single-date techniques (Mergeson, 1981; Hill and Megier, 1986; Mauser, 1989). The State of South Carolina Land Resources Conservation Commission developed a detailed land cover map for 19 land cover classes using leaf-on and leaf-off TM imagery (EOSAT, 1994). Fuller et al. (1994) created a land cover map for Great Britain using bi-temporal (summer and winter) scenes. Landsat TM bands 3 (red), 4 (near infrared), and 5 (middle infrared) were combined from summer and winter scenes, resulting in 6-band images that were then submitted to a supervised classification approach that used a maximum likelihood classifier in an iterative process. The authors reported the combined summer-winter scenes offered substantial improvement over single-date classifications.
In the Kansas Land Cover Mapping Project recently completed by the Kansas Applied Remote Sensing Program (Whistler et al., 1995), the state of Kansas was mapped for land cover, county by county, using computer classified single-date Thematic Mapper imagery. Although high accuracies were achieved for the project (>85%), it was found that single-date imagery was limited in the number of cover types that could be identified reliably. For example, in Finney County, grassland could not be discriminated from croplands better than 70% of the time using a single-date automated classification approach. Using the single-date approach, a substantial amount of manual digitizing was required in this county before a minimum classification accuracy of 85% was achieved. It was hypothesized for the current project that the use of multi-date satellite imagery would increase classification accuracies, reduce the manual input required to produce an accurate map, and increase the number of mappable classes.
The specific goals for this study were to develop an improved method for discrimination between croplands and grasslands, and develop the methods for mapping individual crop types and U.S. Conservation Reserve Program (CRP) lands.
Cloud-free multi-temporal Landsat TM images (Path 30 / Row 34)
were obtained from the EROS Data Center under NASA's Global
Climate Change Program for three years: 1987, 1989, and 1992.
Images for each year were acquired for three times during the
growing season (April/May, July, and September) (Table 1). The
ground resolution of TM data is approximately 30 x 30 m (0.09
ha) per picture element (pixel). The spectral data were
converted to radiance values and the optimal index factor (OIF)
described by Jensen (1996) was used to select the least
inter-correlated bands with the greatest variance. Bands 3, 4,
5, and 7 were selected because they were consistently ranked as
having the highest OIF values. Subscenes of Finney County were
extracted, registered to each other, and transformed to a
Universal Transverse Mercator (UTM) projection (30 x 30 m pixel
size) using a nearest neighbor resampling algorithm (rms error
< 0.5 pixels). the data were next adjusted for atmospheric
scatter using the improved dark object subtraction method
developed by chavez (1988).
The crop types, for randomly selected fields, were determined
from the records of the Finney County Farm Service Agency (FSA
- formerly the U.S. Agricultural Stabilization and Conservation
Service - ASCS). This information was used to identify field
training sites for five major crop and land cover types (winter
wheat, grain sorghum (milo), corn, alfalfa, and fallowed
lands). The training sites were located on the TM imagery as
it was displayed on a computer monitor. The UTM coordinates
defining the outer boundaries of each site were extracted
through a process called "heads-up digitizing," which involves
the use of a computer mouse to delineate the boundaries on the
displayed TM image. The summary statistics (minimum, maximum,
mean, and standard deviation) for all the pixels within the
training site of each cover type were generated. Using a
maximum likelihood classifier, all the pixels, except those
falling in grassland areas, were classified as one of the five
cover types listed above. The number of training sites used
for each cover class was proportional to the percentage cover
represented by each land cover category. Approximately one
third of the training sites that were not used to generate the
classification statistics were later used to assess
classification accuracy.
Modeling Conservation Reserve Program Lands The CRP represents
one of the most profound changes in LULC in southwest Kansas
(and in many other regions of the United States). Initiated in
1985, one of the major objectives of the CRP was to decrease
crop production and plant croplands to alternative cover types,
usually native grasses, for the primary purpose of reducing
soil erosion. Over 98% of the CRP contracts were issued to
farmers between 1986 and 1991 (FSA, 1994). Southwest Kansas
experienced a high degree of CRP participation, with some
counties enrolling upwards of 20-25% of their total acreage in
CRP (Bair, 1991). Finney County farmers, for example, have
enrolled approximately 23,877 ha (59,000 acres) in CRP. Since
our first TM dataset was for 1987, and our latest was for 1992
(6 growing seasons), we were able to use a post classification
change detection approach (Jensen, 1996) to identify lands that
were converted from cropland to grassland during this 6-year
period. From field observations and conversations with FSA
Officials, we determined that such a land cover change was most
often associated with the CRP (Egbert et al., in review; Nellis
et al. 1996).
The classification results were compared to field crop type
data provided by the Finney County FSA. Verification sites were
randomly selected from all geographic areas within the county
and represented about a 5% sample of the total area. Within
the grassland areas, verification sites were visually
identified on Natural Resource Conservation Service (NRCS -
formally the Soil Conservation Service) 35 mm natural color
aerial photographs.
METHODOLOGY
Mapping Grasslands and Crop Types In Finney County, the land
cover types of primary concern are cropland and grassland,
which together comprise approximately 98% of the county's area
(76% crop and 22% grassland, as of 1989) (Whistler et al.,
1995). Water and woodland, although present, represent a very
small component in the landscape and were classified
separately. Although cropland and grassland are relatively
coarse classes, due to similar spectral characteristics they
proved difficult to separate using the July 1989 image which
was used in the Kansas Land Cover Mapping Project. Two
particularly intractable problems were the confusion of
subirrigated riparian grasslands with growing crops and weedy
fallow fields with grassland (Whistler et al., 1995).
1987 1989 1992
09 May 87 28 April 89 06 May 92
28 July 87 01 July 89 25 July 92
30 Sept. 87 19 Sept. 89 27 Sept. 92
RESULTS AND DISCUSSION
Prior to this study, overall classification accuracy of
single-date imagery for grassland and cropland in Finney County
was 70%. A comparison between the multi-date classification
and the FSA field data show percentage agreement ranging from
92.0% to 99.5%, with an average agreement of 96.7% (Table 2).
The map for crop type and land cover that was produced through
the classification of the 1992 multi-date approach is shown in
Figure 2. A comparison between crop types
and FSA field data show percentage agreement ranging from 82.5% to
99.2%, with an average agreement of 90.5% (Table 3).
Classification Results
Cover Type 1987 1989 1992
Crop 99.3 99.2 99.5
Grassland 93.1 92.0 97.3
Crop Type 1987 1989 1992
Wheat 90.5 90.0 99.2
Sorghum 84.6 90.8 90.0
Corn 94.3 90.2 84.2
Alfalfa 87.2 99.0 92.8
Fallow 82.5 93.2 88.6
Areal comparisons showed little change in wheat, grain sorghum (milo), corn, and alfalfa between 1987 and 1992--see Figure 5. As expected, there was a marked increase in the area of grassland due to CRP. This figure shows that the increase in grassland is offset by a comparable decrease in fallowed lands. The data show that between 1987 and 1992, Finney County farmers reduced the lands in fallow by about 36,400 ha (90,000 acres). This change in land use affected over 10% of the total study area, yet the personnel working with the Natural Resource Conservation Service and Farm Service Agency in Finney County could provide no explanation for the dramatic change.
Given our new ability to accurately classify land cover, some questions that might be asked include: (1) how is land use/land cover changing in southwest Kansas, (2) can crop yield models be more effectively extended over larger areas, (3) can irrigated and non-irrigated croplands be distinguished accurately (some preliminary findings suggest that such discrimination is possible), (4) can land cover change be modeled in relationship to factors such as groundwater depletion, government agricultural policy, economic factors, and climate variation, and (5) how is wildlife habitat affected by changes in land cover and government policies that affect land use?
With the majority of 10-year CRP contracts expiring between 1995 and 1999, there is an urgent need to evaluate the status and success of this program in ways that were not possible before the location of these lands could be accurately mapped and evaluated in the context of their surrounding environment. The necessity to rapidly evaluate current CRP lands implies that remote sensing mapping techniques, such as the one described here with multi-seasonal imagery, may be essential tools in the hands of conservationists and agricultural policy planners.
From these results, we learned that in Finney County, the CRP
did not reduce crop production, and that major changes in land
use did take place within the time frame of this study. We
also learned that even major changes in land use may not be
detected using existing land use inventory methods.
Preliminary findings from a similar study in northwestern
Kansas has shown the multi-date analysis approach can be
extended to other areas within the High Plains. The mapping
accuracy in northwestern Kansas exceeded 90% for nine cover
types which included: alfalfa, cane sorghum, milo, oats,
soybeans, sunflowers, winter wheat, fallow, and grasslands.
Methods we are now developing also show promise for
discriminating irrigated from nonirrigated agriculture by crop
type. Similar methodologies as described above are now being
adapted to nonagricultural lands in Kansas as well. By the end
of 1999, a vegetation alliance level (similar to plant
community level) map will be completed for the State of
Kansas. Preliminary findings from this project again suggest
that the multi- date analysis approach will significantly
improve our ability to discriminate among natural vegetation
types of Kansas.
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CONCLUSIONS
Classification accuracy of land cover types and the level of
classification detail were substantially improved by using the
multi-date dataset derived from Landsat TM imagery. The
multi-date approach improved our ability to discriminate
between grassland and cropland by over 25%. We found over 90%
agreement between the multi-date crop type classification and
the crop types reported by FSA. A question that remains
unanswered is which estimate is correct when there are
disagreements between the multi-date classification and the FSA
information. To our knowledge, this study produced the first
automated classification of CRP lands. This demonstrates the
ability of satellite remotely sensed data to identify lands
under different management practices, which is critical to a
better understanding such things as: land use, vegetation
productivity, biodiversity and changing quality of wildlife
habitat, CO2 gas flux, and potential impacts of such changes
on the social and economic conditions of a region.ACKNOWLEDGMENTS
The authors wish to thank the National Aeronautical and Space
Administration for financial support provided by a grant
through the Remote Sensing Applications section of the Mission
to Planet Earth program (NAGW 3810). We also thank Brad
Rundquist, Rich Lissitschenko, and Eric White at Kansas State
University for their work in acquiring ground truth data for
training sites and accuracy assessment. The personnel working
for the: Finney County Farm Service Agency, Groundwater
Management District #3, Natural Resource Conservation Service,
and Kansas State Agricultural Experiment Station were most
helpful.REFERENCES