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Assessing the impact of urban land

Assessing the impact of urban land development on net primary

productivity in the southeastern United States

Cristina Milesi a,*,Christopher D.Elvidge b ,Ramakrishna R.Nemani a ,Steven W.Running a

Numerical Terradynamic Simulation Group,School of Forestry,University of Montana,Missoula,MT 59812,USA

Office of the Director,NOAA/National Geophysical Data Center,325Broadway,Boulder,CO,USA

Received 25March 2002;received in revised form 19August 2002;accepted 19August 2002

Abstract

The southeastern United States (SE-US)has undergone one of the highest rates of landscape changes in the country due to changing demographics and land use practices over the last few decades.Increasing evidence indicates that these changes have impacted mesoscale weather patterns,biodiversity and water resources.Since the Southeast has one of the highest rates of land productivity in the nation,it is important to monitor the effects of such changes regularly.Here,we propose a remote sensing based methodology to estimate regional impacts of urban land development on ecosystem structure and function.As an indicator of ecosystem functioning,we chose net primary productivity (NPP),which is now routinely estimated from the MODerate resolution Imaging Spectroradiometer (MODIS)data.We used the MODIS data,a 1992Landsat-based land cover map and nighttime data derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS)for the years 1992/1993and 2000to estimate the extent of urban development and its impact on NPP.The analysis based on the nighttime data indicated that in 1992/1993,urban areas amounted to 4.5%of the total land surface of the region.In the year 2000,the nighttime data showed an increase in urban development for the southeastern United States of 1.9%.Estimates derived from the MODIS data indicated that land cover changes due to urban development that took place during the 1992–2000period reduced annual NPP of the southeastern United States by 0.4%.Despite the uncertainties in sensor fusion and the coarse resolution of the data used in this study,results show that the combination of MODIS products such as NPP with nighttime data could provide rapid assessment of urban land cover changes and their impacts on regional ecosystem resources.D 2003Elsevier Science Inc.All rights reserved.

Keywords:DMSP/OLS;MODIS;Net primary productivity;Southeastern United States;Urbanization

1.Introduction

Land development in the United States is proceeding rapidly,at a rate faster than population growth (Heimlich &Anderson,2001;U.S.Department of Housing and Urban Development,2000;U.S.Environmental Protection Agency,2000),to accommodate the space demands of an affluent society.From an ecological perspective,land development is one of the most disturbing processes since it dramatically alters the natural energy and material cycles of ecosystems (Berry,1990;McDonnell et al.,1997;Oke,1989;Pielke et al.,1999).For example,the carbon cycle is altered due to the

subtraction of developed land from the photosynthetic proc-ess and the increase in CO 2emissions from fossil energy use in urban areas.While not all of the land in urban areas is paved,it has been shown that at least in the less resource-limited regions of the United States (eastern and south-eastern),urbanization lowers the photosynthetic activity of the landscape (Imhoff,Tucker,Lawrence,&Stutzer,2000).This observation is particularly relevant when considering that urbanization in the United States occurs preferentially where the soils are most productive (Imhoff,Lawrence,Elvidge,et al.,1997),thereby causing a loss of prime farmland.

In recent years,growth in population size and land occupation has been higher than the national average in the southeastern United States (SE-US)where strong eco-nomic forces are reshaping the landscape through urban-ization (U.S.Bureau of the Census,2001).These forces are significantly fragmenting the landscape of this traditionally

*Corresponding author.

E-mail addresses:milesi@https://www.wendangku.net/doc/f66013136.html, (https://www.wendangku.net/doc/f66013136.html,esi),chris.elvidge@https://www.wendangku.net/doc/f66013136.html, (C.D.Elvidge),

nemani@https://www.wendangku.net/doc/f66013136.html, (R.R.Nemani),swr@https://www.wendangku.net/doc/f66013136.html, (S.W.Running).

https://www.wendangku.net/doc/f66013136.html,/locate/rse

Remote Sensing of Environment 86(2003)401

–410

rural region,which also hosts among the most productive forests of the United States(Wear&Greis,2001).Because of its important ecological resources,the SE-US represents the ideal study area in which to develop a remote sensing based methodology for a regional assessment of the effects of land cover changes,in particular urban land development, on ecosystem resources.Earlier studies on the impact of land cover changes on ecosystem resources have been conducted at either global scales(using coarse resolution data sets)or over small regions(DeFries,Field,Fung, Collatz,&Bounoua,1999;Houghton,Hackler,&Law-rence,1999;Imhoff et al.,2000;Paruelo,Burke,&Lauen-roth,2001).A methodology that is consistent across various spatial scales would provide an ideal tool allowing resource managers to map and monitor the impacts of land cover changes.

The recent availability of remote sensing data from the MODerate resolution Imaging Spectroradiometer(MODIS) sensor on-board TERRA(EOS-AM1)platform offers an improved opportunity to monitor ecosystem resources and functioning at regional to global scales.Similarly,improve-ments to the Defense Meteorological Satellite Program’s Operational Linescan System(DMSP/OLS)based night-time data(Elvidge et al.,2001)also allow us to track changes in human settlements.In this study,we explore the combination of the MODIS and DMSP/OLS data sets to assess the impacts of urban development on net primary productivity(NPP)in the SE-US.NPP,the amount of carbon fixed by plants,represents an integrative descriptor of ecosystem functioning and resources because it modu-lates a number of other ecosystem services ranging from freshwater availability to biodiversity(Field,2001; McNaughton,Oesterheld,Frank,&Williams,1989).

Specifically,we address the following issues:(1)What is the extent of recent intensification of urban land develop-ment?(2)How has the urban land development impacted regional NPP?

2.Study area

The region examined in this work includes the states of Tennessee,Mississippi,Alabama,North Carolina,South Carolina,Georgia and Florida.These states occupy the southeastern portion of the United States and are charac-terized by a mild wet climate,with an average annual temperature of17j C and annual precipitation greater than 1300mm.The climate has favored,over time,intense agricultural exploitation,intense timber exploitation and currently,through a strong economic growth,population and urbanization(Alig&Healy,1987).According to the latest U.S.Census,over49million people were living in these seven states in2000,20%more than in1990.It is expected that between1992and2020,urban areas in the South will more than double in extent(Wear&Greis, 2001).3.Methods

Our methodology used MODIS,DMSP/OLS and Land-sat data organized in a geographic information system (GIS).All the data were reproduced at1km of spatial resolution and projected to Lambert Azimuthal Equal Area.

A high resolution land cover map and nighttime imagery from the DMSP/OLS for the years1992/1993and2000 were used to describe the land cover changes that have taken place in the SE-US as a consequence of recent urban land development.We used NPP from MODIS,estimated using MODIS-derived Leaf Area Index/Fraction of Photo-synthetic Active Radiation absorbed by vegetation(LAI/ FPAR)and climate data.We estimated NPP contributions from each land cover type in the1992land cover map. Using the1992land cover as a template,we also identified the surface of each land cover type that has been recently converted into urban use,as inferred from1992/1993to 2000nighttime data change detection.Finally,we esti-mated the impact of this recent development on the regional NPP as a sum of losses in NPP from each land cover type.

3.1.Mapping southeastern land cover

Predominant land cover types for the SE-US were derived from the1992National Land Cover Data set (NLCD)(Vogelmann et al.,2001).This data set was produced at30m of spatial resolution from Landsat Thematic Mapper images acquired in the early1990s and other sources of digital data,mapping21land cover classes for the conterminous United States.Overall accu-racy for the eastern United States was assessed to be81% for Anderson level I aggregations(i.e.water,urban, barren land,forest,agricultural land,wetland,rangeland; Anderson,Hardy,Roach,&Witmer,1976),and60%for all21land cover classes(V ogelmann et al.,2001).The NLCD data set exists both in the native30-m resolution and in a multilayer1-km resolution(one layer for each land cover class),in which each pixel reports the per-centage land cover type occupied in the square kilometer unit.

We needed a land cover map both to track land cover changes due to recent urban sprawl in the SE-US and to guide the estimation of NPP from MODIS data.Since the MODIS algorithm for the calculation of NPP requires a map of canopy functional types,we grouped the21original land cover types from the1-km resolution product into eight classes,namely,urban,crops,deciduous broadleaf forest, evergreen needleleaf forest,mixed forest(deciduous broad-leaf and evergreen needleleaf),grassland,shrubland and barren.We then assigned each square kilometer to the dominant land cover in the pixel(i.e.to the land cover occupying the largest fraction)(Fig.1).The surface fraction occupied by each land cover on a state by state basis is reported in Table1.

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3.2.Mapping developed land with nighttime data Land cover data sets such as the NLCD are useful for several regional and national management applications.However,the large amount of effort involved in their production limits their updating to not more than once every 10years.While this time scale is more than appropriate for a number of applications,the monitoring of urban growth may require higher frequency assessments in rapidly devel-oping regions.The nighttime imagery from the DMSP/OLS provides the tool for timely and inexpensive monitoring of human settlements (Elvidge et al.,1999;Elvidge,Baugh,Kihn,Kroehl,&Davis,1997).These data have previously been used to map urbanization in the United States (Imhoff,Lawrence,Elvidge,et al.,1997;Imhoff,Lawrence,Stutzer,

&Elvidge,1997),to estimate population (Sutton,Roberts,Elvidge,&Baugh,2001;Sutton,Roberts,Elvidge,&Meij,1997)and to indicate energy consumption and greenhouse gas emissions (Doll,Muller,&Elvidge,2000;Elvidge,Baugh,Kihns,et al.,1997).At night,the DMSP/OLS sensor operates at high sensitivity in the visible–near infrared portions of the electromagnetic spectrum (0.44–0.94A m)and is able to detect even faint light emissions from human activity on the Earth.The data are recorded on a 6-bit scale with a nominal spatial resolution of 2.7km.

In this paper,we used the DMSP/OLS nighttime data to estimate the extent of recent (1992/1993–2000)urban land development in the SE-US.For this purpose,we used average digital number (DN)nighttime lights from cloud-free portions of orbits collected during the dark portions of the lunar cycles during September,October and November of 1992/1993and 2000.The averaged nighttime lights have a spatial resolution of 1km.The basic procedure for producing a cloud-free composite for the average DN for lights of each time period (1992/1993and 2000)can be found in Elvidge,Baugh,Kihn,et al.(1997).

The resulting average DN nighttime lights could not be used directly to estimate the extent of urban land develop-ment in the study area since this would produce an over-estimate.The use of the raw average DN data tends to overestimate the size of small towns due to several factors,including (1)the large size of the OLS pixel footprint;(2)wide overlap in the footprints of adjacent pixels;(3)accu-mulation of geolocation errors;and (4)possible inclusion of scattered light due to fog,clouds or haze.We experimentally applied different DN thresholds to the 1992/1993DMSP/OLS data,below which all the pixels were zeroed.For each threshold,we compared the total lit area to the total urban area by state from the 1-km land cover derived from the 1-km NLCD data,from the original (30m)NLCD data and from 1990U.S.Census tabular data.The statistics obtained for a set of thresholds are reported in Table 2.

The total urban area from the NLCD data was derived aggregating the following NLCD classes:low density res-idential,high density residential,commercial/industrial/transportation and urban/recreational grasses.Then,the percentages of total urban area for each 1-km unit were summed up on a state by state basis.The computation

Table 1

Percent fractions of land cover classes for the seven southeastern states reported from the 1-km land cover State Urban (%)Crops (%)Deciduous forest (%)Evergreen forest (%)Mixed forest (%)Grassland (%)Shrubland (%)Transitional (%)Alabama 1.823.735.618.818.70.1– 1.4Florida 10.913.025.823.2 1.021.60.2 3.0Georgia 3.531.232.925.0 4.4 1.2– 1.8Mississippi 1.439.330.521.3 6.10.3– 1.1N.Carolina 4.928.147.416.1 1.90.7–0.6S.Carolina 4.127.332.231.60.6 2.3– 1.5Tennessee 3.435.953.5 3.4 3.30.1–0.3SE-US

4.4

27.9

36.4

19.8

5.4

4.2

0.02

1.4

Fig.1.A 1-km land cover derived from the 1992NLCD data set (30m)by assigning each pixel to the dominant land cover within the 1-km unit.The borders of the seven states included in the SE-US region analyzed in this study are imposed on the land cover.AL =Alabama;FL =Florida;GA=Georgia;MS =Mississippi;NC =North Carolina;SC =South Caro-lina;TN =Tennessee.

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indicated that in 1992,the urban areas in the SE-US occupied 33,916.5m 2(4%of the total land surface).The total urban area from the 1990U.S.Census was obtained from tabular data listing the land area of populated places in 1990on a state by state basis (U.S.Bureau of the Census,1996).The definition of ‘urban’for the 1990U.S.Census included all the urbanized areas and places with a popula-tion of more than 2500.The sum of the land area of all the places with 2500or more people yielded a total urban area

for the region of 42,493.1km 2(about 4.9%of the total land surface).

A number of issues challenged our selection of an ap-propriate threshold for the 1992/1993DMSP data.One of these issues was the large disagreement (more than 8500km 2)between the total urban area estimated from the NLCD data and from the 1990U.S.Census.The 1992land cover we derived from the NLCD data set,by assigning each 1-km pixel to the dominant land cover in the unit,estimated a total

Table 2

Comparison of urban area estimates for the seven states of the southeastern U.S.from DMSP data (threshold at digital number (DN)greater or equal to 48,49,50,51and 52,respectively),from the 1-km land cover,the original NLCD data and from U.S.Bureau of the Census

DMSP threshold,DN z 48(km 2)

DMSP threshold,DN z 49(km 2)DMSP threshold,DN z 50(km 2)DMSP threshold,DN z 51(km 2)DMSP threshold,DN z 52(km 2)Land cover,1km (km 2)NLCD,30m (km 2)

U.S.Census y (km 2)

1992/1993DMSP data Alabama 3967382136883522337923532274.17111.6Florida 13,49113,19812,90912,61712,29415,94713,086.912,318Georgia 6992676465726376614952974364.85745.2Mississippi 1837175516641584148416961770.72931N.Carolina 6362612758935634544862545756.14956.5S.Carolina 3820365734943338318932752954.22954.2Tennessee 5051487046974524434636563709.86476.6SE-US Total 41,52040,19238,91737,59536,28938,47833,916.542,493.1%urban

4.8

4.6

4.5

4.3

4.2

4.4

3.9

4.9

Threshold DMSP https://www.wendangku.net/doc/f66013136.html,nd cover,1km *àb 1.29 1.20 1.32

1.34 1.36

àa à2042

à1933à1817.3à1693à1588.4àR 20.9560.95760.95770.9580.9595

Threshold DMSP vs.NLCD,30m *àb 1.01 1.02

1.04 1.06 1.08àa à1121.7

à1034.7à941.7à841.6à759.2àR 20.9490.95030.950.95010.9512

Threshold DMSP vs.U.S.Census *àb 0.7510.770.780.800.81àa 1607.8

1667.51727.71798.61860.9àR 20.77990.78290.78610.78770.7885t -test,P -value DMSP https://www.wendangku.net/doc/f66013136.html,nd Cover,1km 0.45540.67620.91670.8380.6264

2000DMSP data Alabama 583856245421521250168372.1Florida 17,26316,90016,53816,18515,79815,890.9Georgia 10,21799269662937190607278.6Mississippi 324230992963281826793872.6N.Carolina 960192698933862982917306.1S.Carolina 585356375417520250043390.6Tennessee 701767806542629660927951.0SE-US Total 59,03057,23555,47653,71351,94054,061.9%urban

6.9 6.6 6.4 6.2 6.0 6.2

Threshold DMSP vs.U.S.Census *àb 0.800.81

0.830.840.86àa 969

1063.31157.41264.11339.8àR 20.7918

0.79390.7960.79770.8011

*b is slope of the linear regression and a is the intercept.y

U.S.Census data refer to the year 1990for the comparison with 1992/1993DMSP data set and to the year 2000for the comparison with the 2000DMSP data set.

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urban area of38,478km2,a value in between the ones estimated from the original NLCD and from the Census. Since we used this land cover to drive the MODIS algo-rithms for the estimation of FPAR/LAI and NPP,we decided to use it as the reference layer to compare the total lit area from the DMSP at different thresholds.We estimated the best correspondence by linear regression and with a t-test for significant differences.The best correspondence between the total lit area from the1992/1993DMSP data and the current land cover was shown for a threshold at a value of DN greater than or equal to50.While lit area estimates from other thresholds all displayed similar R2by linear regres-sion,the estimates from the DMSP with a threshold of DN z50proved to be the least significantly different from the1-km land cover estimates of urban area(paired t-test, hypothesized difference=0,alpha=0.05,t-value=0.1091, P-value=0.9167,N=7).The1992/1993total lit area of all the pixel with a DN z50was38,917km2(4.5%of the total land surface),and its geographical distribution is shown in Fig.2a.An alternative to this approach would be the selection of a pair of thresholds,to provide a lower and an upper estimate of the extent of urban area and its impact on NPP.

To determine the extent of total urban area from the2000 average DN nighttime lights,we applied the same threshold of DN z50,obtaining a total urbanized surface of55,476 km2(6.4%of the total surface)(Fig.2b).The only data set we had available to verify this value was the total land area of all incorporated urban areas and places with2500or more people from the County and City Data Book2000(U.S. Bureau of the Census,2000).According to the U.S.Census data,the total urbanized surface for the SE-US region for the year2000is54,061.9km2.While the total estimates are similar for the two methods,wide discrepancies between the state by state estimates exist,especially in the case of Alabama.

A difference image between the nighttime averages of 2000and1992/1993thresholded at DN z50was used to estimate the distribution of recent land development in the SE-US.While most of the change in nighttime lights over the study area has taken place in the form of an intensification of human activity in areas already developed by1992,we took into account only those pixels that had no nighttime activity in1992/1993and were lit in2000.The total land developed during1992/1993–2000according to our esti-mate amounted to16,559km2(1.9%increase)(Fig.2c).

3.3.Estimation of land cover change effects on primary productivity

One year’s worth of MODIS NPP(2001)data are available and distributed by the EROS Data Center Dis-tributed Active Archive Center(EDC DAAC)with the product name of MOD17.However,in this data set,urban areas,along with water bodies,are masked out.As a consequence,we needed to generate our own NPP for the study area.To generate LAI and FPAR(MOD15),the main inputs to the NPP algorithm,we used the MODIS Normal-ized Difference Vegetation Index(NDVI)(MOD13)and the MOD15backup algorithm.We downloaded the available MOD13(NDVI)data for the year2001from the EDC DAAC.These data represent16-day composites of atmos-pherically corrected maximum NDVI and Enhanced Vege-tation Index(EVI)at1km of spatial resolution.For a detailed description of the data,see Huete,Justice,and van Leeuwen(1999).In order to cover the study area,three MODIS tiles were required,each covering a ground area of 1200?1200km.We produced a mosaic of the three NDVI tiles for each of the20available biweekly composites and reprojected the composites to Lambert Azimuthal Equal Area from the original Integerized Sinusoidal Projection.

The MOD15backup algorithm uses empirical relations between NDVI and LAI and FPAR derived for various land cover types(Knyazikhin et al.,1999;Myneni,Nemani,& Running,1997).We used the1992land cover map to guide the estimation of LAI/FPAR after rearranging the classes from the perspective of the radiative transfer theory.While urban areas can have substantial amounts of forest vegeta-tion,at the resolution of1km2,most of the urban cover is a mosaic of trees with grass underneath and buildings.As

a Fig.2.Average nighttime lights with digital numbers greater or equal to50for(a)1992/1993,(b)2000and(c)difference between2000and1992/1993. 1=Atlanta,GA;2=Nashville,TN;3=Atlanta,GA to Greensboro,NC;4=Florida.

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consequence,we considered appropriate to assign the urban class to the savanna biome,defined as a two-layer canopy with an overstory of trees and an understory of grasses (Myneni et al.,1997).The land cover class of mixed forest is also not contemplated as an independent structural type from the radiative transfer theory perspective.Thus,we ran the algorithm twice over the pixels classified as mixed forest,once considering them as broadleaf forests and once as needleleaf forests.Assuming that broadleaf and needle-leaf species were present in equal proportions in the land cover class of mixed forest,we averaged the LAI/FPAR values of the two runs.

The algorithm used to produce MODIS NPP is shown in Fig.3(Running,Nemani,Glassy,&Thornton,1999; Running,Thornton,Nemani,&Glassy,2000).The algo-rithm is based on a light use efficiency logic that relates Absorbed Photosynthetic Active Radiation(APAR)to Gross Primary Productivity(GPP),where APAR=FPAR*IPAR (IPAR=Incident Photosynthetic Active Radiation).e max is a biome-specific light use efficiency factor that is modified into e by daily meteorological conditions(minimum temper-ature and vapor pressure deficit).e is used to convert APAR to GPP.NPP is obtained by subtracting maintenance respi-ration(MR)and growth respiration(GR)components from living tissue material from the annual integral of https://www.wendangku.net/doc/f66013136.html,I is used to compute living biomass,which is a key compo-nent of respiration estimation.The meteorological condi-tions and the IPAR required for the NPP calculation were derived as an average of the1980–19971-km spatially interpolated surface weather observations(Thornton,Run-ning,&White,1997).

An estimate of total NPP for the1992land cover could be obtained in two ways:(1)as a spatially explicit summa-tion of the NPP values derived for each pixel or(2)by multiplying,for each state,the number of pixels in each land cover by its mean NPP.Because of uncertainties related with the land cover accuracy and with the fusion of data from different sources,we applied the second method.Mean NPP for each1992land cover class was obtained as an arithmetic average of the total NPP by land cover category in each state,including only those pixels that were not lit in the 2000average nighttime data with a threshold of DN z50. We calculated an estimate of NPP also for the barren category,since in the SE-US,this land cover type

is Fig. 3.The MODIS productivity logic has three key components:(1)remote sensing inputs(land cover,FPAR,LAI),(2)daily surface weather (IPAR=Incident Photosynthetic Active Radiation,T min=minimum daily temperature,T avg=daily average temperature estimated from T min and T max,and VPD=Vapor Pressure Deficit),(3)a look-up-table containing biome-specific coefficients(e max,biometry,leaf longevity and those used in respiration) generated from an ecosystem model.Based on the land cover,a characteristic radiation conversion efficiency parameter(e max)is extracted from a lookup table. T min and VPD are used to attenuate e max to produce e,which is then used with the Absorbed Photosynthetic Active Radiation(APAR)to predict daily Gross Primary Productivity(GPP=e*APAR,where APAR=IPAR*FPAR).Specific Leaf Area(SLA)determined by land cover is used to estimate leaf mass from LAI.Fine root mass is assumed to be in a constant fraction of leaf mass for each land cover.In an annual time step logic,annual leaf mass is assessed from daily leaf mass and used to estimate annual average live wood mass.Maintenance respiration(MR)costs of leaf,fine root and live wood mass are calculated daily as exponential functions of daily average temperature(T avg).Leaf longevity from a lookup table is used to determine annual leaf growth and,through allometric relationships,annual fine root and wood growth and the associated annual growth respiration(GR)costs.Final estimation of annual Net Primary Productivity (NPP)is obtained by subtracting the annual integral of daily MR and annual GR from the annual integral of daily GPP.

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represented mainly by a transitional class.The NLCD land cover defines transitional those areas with sparse vegetation (less than25%of cover)that are dynamically changing from one land cover to another,often because of land use activities(i.e.forest clearcuts,transition between forest and agricultural land,etc.).We assumed that the transitional pixel was previously covered with the second most domi-nant cover in the1-km unit.The mean NPP of the transi-tional pixel was then assigned to be25%of the mean NPP of the second most dominant cover.For example,if the second largest cover in the pixel was deciduous broadleaf, we assumed the transitional pixel to be a clearcut of this forest type and its mean NPP equals to25%of the mean NPP of the deciduous forest.While these assumptions might be incorrect,they do not have a large impact on the total NPP because of the small surface occupied by this category.

An estimate of the NPP of the recently developed areas was obtained by multiplying the mean NPP for the urban category of each state by the number of newly developed pixels in the state that were not classified as urban in the 1992land cover.

4.Results and discussion

4.1.Changes in land cover due to urban sprawl

Fig.2shows the1992/1993and2000average nighttime lights with a threshold of DN z50and a difference image between the two periods.The difference image provides an estimate of the most intense land development occurring during the1990s in the SE-US.It indicates that most of the newly developed land is located at the periphery of the largest urban areas,as already demonstrated in other regions of the United States by Imhoff et al.(2000).Large develop-ment is present around Atlanta,GA,from Atlanta,GA,to Greensboro,NC,around Nashville,TN,and in Florida.It cannot be denied that in the recent years,human presence has significantly increased in the SE-US much beyond the urban fringe.A comparison between the raw1992/1993and the2000nighttime averages indicates a dramatic increase in the presence of lower intensity nighttime lights in the countryside,much higher than the one seen after applying the threshold.While a portion of these low density lights may be attributed to error,we think that most of them are the result of the significant fragmentation process that is taking place in the SE-US landscape.Even if the low-intensity nighttime lights in these rural areas are effectively due to the presence of a built-up structure,we assumed that they would probably occupy a very low fraction of the1-km pixel, therefore not significantly impacting the regional NPP.

In Table3,we report the surface in each land cover class that,according to the nighttime lights difference between the2000and1992/1993,showed the most substantial increase in human activity.According to our estimates, between1992and2000,land development has irreversibly transformed about1.9%of the SE-US.The largest increase in lit surface was recorded for the states of Georgia,Florida, North Carolina and South Carolina.It should be noted that a substantial portion of newly lit areas matches with land already classified as urban in the1992land cover.There are a number of factors that could explain this inconsistency;an inappropriate choice of threshold for the DMSP/OLS data would be a significant factor.As listed in Table2,the total urban area for the SE-US estimated from the DMSP/OLS with a threshold of DN z50is similar to the estimate derived from the1992land cover,but large differences can occur on a state by state basis.For example,the1992/ 1993DMSP data estimate a urban surface of12,909km2for the state of Florida,a value3038km2lower than the figure from the1992land cover,which probably is overestimating the real value.The selected threshold for the DMSP/OLS data estimates a smaller urban area than the1992land cover also for the states of Mississippi and North Carolina. Another factor contributing to the inconsistency could be related to the preparation of the1-km land cover from the NLCD data.Assigning the pixels to the dominant cover in the1-km unit could overestimate the total urban surface if the built-up surface in the pixel was larger than any other cover but less than50%.Finally,the inconsistency could be due to geolocation errors in the DMSP data or to inaccuracy of the NLCD cover.

Overall,overlaying the nighttime change image with the current biome land cover map indicates that most of the new development(50%)is due to the conversion of forest,in particular deciduous broadleaf forest,which is the dominant

Table3

Surface area developed between1992/1993and2000and percent fractions of total land area based on change detection of thresholded DMSP/OLS data for the two composite periods

State Urban

(km2)Crops

(km2)

Deciduous

forest(km2)

Evergreen

forest(km2)

Mixed

forest(km2)

Grassland

(km2)

Shrubland

(km2)

Transitional

(km2)

Total

(km2)

Fraction

(%)

Alabama1605525732202167–51773 1.3 Florida1183444809511106502203629 2.5 Georgia348611128446334617–212739 2.0 Mississippi2095163132103413–41090 1.1 N.Carolina6217131136520326–122419 2.4

S.Carolina325542495472273–141597 2.4 Tennessee23381864249990–41612 1.7

SE-US307941965252244573976628016,559 1.9

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forest type in the region.About 25%of the new develop-ment resulted from the conversion of cropland and almost 5%from the conversion of grassland (mainly in Florida).These statistics are in agreement with the results presented in the Southern Forest Assessment Draft Report (Wear &Greis,2001).According to the same report,this conversion of forest area into developed land is counterbalanced by a conversion of cropland into forest use.Therefore,it is actually the extent of croplands that is being reduced in the SE-US.

4.2.Effects of land cover change on regional primary productivity

Table 4reports mean and total NPP estimates for each of the land covers.The 1992total NPP for the SE-US is estimated to be 872.57Tg (Teragrams,1012g)of carbon per year,68%in forests and grasslands,28%in crops and about 3%in urban areas.For the urban class,Table 4shows also the percentage of urban area covered by tree canopies,as measured by Dwyer,Nowak,Noble,and Sisinni (2000).Urban areas retain high primary productivity,which corre-lates well with the tree cover.The highest urban productivity per unit area is reported for Georgia (848gC m à2y à1),which also presents the highest urban tree cover in the nation.Another factor contributing to this high productivity is the presence of parks and golf courses,which tend to be intensively managed with irrigation and fertilizers.Golf courses are particularly numerous in this region,which is also one of the prime North American retirees destinations.Table 5shows the estimates of NPP loss due to new development as estimated from the change detection anal-ysis of the nighttime imagery.We considered that no loss took place over those areas that appear as newly urbanized

from the nighttime lights change detection but were already classified as urban in the 1992land cover.The average loss in annual NPP per unit area is 183g of carbon per square meter.The total loss amounts to 3.04Tg of carbon per year,0.35%of the total NPP in 1992and apparently due to about 1.9%increase in the urban surface.This seems a modest loss in NPP and in carbon sequestration potential,probably contained by fertilization and irrigation of the urban vege-tation.However,this loss becomes relevant if we consider that it is accompanied by an increase in emissions of CO 2due to the significant growth in population of the SE-US during the years between 1990and 2000.This growth,according to the U.S.Census,amounted to almost 8.2million people,a number equal to the current population of Georgia.

It is also important to understand that changes in land cover due to urban sprawl add to the other changes in land cover that took place in the SE-US,which have left unaltered very little of the original vegetation in the region.Most of the original mixed forest has been replaced by deciduous or evergreen stands for industrial timber produc-

Table 4

Estimates of mean and total NPP by current land cover types for the seven southeastern states State

Urban*

Crops

Deciduous forest Evergreen forest Mixed forest Grassland

Shrubland

Transitional

Total

Mean NPP (gC m à2y à1)Alabama 800(48.2)993106510851115335–269Florida 749(18.4)106697510381056888844234Georgia 848(55.3)1120108110831057712–266Mississippi 765(38.6)958102310771117675–263N.Carolina 798(42.9)104010329971068361–241S.Carolina 789(39.8)1089105310041047585–254Tennessee 759(43.9)905987

1016

1002

844–231

Total NPP (TgC y à1)Alabama 1.8831.5150.7727.2827.890.05–0.50139.88Florida 11.9520.2236.6935.09 1.5127.980.18 1.01134.63Georgia 4.4953.0854.1541.147.04 1.28–0.74161.92Mississippi 1.3046.4738.4428.308.420.22–0.35123.50N.Carolina 5.0037.2562.3920.40 2.580.34–0.18128.14S.Carolina 2.5823.8027.1225.400.53 1.08–0.3180.82Tennessee 2.7835.5457.77 3.82 3.650.06–0.07103.69SE-US

29.98

247.87327.33181.4151.6231.010.18 3.16872.57

*Between parentheses is the urban tree cover,in percent,reported by Dwyer et al.,2000.

Table 5

Estimates of NPP lost due to estimated development between 1992/1993and 2000State Unit loss in NPP (gC m à2y à1)Total loss in NPP (TgC y à1)Alabama 2210.38Florida 1530.55Georgia 2040.63Mississippi 1960.26N.Carolina 1780.54S.Carolina 1940.37Tennessee 1630.30SE-US

183

3.04

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408

tion and by cropland.Crops,which contribute to almost a third of the primary productivity,are characterized by fast carbon cycling due to annual or biannual harvest.The harvesting of crops is also associated with carbon release from soils.Similarly,the turnover of the carbon from the timber plantation has been accelerated,also reducing the potential for long-term carbon sequestration.

The loss in NPP due to development could be reduced with intensive urban forestry programs,as shown for the state of Georgia,which has been the most successful in maintaining a high urban tree cover.Trees not only seques-ter more carbon per unit area than grasses,but also contrib-ute to reduce surface runoff and evapotranspiration from irrigated lawns.Other benefits(Nowak&Crane,2000) include emission reductions from air conditioners(trees provide shade to buildings)and general mitigation of the urban heat island effect.Urban forests also increase oppor-tunities for wildlife survival and reduce land fragmentation.

5.Conclusions

The southeastern states,ecologically important because of their high primary productivity,are undergoing rapid changes in land use and land cover as a result of rapid population growth.In the context of characterizing and quantifying these changes,recent advances in remotely sensed data offer a valuable tool.The integration of advanced products such as NPP from satellite data with a nighttime light map allows not only rapidly monitoring changes in human settlements,but also estimating their impacts on ecosystem resources.Much of the data used in this study is readily available to the public, and should therefore be a valuable resource for communities world-wide.

The results presented in this paper provide a coarse assessment of the extent of urban sprawl and its impact on NPP in the SE-US.The spatial resolution and uncertainties of the input data limit the accuracy of the results.Nevertheless,it provides a methodology for understanding regional effects of urbanization on primary productivity.

Other MODIS products that are routinely produced and may be of use in urban studies include surface albedo, surface temperature and aerosol concentration.Their inte-gration with the LAI/NPP products could provide useful information of the impact of urban areas on energy effi-ciency and other ecosystem variables.Though not widely available as yet,MODIS also produces250m of NDVI data,from which higher resolution NPP could be derived. Once the processing is streamlined,it should vastly enhance the potential for urban studies.

Acknowledgements

This work was supported by funding from NASA,Earth System Science Fellowship Program to https://www.wendangku.net/doc/f66013136.html,esi,and grants from Land Cover Change and EOS/MODIS pro-grams to C.D.Elvidge,R.R.Nemani and S.W.Running.We would like to thank Dr.Mark Imhoff,Dr.Paul Sutton and an anonymous reviewer whose comments lead to significant improvements in this manuscript.We are grateful to Eva Karau and Alana Oakins for editing the manuscript. References

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(完整版)新人教版Book4Unit2WorkingtheLand课文

Unit 2 Working the land Part I a Pioneer for all People Although he is one of China’s most famous scientists, Yuan Longping consider s himself a farmer, for he works the land to do his research. Indeed, his sunburnt face and arms and his slim, strong body are just like those of millions of Chinese farmers, for whom he has struggle d for the past five decade s. Dr Yuan Longping grows what is called super hybrid rice. In 1974, he became the first agricultural pioneer in the world to grow rice that has a high output. This special strain of rice makes it possible to produce one-third more of the crop in the same field s. Now more than 60% of the rice produced in China each year is from this hybrid strain. Born in 1930, Dr Yuan graduate d from Southwest Agricultural College in 1953. Since then, finding ways to grow more rice has been his life goal. As a young man, he saw the great need for increasing the rice output. At that time, hunger was a disturbing problem in many parts of the countryside. Dr Yuan search ed for a way to increase rice harvest s without expand ing the area of the fields. In 1950, Chinese farmers could produce only fifty million tons of rice. In a recent harvest, however, nearly two hundred million tons of rice was produce d. These increased harvests mean that 22% of the world’s people are fed from just 7% of the farmland in China. Dr Yuan is now circulating his knowledge in India, Vietnam and many other less developed countries to increase their rice harvests. Thanks to his research, the UN has more tool s in the battle to rid the world of hunger. Using his hybrid rice, farmers are producing harvests twice as large as before. Dr Yuan is quite satisfied with his life. However, he doesn’t care about being famous. He feel s it gives him less freedom to do his research. He would much rather keep time for his hobbies. He enjoy s listening to violin music, playing mah-jong, swimming and reading. Spend ing money on himself or lead ing a comfortable life also means very little to him. Indeed, he believe s that a person with too much money has more rather than fewer trouble s. He therefore gives millions of yuan to equip others for their research in agriculture. Just dream ing for things, however, cost s nothing. Long ago Dr yuan had a dream about rice plants as tall as sorghum. Each ear of rice was as big as an ear of corn and each grain of rice was as huge as a peanut. Dr Yuan awoke from his dream with the hope of producing a kind of rice that could feed more people. Now, many years later, Dr Yuan has another dream: to export his rice so that it can be grown around the globe. One dream is not always enough, especially for a person who loves and cares for his people.

Unit2Workingtheland

Unit2Workingtheland unit 2 working the land teaching aims:1. target languagea.words and phrasessunburn, struggle, super, expand, circulate, equip, export, rid ... of, be satisfied with, lead a ... life, search for, would rather, thanks to, with the hope of, rather than b. important sentencesthis special strain of rice makes it possible to produce one-third more of the crop in the same fields. p10he cares little about spending the money on himself or leading a comfortable life. p102. ability goals enable ss to learn more about agriculture, countryside and farming. by talking they can exchange their experience with each other. by reading they will realize the role that agriculture plays in human life. in fact this world faces a serious problem —starvation. so after reading the passage about dr yuan students will know the importance of his achievement to man. of course they will learn from dr yuan some noble character.3. learning ability goals help ss learn how to describe dr yuan longping including his personality.teaching important points a. help to comprehend the text and grasp the main idea of the text.b. grasp the usage of some words and expressions.c. how to

Unit2 Working the land 教案

Unit2Working the land Teaching objectives 1.Knowledge objectives (1).important words Sunburt.decade.super.output.crop.hunger.disturb.expand.circulate. Vietnam.battle.freedom.therefore.equip.grain.export. (2).important phrases Struggle for, Thanks to, Ri d…of, Be satisfied with, would… rather. (3).important sentence pattern Finding ways to grow more rice has been his life goal. Just dreaming for things, however, costs nothing. 2. Ability objectives Improve students’ reading ability through reading activities. 3. Affective objectives (1) After learning the passage, students are expected to know about Yuan Longping and his quality. (2)Enable students to know Dr Yuan’s key secret to success. Teaching important points 1. List the words .phases and sentences listed above. 2. Enable students to improve their reading comprehension. Teaching difficult points

Unit2《Working_the_land》教案

Unit2 Working the land Period 1 Warming up and pre-reading. Step 1 Lead-in. Poem By Li Shen Farmers weeding at noon,Sweat down the field soon. Who knows food on a tray,Due to their toiling day. Then let one student recite the poem in Chinese. Step 2 Warming up by questioning Hello, everyone. We shall read about man who works the land today. Have you ever grown any plants? If not, what kind of plant would you like to grow? How will you grow it? (For reference: Mr. Li, I worked with my father in the rice field last year. We grow hybrid rice and use animal wastes to make the soil rich.) Has anyone of you ever been to the countryside? What did you do there? (For reference: I went to Chuankou the day before yesterday. It is a small mountain village 75 li n orth of Beijing. I went there to visit my uncle’s family. I like that small beautiful village very much. ) Who are from a farmer’s family? What do you know about farming? Step 3 Pre-reading and talking Questioning and answering Rice is main food in South China. What do you think would happen if tomorrow there was suddenly no rice to eat?

UnitWorkingtheland练习题及答案

U n i t W o r k i n g t h e l a n d练习题及答案集团标准化工作小组 [Q8QX9QT-X8QQB8Q8-NQ8QJ8-M8QMN]

Unit 2 Working the land 一. 单词和短语翻译（每小题1分） 1. 发现，发觉 2. 国籍 3. 毕业于 4. 生产，制造 5. 坚持；要求 6. 评论，议论二. 用所给词的适当形式填空（共10小题；每小题1分，满分10分） 1. It is no use_____________ ( complain ). 2. We succeeded in____________ (finish) the task ahead of time. 3. His ____________ (late) for class made his teacher very angry. 4. John finished_____________ (read) the book yesterday. 5. _____________ (collect) information is very important to businessmen. 6. Tom could not help____________ (jump) when he heard the news. 7. I should say sorry to Kate. I regret____________ (refuse) to help her that day. 8. Everybody was made unhappy because he insisted on____________ (stop) the work. 9. __________ (do) morning exercises is very important to us. 10. It is not worth___________ (see) the movie for the second time. 三. 单词拼写和短语填空。根据下列句子及所缺单词的首字母或汉语意思，写出单词的正确形式。（共27小题；每空1分，满分31分） 1. A c________ change takes place in paper when it burns. 2. We must r________ ourselves of these old ideas. 3. Not having had food for over forty hours, we were all weak from h_________. 4. The baby is hungry but it is too young to ______ (喂养) itself yet. 5. It was a new form of ________ (细菌) and nobody knew how it would affect humans. 6. The cookies are made from g________ and fruit. 7. Farmers consider rabbits to be p________, because they destroy some crops they grow. 8. The violent film is not s________ for children. 9. This food provides the ________(营养) your dog needs. 10. The average o ________ of the factory is 20 cars a day. 11. Many African children die of h_________ because of lack of food. 12. Sydney's population e__________rapidly in the 1960s. 13. Look, the fish are s__________for survival because the water level has dropped in the lake. 14. Yesterday 1 bought 10 flavors of ice-cream---enough to s______my roommates. 15. I am a bit c _ . Is that her husband or her son she is with

Unit 2 working the land基础测试题

Unit 2 working the land基础测试题姓名________ 班级__________ 得分______________ 一．单词拼写（每个1分，共15分） 1. He ________________(挣扎) to his feet and dragged slowly ahead. 2. The________________(令人不安的) news made him disturbed. 3. He is ________________(迷惑) about his future. 4. He________________(后悔) that he had missed the lecture by Professor Smith. 5. Water ________________(膨胀) when it freezes. 6. The minister(部长) refused to ________________(作出评论) on this accident. 7. Great changes have taken place in our school in the past two ________________(十年). 8. Over the past half century, using ________________(化学的)fertilizers has become very common in farming. 9. The ________________(发现) of new land made Columbus(哥伦布) world-famous. 10. (饥饿) is the best sauce. 11. Her shoulders were badly (晒伤). 12. Anot her (超级) skyscraper is being built. 13. Manufacturing (产量) has increased by 8%. 14. Farmers produce millions of tons of g to feed the nation. 15. Would you mind writing a s of the passage? About 200 words are OK. 二、完成句子（每个空1分，共30分） 1. 袁隆平认为自己是个农民，因为他在田里耕作，进行科学研究。 Yuan Longping _________ himself ___ _______, for he works the land to do his research. 2. 多亏了他的研究，联合国在消除世界饥饿的战斗中有了更多的办法。 ______ ____his research, the UN has more tools in the battle to _____ the world _______hunger. 3. 袁博士很满意他的生活。 Dr Yuan is quite _______ ____ his life. 4. 他宁愿把时间花在自己的业余爱好上。 He _________much _________ keep time for his hobbles. 5. 在自己身上花钱或者享受舒适的生活对袁博士来说意义不大。 Spending money on himself or ______ ____ _______ _______ also means very little to him. 6. 事实上，他认为一个人有了太多钱，他的麻烦事只会更多，而不是更少。Indeed, he believes that a person with too much money has more_______ _______ fewer troubles. 7. 食物中的化学成分会在人体中堆积。 These chemicals in the food supply ______ ___ in people's bodies over time. 8. 很多化学成分能导致癌症或其它疾病。 Many of these chemicals can______ ___ cancer or other illnesses. 9. 农民关心的是保持土壤肥沃并且免受病害。

Unit 2 Working the land教案

Unit 2 Working the land教案 Unit 2 Working the land The First Period Reading Teaching goalablleaabout agriculture, countryside and farming. Help Ss learn how to describe Dr Yuan Longping includingalTeaching important and difficula. Held the text and graain ideaxt. b. Grasp the usagwords and exHow to help students leaabout agricultuTeaching methods Talking, qug-and-answering activity and reading. Teaching aids A tader, aand a compuⅠ Greeting and leadingT: Hello, ev: Hello, teaT: In last unit we leagreat women. Today we’ll learn a famous man. Who will it be? Alet’s look at the two pictures on Page 9. What ale doing? Ss: They are plantingT: Can you tellg about rice? S: Rice growu: Bulawe can also findT: Yeah. You are right. In faa cereal grain that has been grown for at least 5,000 years and is eaten by 2.4 billle everyday throughout the world. In Australia, farmers

Workingtheland优秀教案

个人收集整理仅供参考学习 Unit 2 Working the land Period 4 Using language: Extensive reading Teaching aims: I. Topics: Chemical or organic farming II. Useful words and expressions: Chemical, production, bacteria, pest, nutrition, mineral, discovery, focus, soil, reduce, root, skim, underline, summary, comment, build up, lead to, focus on, keep...free from/ of b5E2RGbCAP III. Ability and emotion 1. Develop Ss’ reading skills by extensive reading and enable them to learn how to use different reading skills to read different reading materials.p1EanqFDPw 2. Have Ss tell about modern agriculture and organic farming.DXDiTa9E3d 3. Let Ss have a better understanding of organic farming and pay attention to the quality of food we eat.RTCrpUDGiT Step1 Leading in Ask Ss what they know about organic farming. Collect their ideas on the blackboard. 5PCzVD7HxA Step 2 Skimming Ask Ss to skim the passage and find the main idea of this passage and each paragraph.jLBHrnAILg Paragraph&passage Main idea Paragraph 1 Paragraph 2 Paragraph 3 Paragraph 4 Passage Step 3 Scanning 1 Ask Ss to scan the passage to locate particular information and answer the following questions in Exercise 1 an then fill in the form about the methods and advantages in Exercise 2. xHAQX74J0X Methods of organic farming Advantages of methods 1.Farmers use natural waste from animal. This makes the soil richer in minerals and so more fertile. 1 / 7

高二英语：Working the land教学设计

新修订高中阶段原创精品配套教材 Working the land 教材定制 / 提高课堂效率 /内容可修改 Working the land 教师：风老师风顺第二中学编订：FoonShion教育

Working the land unit 2 working the land 核心单词 1. struggle v.挣扎；努力；拼搏；斗争 n. (为争取自由、政治权利等而进行的)斗争，奋斗常用结构： struggle with与……斗争 struggle for 为争取……而斗争 struggle against与……斗争；为反对……而斗争struggle to do sth. (=make great efforts to do sth.) 努力做某事 struggle to one’s feet 挣扎着站起来 she struggled to keep back the tears. 她努力忍住泪水。it was a hard struggle to get my work done on time. 为使工作按时完成, 我做了一番努力。易混辨析 struggle/fight

struggle指较长时间的、激烈的斗争，往往指肉体及精神上的战斗。 fight意为“搏斗，打斗，打架”，表示“斗争”时，包含体力和勇猛的因素。高手过招 (1)单项填空 the working people have never stopped their struggle unfair treatment. （XX?01?山西太原五中检测） a. against b. for c. from d. to (2)完成句子（原创） ①我们应当帮助那些仍在为独立而斗争的人们。 we should help those who are still . ②他们得和各种各样的困难作斗争。 they had to . (1)解析：选a。struggle against意为“同……作斗争”；struggle for意为“为了……而斗争”。 (2)①struggling for independence ②struggle with/against all kinds of difficulties 2. expand v.扩大；扩展；增加；增长；（使）膨胀；阐述；使变大常用结构： expand...into...将……扩展/发展成……

Unit 2 Working the land单词讲解

必修四第二单元Working the land 重点单词讲解 1.struggle vt. & vi.斗争；拼搏；努力 struggle with与……斗争；和……一起战斗struggle...for 为争取……而斗争struggle...against与……斗争；为反对……而斗争struggle+不定式，如： A bird was caught in the net and was struggling to get free. 一只鸟被网罩住了，挣扎着想要逃脱。struggle to one’ s feet挣扎着站起来 a life-and-death struggle生死搏斗 struggle还可以作名词，意思是“斗争；搏斗；努力；挣扎；难事”等。如： With a struggle，he controlled his feelings. 他费力地控制住了自己的感情。 2.expand vt. & vi.使变大；伸展；阐述 expand指向四面八方的扩大扩张 extend 强调向某一方向的延长 spread 指消息，疾病，瘟疫等的传播、蔓延，也指把某物铺开，把胳膊张开【练习】用expand，extend，spread，stretch的适当形式填空。 1). The man _______ the information around. 2). The empire _______ its country in the 16th century. 3). The road builders worked hard to _______ the high way. Keys: 1). spread 2). expanded 3). extend 3.circulate vt. & vi. 循环；流传【例句】Blood circulates through the body.血液在体内循环。【考点】1）形容词：circular圆形的；循环的；名词：circulation循环；流通；发行量； 2）circulation 作“（报纸、杂志等的）发行量”解时，是可数名词。 4.thanks to thanks to 幸亏；由于；因为 1). Thanks to your help, much trouble was saved. 多亏你的帮助, 减少了许多麻烦。 2). Thanks to the bad weather, the match had been cancelled. 多亏这个倒霉天气, 比赛取消了。thanks to 相当于because of /owing to /due to /thanks to /on account of /as a result of 5.rid rid sb./sth. of... 使某人/某物摆脱…… 1). Many people are working hard to rid the world of famine. 很多人在努力使世界不再有饥荒。 2). The dentist rid him of the pain by taking out his bad tooth. 牙科医生把他的坏牙拔掉，使他免除痛苦。[重点用法] rid 短语： be rid of 摆脱get rid of 摆脱；除掉；去掉 rid a house of mice 清除室内老鼠rid oneself of debt 还清债务 [类似用法动词] inform/ warn/ cure sb. of… 通知/警告/治愈某人…… 6.be satisfied with be satisfied with = be content with 对……表示满足或满意 1). I was not satisfied with the result. 我对那个结果感到不满意。 2). You’ve done well at school. I’m very satisfied with you. 你在学校干得不错，我对你很满意。[重点用法] sth. satisfy sb. 某事使某人满意sb. is satisfied with sth. 对……表示满足或满意 be satisfied to do 对做……感到满意be satisfied that clause 对做……感到满意 a satisfied smile 满意的微笑 a satisfied customer 感到满意的顾客 feel a sense of satisfaction感到满足to sb’ s / sth’ s satisfaction 使某人满意的是 far from satisfactory 远远不能令人满足it is satisfying (to do sth) 做某事是令人满意的 a satisfactory explanation / performance令人满意的解释/演出 get/ob tain satisfaction from one’ s work 从自己的工作中得到满足 7.would rather 1). I’ d rather walk than take a bus. 我愿意走路而不愿意坐公共汽车。 2). “Some more wine?” “Thank you, I’ d rather not. I have to drive home.” [重点用法] would rather do A (than do B) = would (prefer to) do A (rather than do B) 宁愿做甲事(而不做乙事） would rather sb. did sth.宁愿某人做某事(从句用虚拟语气) 8.therefore adv.因此, 所以=for that reason=consequently常用于连接两个并列分句，其前加“and”或分号“；”。 1）He was ill, and therefore could not come. 他病了, 所以未能来。 2）He has broken his leg and therefore he can't walk.他摔坏了腿，因此不能走路了。 3）We do not have enough money. Therefore we cannot afford to buy the new car.我们的钱不够,因此买不成这辆新车。 9.equip vt. & vi.配备；装备【考点聚焦】1）名词：equipment n.[u] 装备；设备常用搭配：office equipment 办公设备sports equipment 运动器械 2）与equip相关的词组：equip with 配备…… 3）equip的过去式和过去分词都是equipped；现在分词是equipping。 10.export vt. & vi.输出；出口

必修4unit2workingtheland词汇讲解及练习

必修4 Unit2 Working the land 1 struggle [?str?gl]vi.搏斗；奋斗；努力；争取n.打斗；竞争；奋斗【例句】They had to struggle against/with all kinds of difficulties. 他们必须和各种各样的困难作斗争。 After 5 years’ of struggle,people in Wenchuan are living a normal life now.经过五年的努力，现在汶川人民的生活步入了正轨。【搭配】struggle against/with与……斗争struggle for 为争取……而斗争struggle to do 努力去做struggle to one’s feet 挣扎着站起来 a life-and-death struggle生死搏斗【辨析】battle, war, campaign, struggle, fight 这些名词均有“战斗，战争”之意。 ?battle：侧重指战争中的一次较全面、时间较长的战斗，也指陆军或海军在某一特定地区进行的战斗，或个人之间的争斗。 ?war：是战争的总称，一般指包括多个战役的大规模战争。 ?campaign：通常指在一场大的战争中在某一地区进行的一连串有既定目的的军事行动。也可作引申用。 ?struggle：指激烈或时间持续长的战斗或奋力斗争。 ?fight：最普通用词，含义广，指战斗、斗争或打斗。 2 hunger [?h??g?(r)] n.饿，饥饿；欲望vt.& vi.（使）饥饿【例句】His hunger for knowledge drove him to the library. 他对知识的强烈愿望驱使他上图书馆。 These students hunger for new knowledge and ideas. 这些学生渴望学到新知识，获得新思想。【搭配】have a hunger for 渴望hunger for 渴望得到【拓展延伸】“渴望得到某物/渴望做某事”的多种表达法归纳如下： hunger for sth. long for sth. hope for sth. wish for sth. be eager for sth. desire sth. hunger to do sth long to do sth hope to do sth wish to do sth be eager to do sth. desire to do sth. 3 expand [?k?sp?nd] vt.使…变大；扩张；详述vi.扩展；发展；张开；展开【例句】In ten years the city’s population expanded by 12%. 十年之中,该市人口增加了百分之十二。【搭配】expand...into...将……扩展/发展成……expand on 阐述；详谈【辨析】expand/extend/spread/stretch expand 意为“展开，扩大”，不仅指尺寸的增加，还可指范围和体积的扩大。 extend 意为“伸出，延伸”，指空间范围的扩大，以及长度，宽度的向外延伸，也可指时间的延长。 spread 意为“传播；蔓延；铺开”。一般指向四面八方扩大传播的范围，如传播（疾病），散布（信息）等。 stretch 意为“伸展(身体等)，拉长；连绵”，一般指由曲变直，由短变长的伸展，不是加长。【拓展延伸】expansion n.展开；膨胀；扩展expansive adj.广阔的；易膨胀的 4rid [r?d] vt.使摆脱，解除，免除【例句】You are supposed to rid yourself of carelessness,for it often leads to trouble. 你应该