Editorial Manager(tm) for Natural Hazards Manuscript Draft Manuscript Number: NHAZ405R2 Title: LANDUSE CHANGE IN UPPER KANSAS RIVER FLOODPLAIN: FOLLOWING THE 1993 FLOOD Article Type: Manuscript Keywords: Kansas River, flood, landuse change, satellite images. Corresponding Author: Assis. Prof Mahnaz Gumrukcuoglu, PhD Corresponding Author's Institution: Sakarya University First Author: Mahnaz Gumrukcuoglu, Assis. Prof. (PhD) Order of Authors: Mahnaz Gumrukcuoglu, Assis. Prof. (PhD); Doug Goodin, Professor; Charles Martin, Associate Professor Abstract: ABSTRACT The last major flood event occurred in 1993 that have caused great damage in the Kansas River on USA. The purpose of this study is to document land use changes that occurred along the Kansas River following the 1993 floods. Patterns of land use before and after the 1993 flood are compared using Landsat Thematic Mapper satellite images from 1988, 1991, 1993, 1996 and 1998. Results show a changing in agricultural land in riparian areas following the 1993 flooding event. As a result of this study is that agricultural land, grassland, woodland and reduced during the 1993 flood. The flood also resulted in permanent loss of agricultural land and production. Following the flood, grassland returned almost to pre-1993 levels. This study provides useful description of a river flood by using remote sensing methods and can show a way to decision-makers for mitigation of flood effects and future river management plans. Response to Reviewers:
Manuscript Click here to download Manuscript: Manuscript.revised.doc
Click here to view linked References
LANDUSE CHANGE IN UPPER KANSAS RIVER FLOODPLAIN: FOLLOWING THE 1993 FLOOD
M. Gumrukcuoglu1, D. Goodin2, C. Martin3 1 2, 3
Sakarya University, Environmental Engineering Department, 54187, Sakarya, Turkey Kansas State University, Department of Geography,118 Seaton Hall, Manhattan, KS 66506-2904, USA
Corresponding Author: Mahnaz Gümrükçüoglu Sakarya University, Environmental Eng. Department, 54187, Sakarya, Turkey Phone: #90 264 2955637, Fax: #90 264 2955600 Email:
[email protected],
[email protected]
1
ABSTRACT The last major flood event occurred in 1993 that have caused great damage in the Kansas River on USA. The purpose of this study is to document land use changes that occurred along the Kansas River following the 1993 floods. Patterns of land use before and after the 1993 flood are compared using Landsat Thematic Mapper satellite images from 1988, 1991, 1993, 1996 and 1998. Results show a changing in agricultural land in riparian areas following the 1993 flooding event. As a result of this study is that agricultural land, grassland, woodland and reduced during the 1993 flood. The flood also resulted in permanent loss of agricultural land and production. Following the flood, grassland returned almost to pre-1993 levels. This study provides useful description of a river flood by using remote sensing methods and can show a way to decision-makers for mitigation of flood effects and future river management plans.
Key Words: Kansas River, flood, landuse change, satellite images.
1. INTRODUCTION
Flood is one of the most severe natural disasters that greatly harm economic and social development and that profoundly affects the life and welfare of people. Floods are serious in that they often degrade floodplain land, resulting in loss of agricultural productivity, usable land due to channel migration, and ultimately to changes in land use. There have been many flood occurrences that caused great damage in the last 100 years along the Kansas River. The 1951 flood was, without question, the most significant flood on record in terms of extent and area affected (Kolva, 2001). The flood of March of 1960 was unique in that it was caused primarily by snowmelt. The weather patterns experienced during June 1967 created a flood similar to those that caused the record flood of 1951. In 1973, heavy precipitation throughout the Kansas River basin caused flooding in October (Kolva 2001). The Great Midwest Flood of 1993 was the most devastating flood in modern United States history. This flood was unique not only because of the record high water levels and flows and the wide inundated area extent, but also because of the long duration of flooding. Significant flooding in the Upper Mississippi River Basin began in mid-June and persisted into early August 1993. The long period of inundation had significant 2
effects on agricultural land and wetlands. The economic damage was near $20 billion and more than 50 000 homes were damaged or destroyed. At least 38 people lost their lives as a result of this extreme flood. The area extent of this flooding included southern Minnesota, southwestern Wisconsin, Iowa, western Illinois, northern Missouri, southern North Dakota, and eastern parts of South Dakota, Nebraska and Kansas (Kolva 2001). The long period of inundation had significant effects on agricultural land and wetlands. Loss of land in the river valley is not only a financial loss to the owner, but also a significant loss of natural resources (Brady et al, 1998). Therefore this particular event has been selected for this study.
There are many studies about floods on the Kansas River and elsewhere. Sandholt et al. (2003) used SAR images to estimate flood area at the height of the flood event, and estimate crop loss resulting from flooding. A similar study was conducted by Wang et al (2004) who used remote sensing for monitoring the whole development of the flood disaster, from the beginning of the flood to the recovering after the disaster. They totally produced 23 reports of the flood remote sensing monitoring results from MODIS data, RADARSAT-1 data and high-resolution airborne radar data. They used these data to evaluate the efforts of the flood diversion storage in their study area. Another study of river flooding in south Asia sought to identify the rural settlements that are vulnerable floods (Sanyal and Lu, 2005). In a separate study, Sanyal and Lu (2004) used remote sensing and GIS in flood management with particular focus on the developing countries of Asia. Werle et al have studied about flood and coastal zone monitoring in Bangladesh with Radarsat ScanSAR. In their article, they discuss ScanSAR in the context of recent satellite radar work in Bangladesh. Then they outline potential applications of wide swath SAR from a resource management and emergency response point of view.
This study focuses on the effect of the 1993 flooding on agricultural land, grassland, woodland in the floodplain of the Kansas River. Since agriculture is a very important activity in the river valley, we emphasize loss of agricultural land. The study area encompasses a reach of the river beginning at the point where the Smoky Hill and Republican Rivers join to form the Kansas River to the eastern boundary of Riley County. We used Landsat Thematic Mapper (TM) images from 1988, 1991, 1993, 1996 and 1998 to classify land use before and after the 1993 flood event. Digital change detection techniques were used to assess and analyze the extent and geographic pattern of change. The major change in landuse took place in grassland during the flood year. 3
The results of the work demonstrated that flood is important for landuse changes. The other studies focused on flood estimation or monitoring, flood plain geomorphology, and hydrology, channel migration and crop loss. Instead of conventional approach to flood damage estimation by using different remote sensing methods, our study seeks with observations and comments about landuse change after the great flood in adapting standard remote sensing methods. The combination of inundation demages and landuse change information obtained by using remote sensing methods provides a complete and useful description of a river flood for landuse and river management.
2. MATERIALS AND METHODS
2.1 Study Area The study area encompasses a reach of the river beginning at the point where the Smoky Hill and Republican Rivers join to form the Kansas River to the eastern boundary of Riley County, and includes portions of the river flood plain in Geary, Riley, and Pottawatomie Counties (Figure 1).
(Figure 1)
The Kansas River watershed includes 155000 km2, with 89155 km2 of this area located in northern Kansas. The river flows eastward through northeastern Kansas and joins the Missouri River at Kansas City, a total distance of 272 km. The elevation of the river basin ranges from about 220m above sea level to the highest point in the river corridor in southeast Riley County at 465m above sea level (Brady et al, 1998).
The Kansas River is a meandering river with a wide flood plain and a sand substrate. Channel migration of rivers has damaged or is threatening to damage public conveyance systems such as roads, railroads, bridges and pipelines crossing farmlands (Figure 2) (USACE 1978). Significant River channel migration has primarily been a response to flood events that have occurred on the river. Mapped historical channel migration in the Kansas
4
River Basin study, directed by the University of Kansas, focused on the bank stabilization of the Kansas River channel and its significant tributaries for the period of 1857-1976 (Weatherly, 1994).
(Figure 2)
The Kansas River basin has a continental climate typical of the central Great Plains. The winters are usually cold and summers are long and hot. The climatic record of the basin documents intense and prolonged rainfall during some years. For instance in 1993, precipitation for the period January-July was more than 52 cm in most of the flooded area and more than 104 cm in parts of northeastern Kansas (Kolva, 2001).
The soils in valleys along creeks and rivers are deep and generally fertile. Flood plains of the Kansas River are underlain by alluvium (Sorenson et al, 1987). The main concern of management on these soils is the maintenance of fertility and good soil structure. The soils in broken, hilly areas are shallow and moderately deep. The alluvium consists of clay, silt, sand and gravel, the character and proportions of which differ from one place to another.
The topography of the study area may be divided into three types: the high uplands or prairies, the creek and river valleys, including the alluvial floors and terraces, and the broken, hilly country extending from the borders of the uplands downward to the valley floors or terraces, where present. The floor of the Kansas River valley is generally flat and bordered on its sides in most places by steep bluffs. The highest bluffs occur just southeast of Manhattan at Fremont point (Sorenson et al, 1987).
The location of the Kansas River basin within the interior central plain encourages the growth of prairie grasses. The native short grasses (grama and buffalo grasses) are found in the western portion of the basin. Tall grass (blue stem) is typical in the east. It should be noted that, except for the extreme eastern portion of the state, forests are located almost exclusively in the flood plain of the Kansas River. The bottomlands of the Kansas River valley were once too forested although the species composition differed from the uplands. Today some 5
downstream parts are covered with forest, but large portions of the area have been converted to agricultural land (Sorenson et al, 1987). In the Fort Riley area north of the Kansas River, the larger tributary valleys are filled with woodlands, whereas on privately owned land south of the river tributary valleys are mostly agricultural land (Brady et al, 1998).
The Kansas River from Junction City to Topeka has experienced changes in the land use over the past fifty years. Land use has changed from primarily agricultural to extensively urban in many areas. Geary County has a more steady decrease in harvested agricultural land. Grassland use has decreased only a little. Pottawatomie County has also remained fairly stable in its landuse. Riley County has experienced urban growth, which created a steady decrease in grassland and harvested agricultural land (Messbarger et al, 1999).
2.2 Image Analysis and Change Detection 2.2.1 Preprocessing Land cover change along the Kansas River over the period 1988-1998 was analyzed using five summertime Landsat TM images. Although exact annual dates were not available, the dates of the five images were chosen to correspond as nearly as possible. Image of 1988 belongs to August 01st, 1991 to August 26th, 1993 to August 15th, 1996 to August 21, 1998 to August 29th. Selected images were cloud free (cloud coverage< 10%).
Image geometry was corrected using a combination of rectification and image-to-image registration. The 1993 image was rectified and using control points selected from a USGS 1:100,000 topographic quadrangle of the study area. Coordinates of the selected ground control points were determined in-situ using a Lowrance Nav 12 GPS receiver. The image was then rectified to the UTM coordinate system via a first-order transformation (RMS < 1.0) with nearest neighbor resembling (Jensen, 1996). The remaining 5 images were then registered to the August, 1993 image (all RMS < 1.0).
6
Following rectification, raw digital numbers for each image were corrected for atmospheric backscatter using the IDOS method (Song, et al., 2001, Chavez, 1988). The corrected data were then calibrated to radiance values using the method of Robinove (1982): RADij = (DNij/DNmaxj) * (Lmaxj - Lminj) + Lminj
(1)
Where; RADij = radiance in pixel i and band j (mW/cm2/sr), Lmaxj and Lminj = sensor-specific maximum and minimum radiance values in band j (mW/cm2/sr), DNij = “raw” digital number in pixel i and band j., DNmaxj = sensor specific dynamic range band j.
Sensor-specific calibration values were obtained from Markham and Barker (1986). The radiance-corrected images were then cross-calibrated using the runways of the Manhattan Municipal Airport as a pseudo invariant feature. Nine non-mixed pixels were extracted from the runways and used to adjust in-band radiance values for each image (Schott et al., 1988).
2.2.2 Classification and Change Detection Unsupervised classification of the imagery was done using the ISODATA algorithm with maximum likelihood decision rule (Richards, 1993). The 30 spectral clusters resulting from ISODATA were interactively labeled into one of five classes; agricultural, grassland, water, non-vegetated, woodland. Clusters that could not easily be sorted into any of these land cover classes were evaluated using a “cluster-busting” technique (Jensen, 1996). Using this technique, “good” clusters (ones for which class membership confidence is high) were masked from the image. The remaining unsorted pixels were then reclustered using ISODATA. These new clusters were evaluated and labeled as before.
7
Because these analyses were done using archived data, conventional assessment of classification accuracy was not possible due to lack of suitable reference data. However, some idea of classification accuracy was gained by comparison of these results to the Kansas Land Cover Mapping Project (KLCMP) (Whistler et al., 1995). The KLCMP land cover maps were derived from 1989 Landsat TM data, thus they are not directly compatible with any of the image dates used here. However, the 1998 classified image agrees with the KLCMP map with 83% accuracy. Based on the accuracy of the 1988 image and the classifications scheme, we are confident that are classifications are accurate.
2.2.3 Change Analysis Land cover change was analyzed by extracting from the entire image data set and generating change matrices for the time period between each image pair, and for the study area as a whole. These change matrices showed categorical shifts between each time period. Categorical shifts by cover type are summarized in Figures 7. Change images were also generated to visually show where change occurred (Figure 3,4,5,6).
3. RESULTS
The study area was classified as agriculture, grassland, woodland, non-vegetated and water. Total classified land was 138995.96 hectares in 1988, 104063.04 hectares in 1998; agricultural lands accounted for 10000.58 hectares of these figures. Agricultural land was smaller in 1988 because of a dry period; however it has widened a little after 1988 (Figure 3). 1196 hectares of agricultural land remained underwater in between 1991-1993 and 546 hectares of agricultural land were converted to grassland (Table1 and Figure 4). In 1993, the floodplain was inundated, temporarily becoming a wetland area and causing permanent damage to the surface. Agricultural land was 1926.05 hectares in the classified areas during the 1993 flood period, a reduction of 6622.91 hectares compared to the preflood classification period. After the flood, especially in flood plain, large areas were converted to agricultural land as before of flood. Agricultural land increased in 1996 and 270 hectares of water land were again converted to agricultural land and also 81 hectares woodland were converted to agricultural land
8
(Table 1and Figure 5). The agricultural lands expand according to previous years in 1998 (Figure 6). It can be say that it is good for development of agriculture in study area.
Grassland comprised 102257.50 hectares of that total in 1988. Area of grassland decreased markedly in the flood year 1993, down to 62672.70 hectares. This was the lowest total for any of the years. The majority of the grassland ‘lost’ in 1993 was in fact covered with water. Following the flood in 1996 grassland comprised 105064.90 hectares of the total classified land. It is notable that in the period following the flood, 923 hectares of former grassland remained covered with water. This was the one of big changes about water classification (Table 1). If 1988 and 1991 are compared, only 106 hectares of grassland were covered with water but not only water was converted to agricultural land but also 914 hectares grassland converted to agricultural land (Figure 3). 190 hectares woodland were converted to grassland between 1993 and 1996 (Figure 5). If the 1991-1993 year’s changes are compared, 557 hectares woodland were converted with water (Table1 and Figure 4).
(Table1) (Figure 3) (Figure 4) (Figure 5) (Figure 6)
In 1993 much of the study area was covered with water. Comparison of the flood period with preflood imagery shows that 557 hectares of woodland, 923 ha of grassland, 1013 ha of nonvegetated land and 1196 ha of agricultural land were inundated due to flooding. After the flood, 284 hectares of temporarily water-covered land returned to woodland, 2016 hectares to grassland, 571 hectares land to nonvegetated land (Table 1 and Figure 5).
9
4. DISCUSSION
It is apparent that some areas of the flood plain showed notable land cover change before and after the flooding episode. The inundated areas quite probably represent a permanent change in the land surface resulting from the flood. Because of the flood, especially old river bed covered water that it caused to reduce of grassland for temporally when it compared to other years. Agricultural lands have decreased during the flood in 1993. But after the flood was over, especially in the flood plain, the land was again converted to agricultural land to reach its pre- flood status. More serious loss due to flood occurred in grasslands. The flood covered 923 hectares of grassland. This was the major change in land classification during the flood year. Non-vegetated areas have expanded proportionally ratio in 1993 due to flood and especially in 1998 (Figure 7). However these are decreased in an important ratio infavor of agricultural lands and woodlands. The results of this study demonstrated that floods played a very important role in landuse changes. Landuse changes monitoring along the river could be very useful in supporting the development of a long-term national river management plan. The monitoring of landuse changes by using remote sensing technique could provide to determinate of measures.
(Figure 7)
Results of this analysis can present to suggest methods for preventing or mitigating flood impacts in the future. Changes in the Kansas River Basin over the past century include the addition of flood control structures, changes in land use, and human encroachment into the flood plain. Flood control structures decrease peak flow levels, thus reducing the likelihood of flooding. Reservoirs are very important in the prevention of flood damages along the Kansas River. After the construction of the reservoir the peak flows leveled off. Artificial structures such as levees and dikes do not allow streams to access the floodplain during major flow events (USDA, 2001). The flood-control reservoirs and the levee system working in concert protected Junction City, Manhattan, Topeka, Lawrence, and Kansas City; without the levees, these cities would have been flooded (Perry, 1993). Wetlands commonly mitigate the effects of floods. Wetland areas can be filled with and temporarily store floodwaters so that flood effects on agricultural and residential areas are lessened. Although restoration of
10
upland and bottom land wetlands can reduce flood stages in more frequent floods (25 years and less), it is questionable whether they would have significantly altered the 1993 flood conditions (Kolva, 2001).
5. CONCLUSION
This paper discussed the effects of 1993 flood on agriculture, grassland, woodland, water land and nonvegetated land based on the comparison of satellite images taken before and after the flood year. The main goal of the study is landuse change monitoring and comments after the great flood by using standard remote sensing methods. Five Landsat TM images were analyzed for land cover change over the period 1988-1998. Landuse change was analyzed from the image data set and generating change matrices. We have determined the risk areas using these images analysis with the goal of monitoring the ongoing changes in floodplain. Large woodland, agricultural land, grassland and nonvegatated land converted to water land during the flood period. Land damage was due to the destruction of agricultural land and grassland area, the latter can be permanent. The only upside of floods is that they increase the fertilization in land after the flood. From this study, we can draw the conclusion that 1993 flood can play important roles in landuse changes in Kansas River. This study can be use as base for other watershed studies and show the way to local administrator by using time series analysis in remote sensing techniques for river and natural disaster management on floodplain for future.
6. REFERENCES
Brady, L.L., Grisafe, D.A., McCauley, J.R., Ohlmacher,G.C., Quinodoz, H.A.M., Nelson, K.A., 1998, “The Kansas River Corridor-Its Geologic Setting, Land Use, Economic Geology and Hydrology”, Kansas Geological Survey.
Chavez, P.S. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment. 24:459-479.
Department of Army Kansas City District Corps of Engineers (USACE), 1978, “Kansas River Bank Stabilization Study”.
Jensen, J. 1996. Digital image processing: a remote sensing perspective. Prentice-Hall, New York.
Kolva, J.R., 2001, “Effects of the Great Midwest Flood of 1993 on Wetlands”, National Water Summery on Wetland Resources, United States Geological Survey (USGS) Water Supply paper 2425.
11
Perry, C.A, 1993, Effects of Reservoirs On Flood Discharges In The Kansas And The Missouri River Basins, U.S. Geological Survey Circular, 1120-E.
Richards, J.A. 1994. Remote Sensing and Digital Image Interpretation: an Introduction. SpringerVerlag, Berlin.
Sandholt, I., Fog, B., Fensholt, R., 2003. Flood Monitoring the Senegal River Valley: First result based on SAR PRI data, Danish Journal of Geography. 1:103.
Sanyal, J., Lu, X.X., 2004. Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A review. National Hazards. 33:283-301.
Sanyal, J., Lu, X.X., 2005. Remote Sensing and GIS-based Flood Vulnerability Assessment of Human Settlements: A case study of Gangetic West Bengal, India. Hydrological Procecess.19:3699-3716.
Schott, J.R., Salvaggio, C., Volcheck, W.J. 1988. Radiometric Scene Normalization Using Pseudo Invariant Features. Remote Sensing of Environment. 26:1-16.
Sorenson, C.J., Johnson, Q.C., Dort, W., 1987. “Quaternary Environments of Kansas”, Guide Book Series 5, Lawrence, Kansas.
Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., and Macomber, S.A. 2001. Classification and change detection with Landsat TM data: when and how to correct atmospheric effects? Remote Sensing of Environment. 75:230-244.
USDA (U.S. Department of Agriculture) 2001. The Kansas State Conservation Commission “Kansas River and Stream Corridor Management Guide”, Kansas.
Wang, S., Chen, S., Zhou, Y., Zhao, Q., 2004. Assessing the Efforts of the Flood Diversion and Storage in the Drainage Area of Huaihe River Using Remote Sensing. IEEE. 0-7803-8742-2/04.
Werle, D., Martin, T.C., Hasan, K., 2000. Flood and Coastal Zone Monitoring in Bangladesh with Radarsat ScanSAR: Technical Experience and Institutional Challenges. 148 Johns Hopkins Apl. Technical Digest, Volume 21, No.1.
Whistler, J.L, Egbert, S.L., Jakubauskas, M.E., Martinko, E.A., Baumgartner, D.W., and Lee, R-Y. 1995. The Kansas land cover mapping project: regional scale land use/land cover mapping using Landsat Thematic Mapper Data. Proceedings, ASPRS/ACSM Annual Meeting and Exposition, Charlotte, N.C. 3:773-785.
Messbarger, M.S., Mitchell, J.M., Peterie, M.L., 1999, “20th Century Environmental History of the Kansas River, Junction City to Topeka”, Kansas State University, Department of Geography, Student Final Project, Submitted to Dr. C. Martin.
12
Weatherly, J.J.,1994. “Historical Channel Adjustments of the Lower Big Blue River Below Tuttle Creek Reservoir Manhattan, Kansas, 1857-1991”, Master Thesis, Kansas State University, Department of Geography.
13
Manuscript Click here to download Manuscript: Manuscript.revised[2].doc
Click here to view linked References
LANDUSE CHANGE IN UPPER KANSAS RIVER FLOODPLAIN: FOLLOWING THE 1993 FLOOD
M. Gumrukcuoglu1, D. Goodin2, C. Martin3 1 2, 3
Sakarya University, Environmental Engineering Department, 54187, Sakarya, Turkey Kansas State University, Department of Geography,118 Seaton Hall, Manhattan, KS 66506-2904, USA
Corresponding Author: Mahnaz Gümrükçüoglu Sakarya University, Environmental Eng. Department, 54187, Sakarya, Turkey Phone: #90 264 2955637, Fax: #90 264 2955600 Email:
[email protected],
[email protected]
1
ABSTRACT
The last major flood event occurred in 1993 that have caused great damage in the Kansas River on USA. The purpose of this study is to document land use changes that occurred along the Kansas River following the 1993 floods. Patterns of land use before and after the 1993 flood are compared using Landsat Thematic Mapper satellite images from 1988, 1991, 1993, 1996 and 1998. Results show a changing in agricultural land in riparian areas following the 1993 flooding event. As a result of this study is that agricultural land, grassland, woodland and reduced during the 1993 flood. The flood also resulted in permanent loss of agricultural land and production. Following the flood, grassland returned almost to pre-1993 levels. This study provides useful description of a river flood by using remote sensing methods and can show a way to decision-makers for mitigation of flood effects and future river management plans.
Key Words: Kansas River, flood, landuse change, satellite images.
1. INTRODUCTION
Flood is one of the most severe natural disasters that greatly harm economic and social development and that profoundly affects the life and welfare of people. Floods are serious in that they often degrade floodplain land, resulting in loss of agricultural productivity, usable land due to channel migration, and ultimately to changes in land use. There have been many flood occurrences that caused great damage in the last 100 years along the Kansas River. The 1951 flood was, without question, the most significant flood on record in terms of extent and area affected (Kolva, 2001). The flood of March of 1960 was unique in that it was caused primarily by snowmelt. The weather patterns experienced during June 1967 created a flood similar to those that caused the record flood of 1951. In 1973, heavy precipitation throughout the Kansas River basin caused flooding in October (Kolva 2001). The Great Midwest Flood of 1993 was the most devastating flood in modern United States history. This flood was unique not only because of the record high water levels and flows and the wide inundated area extent, but also because of the long duration of flooding. Significant flooding in the Upper Mississippi River Basin began in mid-June and persisted into early August 1993. The long period of inundation had significant 2
effects on agricultural land and wetlands. The economic damage was near $20 billion and more than 50 000 homes were damaged or destroyed. At least 38 people lost their lives as a result of this extreme flood. The area extent of this flooding included southern Minnesota, southwestern Wisconsin, Iowa, western Illinois, northern Missouri, southern North Dakota, and eastern parts of South Dakota, Nebraska and Kansas (Kolva 2001). The long period of inundation had significant effects on agricultural land and wetlands. Loss of land in the river valley is not only a financial loss to the owner, but also a significant loss of natural resources (Brady et al, 1998). Therefore this particular event has been selected for this study.
There are many studies about floods on the Kansas River and elsewhere. Sandholt et al. (2003) used SAR images to estimate flood area at the height of the flood event, and estimate crop loss resulting from flooding. A similar study was conducted by Wang et al (2004) who used remote sensing for monitoring the whole development of the flood disaster, from the beginning of the flood to the recovering after the disaster. They totally produced 23 reports of the flood remote sensing monitoring results from MODIS data, RADARSAT-1 data and high-resolution airborne radar data. They used these data to evaluate the efforts of the flood diversion storage in their study area. Another study of river flooding in south Asia sought to identify the rural settlements that are vulnerable floods (Sanyal and Lu, 2005). In a separate study, Sanyal and Lu (2004) used remote sensing and GIS in flood management with particular focus on the developing countries of Asia. Werle et al have studied about flood and coastal zone monitoring in Bangladesh with Radarsat ScanSAR. In their article, they discuss ScanSAR in the context of recent satellite radar work in Bangladesh. Then they outline potential applications of wide swath SAR from a resource management and emergency response point of view.
Moreover, Spink and Rogers (2004) studied about 1993 flood effects on aquatic vegetation. Submerged species significantly decreased in abundance, especially at sites with more severe flooding and many tree species were very severely impacted. However, many species were able to regenerate in 1994 from seeds or storage organs. An another study about aquatic species that the abundance of eight species declined dramatically after major flooding in 1993 (Sexton et al. 2007).
3
This study focuses on the effect of the 1993 flooding on agricultural land, grassland, woodland in the floodplain of the Kansas River. Since agriculture is a very important activity in the river valley, we emphasize loss of agricultural land. The study area encompasses a reach of the river beginning at the point where the Smoky Hill and Republican Rivers join to form the Kansas River to the eastern boundary of Riley County. We used Landsat Thematic Mapper (TM) images from 1988, 1991, 1993, 1996 and 1998 to classify land use before and after the 1993 flood event. Digital change detection techniques were used to assess and analyze the extent and geographic pattern of change. The major change in landuse took place in grassland during the flood year. The results of the work demonstrated that flood is important for landuse changes. The other studies focused on flood estimation or monitoring, flood plain geomorphology, and hydrology, channel migration and crop loss. Instead of conventional approach to flood damage estimation by using different remote sensing methods, our study seeks with observations and comments about landuse change after the great flood in adapting standard remote sensing methods. The combination of inundation demages and landuse change information obtained by using remote sensing methods provides a complete and useful description of a river flood for landuse and river management.
2. MATERIALS AND METHODS
2.1 Study Area The study area encompasses a reach of the river beginning at the point where the Smoky Hill and Republican Rivers join to form the Kansas River to the eastern boundary of Riley County, and includes portions of the river flood plain in Geary, Riley, and Pottawatomie Counties (Figure 1).
(Figure 1)
The Kansas River watershed includes 155000 km2, with 89155 km2 of this area located in northern Kansas. The river flows eastward through northeastern Kansas and joins the Missouri River at Kansas City, a total
4
distance of 272 km. The elevation of the river basin ranges from about 220m above sea level to the highest point in the river corridor in southeast Riley County at 465m above sea level (Brady et al, 1998).
The Kansas River is a meandering river with a wide flood plain and a sand substrate. Channel migration of rivers has damaged or is threatening to damage public conveyance systems such as roads, railroads, bridges and pipelines crossing farmlands (Figure 2) (USACE 1978). Significant River channel migration has primarily been a response to flood events that have occurred on the river. Mapped historical channel migration in the Kansas River Basin study, directed by the University of Kansas, focused on the bank stabilization of the Kansas River channel and its significant tributaries for the period of 1857-1976 (Weatherly, 1994).
(Figure 2)
The Kansas River basin has a continental climate typical of the central Great Plains. The winters are usually cold and summers are long and hot. The climatic record of the basin documents intense and prolonged rainfall during some years. For instance in 1993, precipitation for the period January-July was more than 52 cm in most of the flooded area and more than 104 cm in parts of northeastern Kansas (Kolva, 2001).
The soils in valleys along creeks and rivers are deep and generally fertile. Flood plains of the Kansas River are underlain by alluvium (Sorenson et al, 1987). The main concern of management on these soils is the maintenance of fertility and good soil structure. The soils in broken, hilly areas are shallow and moderately deep. The alluvium consists of clay, silt, sand and gravel, the character and proportions of which differ from one place to another.
The topography of the study area may be divided into three types: the high uplands or prairies, the creek and river valleys, including the alluvial floors and terraces, and the broken, hilly country extending from the borders of the uplands downward to the valley floors or terraces, where present. The floor of the Kansas River
5
valley is generally flat and bordered on its sides in most places by steep bluffs. The highest bluffs occur just southeast of Manhattan at Fremont point (Sorenson et al, 1987).
The location of the Kansas River basin within the interior central plain encourages the growth of prairie grasses. The native short grasses (grama and buffalo grasses) are found in the western portion of the basin. Tall grass (blue stem) is typical in the east. It should be noted that, except for the extreme eastern portion of the state, forests are located almost exclusively in the flood plain of the Kansas River. The bottomlands of the Kansas River valley were once too forested although the species composition differed from the uplands. Today some downstream parts are covered with forest, but large portions of the area have been converted to agricultural land (Sorenson et al, 1987). In the Fort Riley area north of the Kansas River, the larger tributary valleys are filled with woodlands, whereas on privately owned land south of the river tributary valleys are mostly agricultural land (Brady et al, 1998).
The Kansas River from Junction City to Topeka has experienced changes in the land use over the past fifty years. Land use has changed from primarily agricultural to extensively urban in many areas. Geary County has a more steady decrease in harvested agricultural land. Grassland use has decreased only a little. Pottawatomie County has also remained fairly stable in its landuse. Riley County has experienced urban growth, which created a steady decrease in grassland and harvested agricultural land (Messbarger et al, 1999).
2.2 Image Analysis and Change Detection 2.2.1 Preprocessing Land cover change along the Kansas River over the period 1988-1998 was analyzed using five summertime Landsat TM images. Although exact annual dates were not available, the dates of the five images were chosen to correspond as nearly as possible. Image of 1988 belongs to August 01 st, 1991 to August 26th, 1993 to August 15th, 1996 to August 21, 1998 to August 29th. Selected images were cloud free (cloud coverage< 10%).
6
Image geometry was corrected using a combination of rectification and image-to-image registration. The 1993 image was rectified and using control points selected from a USGS 1:100,000 topographic quadrangle of the study area. Coordinates of the selected ground control points were determined in-situ using a Lowrance Nav 12 GPS receiver. The image was then rectified to the UTM coordinate system via a first-order transformation (RMS < 1.0) with nearest neighbor resembling (Jensen, 1996). The remaining 5 images were then registered to the August, 1993 image (all RMS < 1.0).
Following rectification, raw digital numbers for each image were corrected for atmospheric backscatter using the IDOS method (Song, et al., 2001, Chavez, 1988). The corrected data were then calibrated to radiance values using the method of Robinove (1982): RADij = (DNij/DNmaxj) * (Lmaxj - Lminj) + Lminj
(1)
Where; RADij = radiance in pixel i and band j (mW/cm2/sr), Lmaxj and Lminj = sensor-specific maximum and minimum radiance values in band j (mW/cm2/sr), DNij = “raw” digital number in pixel i and band j., DNmaxj = sensor specific dynamic range band j.
Sensor-specific calibration values were obtained from Markham and Barker (1986). The radiance-corrected images were then cross-calibrated using the runways of the Manhattan Municipal Airport as a pseudo invariant feature. Nine non-mixed pixels were extracted from the runways and used to adjust in-band radiance values for each image (Schott et al., 1988).
2.2.2 Classification and Change Detection
7
Unsupervised classification of the imagery was done using the ISODATA algorithm with maximum likelihood decision rule (Richards, 1993). The 30 spectral clusters resulting from ISODATA were interactively labeled into one of five classes; agricultural, grassland, water, non-vegetated, woodland. Clusters that could not easily be sorted into any of these land cover classes were evaluated using a “cluster-busting” technique (Jensen, 1996). Using this technique, “good” clusters (ones for which class membership confidence is high) were masked from the image. The remaining unsorted pixels were then reclustered using ISODATA. These new clusters were evaluated and labeled as before.
Because these analyses were done using archived data, conventional assessment of classification accuracy was not possible due to lack of suitable reference data. However, some idea of classification accuracy was gained by comparison of these results to the Kansas Land Cover Mapping Project (KLCMP) (Whistler et al., 1995). The KLCMP land cover maps were derived from 1989 Landsat TM data, thus they are not directly compatible with any of the image dates used here. However, the 1998 classified image agrees with the KLCMP map with 83% accuracy. Based on the accuracy of the 1988 image and the classifications scheme, we are confident that are classifications are accurate.
2.2.3 Change Analysis Land cover change was analyzed by extracting from the entire image data set and generating change matrices for the time period between each image pair, and for the study area as a whole. These change matrices showed categorical shifts between each time period. Categorical shifts by cover type are summarized in Figures 7. Change images were also generated to visually show where change occurred (Figure 3,4,5,6).
3. RESULTS
The study area was classified as agriculture, grassland, woodland, non-vegetated and water. Total classified land was 138995.96 hectares in 1988, 104063.04 hectares in 1998; agricultural lands accounted for 10000.58
8
hectares of these figures. Agricultural land was smaller in 1988 because of a dry period; however it has widened a little after 1988 (Figure 3). 1196 hectares of agricultural land remained underwater in between 1991-1993 and 546 hectares of agricultural land were converted to grassland (Table1 and Figure 4). In 1993, the floodplain was inundated, temporarily becoming a wetland area and causing permanent damage to the surface. Agricultural land was 1926.05 hectares in the classified areas during the 1993 flood period, a reduction of 6622.91 hectares compared to the preflood classification period. After the flood, especially in flood plain, large areas were converted to agricultural land as before of flood. Agricultural land increased in 1996 and 270 hectares of water land were again converted to agricultural land and also 81 hectares woodland were converted to agricultural land (Table 1and Figure 5). The agricultural lands expand according to previous years in 1998 (Figure 6). It can be say that it is good for development of agriculture in study area.
Grassland comprised 102257.50 hectares of that total in 1988. Area of grassland decreased markedly in the flood year 1993, down to 62672.70 hectares. This was the lowest total for any of the years. The majority of the grassland „lost‟ in 1993 was in fact covered with water. Following the flood in 1996 grassland comprised 105064.90 hectares of the total classified land. It is notable that in the period following the flood, 923 hectares of former grassland remained covered with water. This was the one of big changes about water classification (Table 1). If 1988 and 1991 are compared, only 106 hectares of grassland were covered with water but not only water was converted to agricultural land but also 914 hectares grassland converted to agricultural land (Figure 3). 190 hectares woodland were converted to grassland between 1993 and 1996 (Figure 5). If the 1991-1993 year‟s changes are compared, 557 hectares woodland were converted with water (Table1 and Figure 4).
(Table1) (Figure 3) (Figure 4) (Figure 5) (Figure 6)
9
In 1993 much of the study area was covered with water. Comparison of the flood period with preflood imagery shows that 557 hectares of woodland, 923 ha of grassland, 1013 ha of nonvegetated land and 1196 ha of agricultural land were inundated due to flooding. After the flood, 284 hectares of temporarily water-covered land returned to woodland, 2016 hectares to grassland, 571 hectares land to nonvegetated land (Table 1 and Figure 5).
4. DISCUSSION
It is apparent that some areas of the flood plain showed notable land cover change before and after the flooding episode. The inundated areas quite probably represent a permanent change in the land surface resulting from the flood. Because of the flood, especially old river bed covered water that it caused to reduce of grassland for temporally when it compared to other years. Agricultural lands have decreased during the flood in 1993. But after the flood was over, especially in the flood plain, the land was again converted to agricultural land to reach its pre- flood status. More serious loss due to flood occurred in grasslands. The flood covered 923 hectares of grassland. This was the major change in land classification during the flood year. Non-vegetated areas have expanded proportionally ratio in 1993 due to flood and especially in 1998 (Figure 7). However these are decreased in an important ratio infavor of agricultural lands and woodlands. The results of this study demonstrated that floods played a very important role in landuse changes. Landuse changes monitoring along the river could be very useful in supporting the development of a long-term national river management plan. The monitoring of landuse changes by using remote sensing technique could provide to determinate of measures.
(Figure 7)
Results of this analysis can present to suggest methods for preventing or mitigating flood impacts in the future. Changes in the Kansas River Basin over the past century include the addition of flood control structures, changes in land use, and human encroachment into the flood plain. Flood control structures decrease peak flow levels, thus reducing the likelihood of flooding. Reservoirs are very important in the prevention of flood 10
damages along the Kansas River. After the construction of the reservoir the peak flows leveled off. Artificial structures such as levees and dikes do not allow streams to access the floodplain during major flow events (USDA, 2001). The flood-control reservoirs and the levee system working in concert protected Junction City, Manhattan, Topeka, Lawrence, and Kansas City; without the levees, these cities would have been flooded (Perry, 1993). Wetlands commonly mitigate the effects of floods. Wetland areas can be filled with and temporarily store floodwaters so that flood effects on agricultural and residential areas are lessened. Although restoration of upland and bottom land wetlands can reduce flood stages in more frequent floods (25 years and less), it is questionable whether they would have significantly altered the 1993 flood conditions (Kolva, 2001).
5. CONCLUSION
This paper discussed the effects of 1993 flood on agriculture, grassland, woodland, water land and nonvegetated land based on the comparison of satellite images taken before and after the flood year. The main goal of the study is landuse change monitoring and comments after the great flood by using standard remote sensing methods. Five Landsat TM images were analyzed for land cover change over the period 1988-1998. Landuse change was analyzed from the image data set and generating change matrices. We have determined the risk areas using these images analysis with the goal of monitoring the ongoing changes in floodplain. Large woodland, agricultural land, grassland and nonvegatated land converted to water land during the flood period. Land damage was due to the destruction of agricultural land and grassland area, the latter can be permanent. The only upside of floods is that they increase the fertilization in land after the flood. From this study, we can draw the conclusion that 1993 flood can play important roles in landuse changes in Kansas River. This study can be use as base for other watershed studies and show the way to local administrator by using time series analysis in remote sensing techniques for river and natural disaster management on floodplain for future.
6. REFERENCES
Brady, L.L., Grisafe, D.A., McCauley, J.R., Ohlmacher,G.C., Quinodoz, H.A.M., Nelson, K.A., 1998, “The Kansas River Corridor-Its Geologic Setting, Land Use, Economic Geology and Hydrology”, Kansas Geological Survey.
11
Chavez, P.S. 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment. 24:459-479.
Department of Army Kansas City District Corps of Engineers (USACE), 1978, “Kansas River Bank Stabilization Study”.
Jensen, J. 1996. Digital image processing: a remote sensing perspective. Prentice-Hall, New York.
Kolva, J.R., 2001, “Effects of the Great Midwest Flood of 1993 on Wetlands”, National Water Summery on Wetland Resources, United States Geological Survey (USGS) Water Supply paper 2425.
Perry, C.A, 1993, Effects of Reservoirs On Flood Discharges In The Kansas And The Missouri River Basins, U.S. Geological Survey Circular, 1120-E.
Richards, J.A. 1994. Remote Sensing and Digital Image Interpretation: an Introduction. SpringerVerlag, Berlin.
Sandholt, I., Fog, B., Fensholt, R., 2003. Flood Monitoring the Senegal River Valley: First result based on SAR PRI data, Danish Journal of Geography. 1:103.
Sanyal, J., Lu, X.X., 2004. Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A review. National Hazards. 33:283-301.
Sanyal, J., Lu, X.X., 2005. Remote Sensing and GIS-based Flood Vulnerability Assessment of Human Settlements: A case study of Gangetic West Bengal, India. Hydrological Procecess.19:3699-3716.
Schott, J.R., Salvaggio, C., Volcheck, W.J. 1988. Radiometric Scene Normalization Using Pseudo Invariant Features. Remote Sensing of Environment. 26:1-16.
Sexton, O.J., Drda, W.J., Sexton , K.G., Bramble, J.E., 2007. The Effects of Flooding Upon the Snake
Fauna of an Isolated Refuge. Natural Areas Journal, 27(2):133-144.
Sorenson, C.J., Johnson, Q.C., Dort, W., 1987. “Quaternary Environments of Kansas”, Guide Book Series 5, Lawrence, Kansas.
Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., and Macomber, S.A. 2001. Classification and change detection with Landsat TM data: when and how to correct atmospheric effects? Remote Sensing of Environment. 75:230-244.
Spink, A., Rogers, S., 1996. The effects of a record flood on the aquatic vegetation of the Upper Mississippi River System: some preliminary findings. Hydrobiologia, 340: 1-3: 51-57.
USDA (U.S. Department of Agriculture) 2001. The Kansas State Conservation Commission “Kansas River and Stream Corridor Management Guide”, Kansas.
Wang, S., Chen, S., Zhou, Y., Zhao, Q., 2004. Assessing the Efforts of the Flood Diversion and Storage in the Drainage Area of Huaihe River Using Remote Sensing. IEEE. 0-7803-8742-2/04.
12
Werle, D., Martin, T.C., Hasan, K., 2000. Flood and Coastal Zone Monitoring in Bangladesh with Radarsat ScanSAR: Technical Experience and Institutional Challenges. 148 Johns Hopkins Apl. Technical Digest, Volume 21, No.1.
Whistler, J.L, Egbert, S.L., Jakubauskas, M.E., Martinko, E.A., Baumgartner, D.W., and Lee, R-Y. 1995. The Kansas land cover mapping project: regional scale land use/land cover mapping using Landsat Thematic Mapper Data. Proceedings, ASPRS/ACSM Annual Meeting and Exposition, Charlotte, N.C. 3:773-785.
Messbarger, M.S., Mitchell, J.M., Peterie, M.L., 1999, “20 th Century Environmental History of the Kansas River, Junction City to Topeka”, Kansas State University, Department of Geography, Student Final Project, Submitted to Dr. C. Martin. Weatherly, J.J.,1994. “Historical Channel Adjustments of the Lower Big Blue River Below Tuttle Creek Reservoir Manhattan, Kansas, 1857-1991”, Master Thesis, Kansas State University, Department of Geography.
13
