Spatiotemporal dynamics of land surface parameters in the Red ...

ARTICLE IN PRESS

Physics and Chemistry of the Earth 29 (2004) 795–810 www.elsevier.com/locate/pce

Spatiotemporal dynamics of land surface parameters in the Red River of the North Basin Assefa M. Melesse

*

Earth System Science Institute, School of Aerospace Sciences, University of North Dakota, Grand Forks, ND 58202-9007, United States Accepted 24 May 2004

Abstract The movement and distribution of water in the hydrologic cycle is affected by the level and type of land surface parameters. Thus accurate representation of the physical and biological features of the landscape within a watershed is required. A strong link exists between climate variability and the resulting changes in such land surface parameters as energy, land-cover and surface microclimates. Imagery from Landsat and other satellites provide land-cover and surface microclimate information with high temporal and spatial accuracy. This paper utilizes the land surface temperature (LST) derived from the thermal band of Landsat images and Normalized Difference Vegetation Index (NDVI) derived from its red and near-infrared bands to further improve land-cover and surface microclimate mapping. Remotely-sensed spatially distributed surface latent and sensible heat fluxes were also estimated. The study was conducted on 10,200 km2 watershed area of the Red River of the North Basin, North Dakota/Minnesota. Over the period of 1974–2002, seven images from Landsat Multispectral Scanner, Thematic Mapper and Enhanced Thematic Mapper plus sensors were used. Landsat images were processed using an unsupervised classification. Corrected LST and NDVI, which indicate a strong relationship with the land-cover data, were identified using scattergrams. Surface microclimate parameters (fractional vegetation cover, FVC and fractional impervious surface, FIS area) were estimated and their spatial and temporal distributions determined. Surface energy fluxes (latent and sensible heat) were assessed over space and time. The results indicate that vegetation cover (FVC > 0.5) increased from 7% in 1974 to 33% in 2002 due to cropland farming in the Red River Valley and an increase in impervious areas (FIS > 0.5) (by 79% from 1974 to 2001) attributed to the growing cities in the valley for the period of study. The study also indicated an increase in sensible and latent heat fluxes from 1998 to 2002 for areas classified as developed and cropland, respectively. Hydrograph analysis of the flow at Grand Forks gauging station also indicated runoff response of the basin has increased between 1993 and 2002 with all years having percent runoff greater than 10% compared to only 35% of the years between 1974 and 1993. 2004 Published by Elsevier Ltd. Keywords: Fractional vegetation cover; Landsat; Land-cover; Energy flux; Surface microclimate; Red River

1. Introduction The land surface affects the partitioning of water and energy fluxes, which in turn changes the state of the surface (Pielke and Avissar, 1990). Hydrologic responses of watersheds have been strongly linked to the land surface process and surface microclimate covers. Remotely-sensed surface parameters such as land-cover, surface microclimate parameters (fractional vegetation cover, FVC, land surface temperature, LST and fractional impervious surface, FIS area), and surface energy *

Present address: Department of Environmental Studies, Florida International University, Miami, FL 33199, USA. Tel.: +1-305-3481930; fax: +1-305-348-6137. E-mail address: melessea@fiu.edu (A.M. Melesse). 1474-7065/$ - see front matter 2004 Published by Elsevier Ltd. doi:10.1016/j.pce.2004.05.007

fluxes (net radiation, latent and sensible heat fluxes) are useful in understanding the spatiotemporal changes of the land surface, hence the resulting hydrologic responses of watersheds (Fig. 1). Remote sensing uses measurements of the electromagnetic radiation, usually sunlight reflected in various bands, to characterize the landscape, infer surface properties, or in some cases actually estimate hydrologic state variables. Measurements of the reflected solar radiation (visible and short wave infrared sensors) give information on land-cover, extent of surface imperviousness and albedo. Thermal radiation (thermal-infrared sensors) gives estimates of surface temperature and surface energy fluxes. Land-cover is the actual distribution of physical and biological features of land surface. Accurate and

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A.M. Melesse / Physics and Chemistry of the Earth 29 (2004) 795–810

Fractional vegetation cover

Precipitation

Topography

Land-cover

HYDROLOGIC CYCLE

Surface energy fluxes

Surface Imperviousness

Snowmelt

Surface Temperature

Fig. 1. Surface parameters and fluxes versus hydrologic response.

up-to-date information on land surfaces and the state of the environment are critical components of environmental planning and management. Land-cover information is used in hydrologic modeling to estimate the value of surface roughness or friction as it affects the velocity of the overland flow of water. It may also be used to determine the potential amount of rainfall that can infiltrate into the soil. Vegetation indices and band ratios provide useful information in characterizing land surface cover and enhance land-cover mapping. Researchers have conducted studies using vegetation indices to derive the relationship between remotelysensed radiance and biophysical properties of forests (Boyd et al., 1996; Curran et al., 1992). Multi-temporal Normalized Difference Vegetation Index (NDVI) data derived from Landsat sensors (Spanner et al., 1990; Danson and Curran, 1993) and the Advanced Very High Resolution Radiometer have been used for land-cover mapping and land use change studies (Stone et al., 1994; Tucker et al., 1991, Lambin and Strahler, 1994). For land-cover mapping, the radiance recorded in the middle-infrared (MIR) (1.3–3 lm) and long wave thermal infrared (TIR) (8–14 lm) wave bands have been shown to provide important additional and supplementary information to that provided by the reflectance data measured in visible (0.4–0.7 lm) and near-infrared (NIR) (0.7–1.3 lm) bands. Data acquired in the MIR and TIR wave bands can discriminate among vegetation types and assess changes in land use (Baret et al., 1988; Panigrahy and Parohar, 1992; Melesse and Jordan, 2002). Surface energy fluxes are related to surface temperature, vegetative properties, albedo, and surface emissivities. Low surface temperatures indicate high moisture and/or vegetated cover, hence latent heat dominance. Conversely, high temperatures indicate dry surface or stressed vegetation, hence dominance by sensible heat flux. In the Red River Valley, North Dakota/Minnesota, average annual ground-surface

temperature has increased 2.5 C and average annual air temperature has increased 2 C during the past century (Gosnold et al., 1997). Precipitation dropped from 1890 until the mid-1930s and has been rising since 1950 (Groisman and Easterling, 1994). Historically, the Red River valley floods frequently and peaked to historic high in April 1997 when 1590 m3 /s value for April flow was measured at the Grand Forks gauging station. The 99-year mean flow at the same station is 330 m3 /s. Changes in human land use patterns can have impacts on flood dynamics. The lag time for water draining the surface and entering the Red River is greatly reduced due to the construction of drainage ditches in agricultural fields and along rural roads (Bluemle, 1997). In addition, increasing urbanization near the river can increase the runoff volume and decrease the time to peak runoff, substantially affecting the river flood dynamics, resulting in more rapid, more severe and more frequent flooding than under natural condition. The Red River of the North receives most of its flow from its eastern tributaries largely as result of regional patterns in precipitation. Annual runoff varies greatly, and most runoff occurs in spring and early summer from rains falling on soils saturated by melting of winter snow. In this study, radiance data acquired by Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) in various bands of the spectrum were used to improve landcover mapping and demonstrate the use of NDVI derived from (1) the visible and NIR bands, and (2) LST from the TIR band in discrimination of land-cover classes and maximize the accuracy of the classification process. In addition, the spatial distribution of surface microclimate parameters (FVC and FIS) and surface energy fluxes (latent and sensible heat) were evaluated spatially for the periods of study. The objectives of this study were (1) to derive spatial land-cover maps using MIR, NDVI and LST and to assess the strength of relationships of the radiance data acquired within land-cover categories of hydrologic interest, (2) to evaluate the change and spatial distribution of actual land-cover and surface microclimate parameters (FVC and FIS) over time, (3) assess the spatiotemporal variability of sensible and latent heat fluxes, and (4) to assess the linkage between the hydrologic responses of the basin and the change in land surface parameters estimated from Landsat images.

2. Description of study area and data sets 2.1. Study area The Red River of the North (Fig. 2) has a meandering length of 880 km along the channel and 456 km in a straight line. It flows north from the confluence of the

ARTICLE IN PRESS A.M. Melesse / Physics and Chemistry of the Earth 29 (2004) 795–810

797

Fig. 3. The study area on the Red River of the North Basin.

Fig. 2. Red River of the North Basin.

Bois de Sioux and Otter Tail rivers in southern North Dakota/Minnesota, USA to Lake Winnipeg, Manitoba, Canada draining about 100,480 km2 . The Red River flows through the very flat Red River Valley, where natural topographic variations are subtle (MacekRowland, 2001). Land-use in the Red River of the North Basin is primarily agricultural accounting for about 62% of the basin’s land-use within 0.4 km buffer of the river. Major crops include wheat, corn, soybeans, barley, sunflowers and sugar beets (USGS, 2003). About one-third of the population of the basin lives in Grand Forks/East Grand Forks, and Fargo/Moorhead, North Dakota/Minnesota. The climate of the Red River of the North Basin is continental, ranging from dry subhumid in the western part of the basin to subhumid in the eastern. Winter precipitation as snow is about 15% of the total precipitation. Average annual precipitation ranges from about 457 mm in the north-west corner of the basin to about 686 mm in the south-east corner. About three-fourths of the annual precipitation falls from April through September. Average annual temperatures vary between 2.8 and 6.1 C. Average monthly temperatures range from )18.3 C in January to 21.7 C in July. The study was conducted using data from 1974 to 2002 on part of the basin along the river from Fargo/ Moorhead in the south to north of Grand Forks/East

Fig. 4. Digital Elevation Model of the study area.

Grand Forks, North Dakota/Minnesota (Fig. 2). A 35km buffer was drawn on both sides of the river from Fargo to Grand Forks covering an area of 10,147 km2 (Fig. 3). A Digital Elevation Model (DEM) (Fig. 4) with spatial horizontal resolution of 30-m acquired from the United States Geological Survey (USGS) shows the Red River is the lowest point in North Dakota with only 15 m elevation difference between Fargo and Grand Forks (125 km) for a slope of 0.00012. 2.2. Data sets Seven Landsat images between 1974 and 2002 from MSS, TM and ETM+ sensors were used in the study (Table 1). The set of images will allow understanding the changes in land surface parameters at intervals of 1, 2, 6, 8, and 10 years. The MSS sensor (1972–1992) carried on Landsat-4 and 5 collected data using 4 bands with



Table 1 Description of images used in the study

3. Methodology

Date

Sensor

Spatial Resolution (m)

No. of bands

3.1. Land-cover mapping

August 06, 1974 August 12, 1984 August 10, 1992 July 10, 1998 July 23, 2000

MSS MSS MSS TM ETM+

4 4 4 7 8

July 10. 2001

ETM+

July 29, 2002

ETM+

80 80 80 30, 120 (TIR) 30, 60 (TIR) 15 (Panchromatic) 30, 60 (TIR) 15 (Panchromatic) 30, 60 (TIR) 15 (Panchromatic)

The Level-1G/systematic corrected scene product images were subsequently geocorrected (affine method) to base map (1:24,000 scale vector roads) from North Dakota State Water Commission (NDSWC) (NDSWC, 2003). The radiance data was radiometrically corrected and converted to reflectance. This will correct the images for limited atmospheric noises and discrepancies from different dates of acquisition will be addressed enabling inter-image comparison. Using the corrected images, unsupervised classification of the images was accomplished using the Iterative Self-organizing Data Analysis Technique (ISODATA) algorithm (Tou and Gonzales, 1974) from ERDAS IMAGINE (ERDAS, 1999). The ISODATA classifier uses an iterative approach that incorporates a number of heuristic (trial and error) procedures to compute classes. The ISODATA utility repeats the clustering of the images until either a maximum number of iterations have been performed, or a maximum percentage of unchanged pixels have been reached between two iterations. The algorithm starts by randomly selecting cluster centers in multidimensional input data space. Each pixel is then grouped into a candidate cluster based on the minimization of distance function between that pixel and the cluster center. After each iteration, the cluster means are updated, and clusters are possibly split or merged depending on the size and spread of the data points in the clusters. The unsupervised ISODATA classifier yielded 30 spectral classes (Fig. 5). Scattergrams of MIR band versus scaled NDVI (scaled between low and high values) for MSS images, and scaled NDVI versus scaled LST (scaled between low and high values) for TM and ETM+ images were used to recode the 30 classes into level 1 land-cover classes. The spectral signatures of all these classes were used to determine the mean radiance for each band. The scattergrams were used to find instances of strong correlation between the NDVI and LST and the land-cover data of the basin. From the scattergrams, seven USGS-Land Use and Land Cover

8 8

spatial resolution of 80 m (Table 2). Landsat-4 and 5 orbit at an altitude of 705 km and provide a 16-day, 233orbit cycle. The Landsat TM instrument also carried aboard Landsat-4 and 5 (1982-present), achieves 30 m image resolution in seven spectral bands (120 m TIR). The Landsat ETM+ instrument, carried aboard Landsat-7 (1999-present), includes new features that make it a more versatile and efficient instrument for global change studies, land-cover monitoring and large area mapping than TM. It has an enhanced sensor with a broad spectrum including a 15 m panchromatic and a 60 m · 60 m spatial resolution of the thermal band (NASA, 2002). Radiance data from sensors on satellites provide valuable information about watershed cover, from which the thermal response of the surface, type and extent of watershed cover can be easily determined. Average monthly precipitation data (1974–2002) were acquired from the National Climatic Data Center (NCDC, 2003). NCDC provides the weighted average precipitation data gathered from networks of rain gauges for each sub-region in each state. The daily flow hydrographs from 1974 to 2002 at the Grand Forks gauging station were obtained from the North Dakota USGS Office (USGS, 2003). The Grand Forks gauging station measures the Red River’s flow from more than 77,000 km2 of its drainage. The data were used for hydrograph analysis to assess the relationship between the change in the land surface parameters and the runoff response of the study area. Table 2 Description of Landsat sensors used in the study NASA (2002) Bands

Landsat 4 & 5 MSS Wavelength (lm)

Blue Green Red NIR MIR TIR MIR Panchromatic

0.5–0.6 0.6–0.7 0.7–0.8 0.8–1.1

Landsat 4 & 5 TM Resolution (m) 80 80 80 80

Landsat 7 ETM+

Wavelength (lm)

Resolution (m)

Wavelength (lm)

Resolution (m)

0.45–0.52 0.52–0.60 0.63–0.69 0.76–0.90 1.55–1.75 10.40–12.50 2.08–2.35

30 30 30 30 30 120 30

0.45–0.52 0.53–0.61 0.63–0.69 0.78–0.90 1.55–1.75 10.40–2.50 2.09–2.35 0.52–0.90

30 30 30 30 30 60 30 15


3.1.2. Land surface temperature Surface temperature is important for understanding the exchange of energy between the earth’s surface and the atmosphere. Surface radiant temperatures were calculated from the thermal band radiance values of TM and ETM+ sensors. The surface temperature was obtained from the Landsat TIR band using the simplified Planck function (Eq. (2)) (Markham and Barker, 1986):

Landsat MSS, TM, and ETM+ images

Unsupervised Classifier

MIR Band/ NDVI s

30 classes

799

TM/ETM+

LST ¼

LSTs

ln

K2 eK1 þ R

1

ð2Þ

where LST is land surface temperature (K), R is band 6 spectral radiance, e is surface emissivity related to NDVI (Eq. (5)), K1 is calibration constant 1; K2 is calibration constant 2. For Landsat-5 TM, K1 and K2 are 607.76 mW cm2 sr1 lm1 and 1260.56 K, respectively. For Landsat-7 ETM+, K1 and K2 are 666.09 W m2 sr1 lm1 and 1282.71K, respectively. For Landsat 4/5 TM, R is a linear function of the digital number (DN):

Scattergram (LSTs vs. NDVIs) Scattergram (MIR Band vs. NDVIs) Recoding Level 1 Land-cover classes

R ¼ m DN þ d

ð3Þ

Fig. 5. Flow chart showing land-cover mapping.

(LULC) system level 1 land-cover classes (Anderson et al., 1976) were identified. The cloud-shadow spots on some parts of the images from 1998 to 2002 were discriminated using the technique described in Melesse and Jordan (2002). The thermal band was used to discriminate clouds from bright surfaces and roof-tops, and combination of NDVI and LST with further masking and reclassifying technique was used to separate shadows of clouds from the water bodies. The overall land-cover classification accuracy was conducted using 40 randomly selected sampling points with at least five sampling points in each recoded class. 3.1.1. Normalized Difference Vegetation Index The Normalized Difference Vegetation Index or NDVI (Rouse et al., 1974) is a measure of the amount of greenness in the vegetation cover of a watershed. It is the ratio of the difference to the sum of the reflectance values of NIR (Landsat band 4) and red (band 3) (Eq. (1)). NDVI ¼

NIR RED NIR þ RED

ð1Þ

In highly vegetated areas, the NDVI typically ranges from 0.1 to 0.6, in proportion to the density and greenness of the plant canopy. Clouds, water and snow, which have larger visible reflectance than NIR reflectance, will yield negative NDVI values. Rock and bare soils have similar reflectances in the two bands and result in NDVI values near zero.

where m ¼ 0:0056322 mW cm2 sr1 lm1 , and d ¼ 0:1238 mW cm2 sr1 lm1 . R values for Landsat-7 ETM+ were calculated as (NASA, 2002) R¼

ðLmax Lmin Þ ðDN 1Þ þ Lmin 254

ð4Þ

where Lmax and Lmin are maximum and minimum spectral radiance (W m2 sr1 lm1 ). Lmax and Lmin are nonreal time post-launch values, different for the low (6L) and high (6H) gain versions of the thermal band on ETM+. For the ETM+ images used in this study, Lmin and Lmax were 0 and 17.04 W m2 sr1 lm1 respectively. Surface emissivity (ratio of the energy radiated by a surface to the energy radiated by a blackbody at the same temperature) is used to compute the LST from the thermal band of Landsat. Surface emissivity is estimated

Astronomical & Weather data

Satellite image

NDVI

α ε

Surf. Temp.

RS

θ, de-s, Ta

Rn

Elevation

DEM, slope

RL

H LE

Fig. 6. Flow chart for surface energy fluxes computation.

G



Fig. 7. Scattergrams of MIR band/LSTs versus NDVIs for the 30 land cover classes: (a) 1974, (b) 1984, (c) 1992, (d) 1998, (e) 2000, (f) 2001, (g) 2002.

using NDVI and an empirically-derived method (Bastiaanssen, 2000), e ¼ 1:009 þ 0:047ðln NDVIÞ

ðNDVI > 0Þ

ð5Þ

For NDVI < 0 (e.g., water) emissivity of 1 was assumed.

3.2. Surface microclimate mapping In addition to land-cover change, the microclimate of the basin from the 1974 to 2002 was studied. The microclimate parameters assessed from radiance data of


Area (%)

60

FVC ðNDVIs Þ

50

1974 1998

40

2002

1984 2000

1992 2001

20 10

D N

W

A

W ET LA

O W

TE R /S H

A

U IL B N A

U R B

D

TU P

N D A N G EL

R ES T

R A

N D /F O

C

R O

B

PL A

A

R

R EN LA

N D

0

Fig. 8. Summary of land-cover distributions for the study years.

the images included FVC and FIS area. Those pixels classified as vegetated or developed (non-water bodies) were further classified, and their fractions of vegetative cover were determined for each pixel. These microclimate parameters were mapped over the study area and their spatial and temporal changes were analyzed to identify areas of intense change in vegetation and development. 3.2.1. Scaled land surface temperature To compare images from different scenes and time, scaling the temperature between the low and high values has been used by researchers (Che and Price, 1992; Carlson and Arthur, 2000). The scaled land surface temperature ðLSTs Þ is given by ð6Þ

where LSTi is the land surface temperature for pixel i, LSTlow and LSThigh is the lowest and the highest surface temperature of the area of analysis. 3.2.2. Fractional vegetation cover To understand the change in the vegetation cover for images of different scenes and dates, the scaled NDVI ðNDVIs Þ has been used by many researchers (Price, 1987; Che and Price, 1992; Carlson and Arthur, 2000). NDVIs ¼

NDVIi NDVIlow NDVIhigh NDVIlow

ð8Þ

where FVC ranges between 0 and 1.

30

LSTi LSTlow LSTs ¼ LSThigh LSTlow

2

801

ð7Þ

NDVIs ranges between 0 and 1. NDVIlow and NDVIhigh are values for bare soil and dense vegetation, respectively. Carlson and Ripley (1997) found the relationship between FVC and NDVIs to be

3.2.3. Fractional impervious surface area Remote sensing techniques are capable of quantifying impervious surface over a region or watershed. Impervious land-cover is widely accepted as an indicator of urbanization, and its impacts on both water quantity and water quality is significant. Impervious surfaces increase the frequency and intensity of downstream runoff events, resulting in alterations of channel structure and the timing and volume of peak runoff. These effects have important ecological and economic impact on the hydrology of watersheds. Impervious surfaces cause local decreases in infiltration, percolation and soil moisture storage, reductions in natural interception and depression storage, and increases in runoff. Runoff from impervious surfaces has greater velocities, larger volumes and shorter times of concentration (Brun and Band, 2000). The extent and spatial distribution of impervious surface relative to streams and watersheds is an important empirical measure for evaluating stream health and for making effective watershed management decisions. Impervious surface effects upon water quality and channel stability have been shown to occur when 10– 15% of the watershed surface is impervious (Shaver and Maxted, 1995; Booth and Reinelt, 1993). Ridd (1995) and Owen et al. (1998) showed that the relation between the FVC and FIS for developed areas as, FIS ¼ 1 FVC

ð9Þ

3.3. Surface energy flux Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near the ground level. These processes include radiative, sensible heat, latent heat, and subsurface heat exchange processes. Results from these models provide microclimatic information such as surface temperature, radiation and heat fluxes related to particular surfaces. Surface energy fluxes require energy inputs, moisture conditions of soil and vegetation, and near-surface climatic conditions (Norman et al., 1995; French et al., 2000). Remote sensing has proven to provide the energy inputs (short and long wave radiations) and surface conditions of soil and vegetation at a reasonable spatial and temporal scale (e.g., 30-m from Landsat TM and ETM+, and every 16 days). Meteorological stations provide the near-surface climatic data at a spatial scale of few kilometers and temporal scale of every few minutes.



Fig. 9. Spatial distributions of fractional vegetation cover: (a) 1974, (b) 1984, (c) 1992, (d) 1998, (e) 2000, (f) 2001, (g) 2002.

In this study, Surface Energy Balance Algorithms for Land (SEBAL) model were used to compute the surface latent and sensible heat fluxes (Bastiaanssen et al., 1998a,b). In the absence of horizontally advective energy, the surface energy budget of land surface satisfying the law of conservation of energy can be expressed as

flux to the air, and G is soil heat flux. Energy flux models solve Eq. (10) by estimating the different components separately (Fig. 6).

Rn LE H G ¼ 0

Rn ¼ ð1 aÞRs # þRL # RL " ð1 eÞRL #

ð10Þ

where Rn is net radiation at the surface, LE is latent heat or moisture flux (ET in energy units), H is sensible heat

3.3.1. Net radiation The Rn absorbed by the surface is the sum of the net short (solar) and long (thermal) wave radiations. ð11Þ

where Rs# is the incoming shortwave radiation, a is surface albedo, e is surface emissivity, RL# is absorbed


FVC (%)

50 45 40

3.3.3. Sensible heat flux The sensible heat loss (W m2 ) from the surface is given by, LST Ta H ¼ qCp ð18Þ Ra

FVC > 0.5

35 30 25 20 15 10 5 0 1974

1984

1992

1998

2000

2001

2002

Year Fig. 10. Percent area covered for pixels with FVC > 0.5.

incoming longwave radiation, and RL" is outgoing longwave radiation. Rs# is estimated as, Rs# ¼

where q is air density (kg m3 ); Cp is the volumetric heat capacity of air (1004 J kg1 K1 ), Ra is aerodynamic resistance, LST is land surface temperature (K). The detailed technique for estimating latent and sensible heat fluxes using remotely-sensed data from Landsat and other sensors is documented in Bastiaanssen et al. (1998a), Bastiaanssen et al. (1998b), and Morse et al., 2000. The model was tested in Europe, Asia, Africa, and in Idaho in the US and proved to provide good results (Bastiaanssen et al., 1998a; Bastiaanssen et al., 1998b; Wang et al., 1998; Bastiaanssen, 2000; Morse et al., 2000).

4. Results and discussion

Gsc ssw 2 sin hdes

ð12Þ 4.1. Land-cover mapping and change analysis

where Gsc is the solar constant expressed as 1367 W m2 , h is sun elevation angle in radians, des is the relative distance between Earth and Sun in astronomical units, and ssw is one-way atmospheric transmitivity, computed as a function of elevation (FAO-56) (Allen et al., 1998), ssw ¼ 0:75 þ 0:00002z

ð13Þ

where z is the elevation above sea level (m). The RL# is estimated using Eq. (14) by the Stefan– Boltzmann law, RL# ¼ rea Ta4

ð14Þ

where r is the Stefan-Boltzmann constant (5.67 · 108 W m2 K4 ), a is the atmospheric emissivity (dimensionless), and Ta is the near-surface air temperature (K). The empirical equation for a by Bastiaanssen et al. (1998a) is, ea ¼ 0:85ðln ssw Þ

803

0:09

ð15Þ

The RL" is the thermal radiation flux emitted from the earth’s surface to the atmosphere determined as, RL" ¼ reTs4

ð16Þ

where Ts is the surface temperature (K). 3.3.2. Soil heat flux Soil heat flux is flux of heat into the soil due to conduction. SEBAL computes the ratio G=Rn using the following empirical equation representing values near midday (Bastiaanssen, 2000; Morse et al., 2000), G=Rn ¼ 0:2ð1 0:98 NDVI4 Þ where G is in W m2 .

ð17Þ

4.1.1. Land-cover mapping Thirty classes of land-cover were recoded to seven classes of hydrologic interest, which were displayed on the images. The seven classes were urban and built-up area (I), cropland (II), forest (III), rangeland (IV), water (V), wetland (VI), and barrenland (VII). The MIR versus NDVIs scattergrams for 1974, 1984 and 1992 MSS images have a positive slope (Fig. 7a–c). Vegetated surfaces have higher NDVI and MIR values than nonvegetated surfaces such as barrenland and developed areas. Although the MIR band responds well to vegetation moisture content, its ability to discriminate wetland from water, barrenland from urban built-ups, and cropland from forest was not as strong as the LSTs versus NDVIs relation. The LSTs versus NDVIs slope in all images is negative (Fig. 7d–g). An increase in green biomass is often associated with a reduction in surface resistance to evapotranspiration, greater transpiration, and a larger latent heat transfer resulting in lower surface temperature. The LSTs versus NDVIs scattergrams show a clear discrimination of land-cover classes and aggregation of classes with similar spectral signatures. Water bodies have lower surface temperature and NDVI and are shown on the lower left corner of the diagram. Clouds have cooler temperatures than water bodies and nearzero NDVI. Wetlands have warmer mid-morning surface temperatures than do open water bodies, and due to their vegetative cover they have higher NDVI than water. Rangeland (grassland and brush) has higher surface temperature than wetlands because the soil is



Fig. 11. Spatial distribution of FIS area (FIS > 0.5) for the Grand Forks/East Grand Forks, and Fargo/Morehead, North Dakota/Minnesota.

unsaturated. The infrared reflectance of the rangeland is not much different from that of the non-forested vegetated wetland. Agriculture (cropland and pasture) has higher NDVI than rangeland, and depending on recent

irrigation and growth stage, the temperature varies. Forests have higher NDVI, and depending on whether it is an upland or wetland forest, the surface temperature varies. Upland forests have higher surface temperatures


than wetland forests. Barrenland has the highest surface temperature and lower NDVI than the urban and builtup areas. Barrenland is shown on the upper left corner of LSTs versus NDVIs diagram. Urban and built-up areas have also higher surface temperature and lower NDVI than all other classes but barrenland. Surface temperature variation in vegetated surfaces (cropland versus forests, rangeland versus cropland) results from variations in the proportion of surrounding bare soil visible to the thermal sensor of Landsat and the thermal inertia of the surface. Thermal inertia is the measure of thermal response of surfaces to temperature changes. It is a function of thermal conductivity and heat capacity, and is affected by surface characteristics such as soil moisture and albedo. The thermal inertia of vegetation canopies is lower than that of soils. The accuracy assessment of the land-cover classification shows the technique successfully mapped the land-over with an average overall accuracy of 87.5%. This was conducted using 40 randomly selected sample points with at least five sampling points in each class.

805

4.1.2. Land-cover change analysis The land-cover change analysis conducted between 1974 and 2002 utilized seven USGS-LULC level 1 landcover classes (Anderson et al., 1976). Summaries of the changes in land-cover distribution, showing percentage of land-use for each year of study, are presented in Fig. 8. The land-cover mapping showed more than 50% of the study area was covered by cropland and forest during the period of study. The cropland area has increased by 23% between 1984 and 1998, and by 31% between 1984 and 2002 (Fig. 8). The increase in vegetative cover for 1998 can be attributed (1) to the 1997 flood which left the area wet for an extended period of time, and (2) to the flood control measures in 1998, which resulted in increased residence time of the flow allowing more water in the valley. This causes an increase in available soil moisture and vegetative cover. The rangeland acreage decreased between 1974 and 2002. Urban and developed areas for the years of study increased by 79% from 1974 to 2002 compared with an

Fig. 12. Spatial maps of sensible heat: (a) 1998, (b) 2000, (c) 2001, (d) 2002.


4.2.1. Fractional vegetation cover The spatial distributions of FVC are indicated in Fig. 9a–g for 1974–2002. The FVC values were higher for areas south of Grand Forks (1998), central part of the study area (2000), the Minnesota side of the study area (2001), and northwest of Fargo (2002) (Fig. 9). The fractional vegetation cover ðFVC > 0:5Þ increased from 7% in 1974 to 48% in 1998 due to the expansion of cropland in the Red River Valley (Fig. 10). The bigger jump in the 1998 FVC can be also attributed to the 1997 record high flood in the study area which increased the soil moisture in the valley for months. The reduced flow to the river increases infiltration and soil moisture allowing more vegetative cover. After the 1997 flood,

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flow to the river was reduced in 1998 (Fig. 14). Since 1998, the FVC of pixels having at least 50% vegetation cover was greater than 20% of the study area, indicating an increase in cropland acreage and other vegetated covers. The differences among 2000, 2001 and 2002 were not significant due to the short period of time to see

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increase in urbanization of 23% between 1974 and 1992. This indicates that most of the intense urban development occurred after 1992 (Fig. 8).

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Fig. 14. Monthly flow hydrographs and precipitation at the Grand Forks gauging station from 1974 to 2002.

Fig. 13. Spatial maps of latent heat: (a) 1998, (b) 2000, (c) 2001, (d) 2002.


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imperviousness of the study area increased significantly around the south and north tips of the study area. This is mainly due to the expansion of Grand Forks/East Grand Forks and Fargo/Moorhead cities (Fig. 11). Fig. 11 shows the increase in the developed areas and impervious surface area ðFIS > 0:5Þ for both urban

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Fig. 15. Monthly flow hydrographs and precipitation for the years of study at the Grand Forks gauging station.



areas. The study shows larger urbanization and development occurred after 1992. 4.3. Surface energy fluxes Surface energy fluxes (latent and sensible heat) estimated from Landsat images from 1998 to 2002 indicated the dynamics of the energy fluxes as a function of landcover (vegetation) and weather variables. The sensible heat fluxes have increased compared to the reduction in the latent heat flux especially around developed areas surrounding the Red River. Figs. 12 and 13 show spatial maps of the sensible and latent heat fluxes from 1998 to 2002. The sensible heat estimates were higher for 2001 and 2002 compared to 1998 and 2000, indicating more partitioning of the net radiation into sensible heat. The latent heat fluxes were higher in 1998, 2002 and in the eastern half of the study area in 2001 compared to 2000. 4.4. Runoff response analysis

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5. Conclusions and recommendations MIR and NDVI from Landsat MSS, and thermal maps and NDVI from Landsat TM and ETM+ sensors enhance land-cover and land surface microclimate mapping. The results show that MIR, LST and NDVI significantly helped discrimination of land-cover classes. In general, mid-morning surface temperature is inversely proportional to NDVI, a measure of plant biomass and condition. Boundaries between vegetated wetlands and free water bodies, agriculture and forest, urban and built-up areas, and barrenland and clouds were easily drawn. The MIR versus NDVI relationships have positive slopes and are also useful for discriminating landcover classes. Over the period of 28 years, the land-cover and surface microclimate parameters in the Red River Valley have changed. Cropland agriculture and urban built-up areas have increased, with most of the increase occurring in the 1990s. Spatial surface energy fluxes (sensible and latent heat) mapped from TM and ETM+ sensors also showed spatiotemporal variations in response to landcover and surface microclimate changes. The runoff response analysis of the study area indicated snowmelt and rainfall on wet soils dictate much of the runoff in the Red River Valley. Hence, partitioning of the surface energy fluxes plays an important role in determining the extent and rate of water flux. Future studies need to assess the surface parameters incorporating more years of data for better understanding and capturing of the full picture of the processes.

Acknowledgements

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The monthly precipitation (rainfall and snow) and discharge data from 1974 to 2002 are shown in Fig. 14. Fig. 14 indicates an increase in precipitation and the corresponding discharge to the river in recent years (1993–2002). The increase in the precipitation is driven by the regional weather pattern which produced higher volumes of rainfall and snowfall. The trend lines in Fig. 16 indicate a linear increase in precipitation and runoff from 1974 to 2002. The monthly hydrograph analysis for each year of study (Fig. 15) indicates that, with the exception of 2002, flow peaks in early spring before or at the beginning of the rainy season, implying snowmelt and rainfall on saturated soils cause much of the flow. This indicates energy flux partitioning (sensible and latent heat) affects the extent of flow to the river. Flow depth (area under the hydrograph divided by the drainage area of the gauging station) and percent runoff (flow divided by precipitation) is shown in Fig. 16. The linear trends in Fig. 16 show increases in pre-

cipitation and runoff from 1974 to 2002. The variable x in the trend line equation is the number of years count beginning in 1974. Since 1993, precipitation and flow have increased: 40% of the years since 1993 have more than 15% of runoff volume, compared to 10.5% of the years during 1974–1992. After 1993 all years had percent runoff greater than 10%, compared with 35% of the years from 1974 to 1992. This indicates the runoff response of the basin has increased significantly after 1993, with greater than 10% of the precipitation converted to runoff.

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Fig. 16. Precipitation, runoff and percent runoff from 1974 to 2002.

The author acknowledges George Seielstad for reviewing the manuscript, and Vijay Nangia, David Baumgartner, Grant Casady, Ofer Beeri, and other members of Upper Midwest Aerospace Consortium for their help in the data analysis. The author extends his appreciation to the USGS of North Dakota and North Dakota State Water Commission for providing some of


their data. The research was partially funded by NASA grant NAG 13-02047.

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