简介:Usingthemulti-temporalLandsatdataandsurveydataofnationalresources,theauthorsstudiedthedynamicsofcultivatedlandandlandcoverchangesoftypicalecologicalregionsinChina.TheresultsofinvestigationshowedthatthewholedistributionofthecultivatedlandshiftedtoNortheastandNorthwestChina,andasaresult,theecologicalqualityofcultivatedlanddroppeddown.TheseacoastandcultivatedlandintheareaofYellowRiverMouthexpandedbyanincreasingrateof0.73km?a-1,withadepositingrateof2.1km?a-1.ThedesertificationareaofthedynamicofHorqinSandyLandincreasedfrom60.02%ofthetotallandareain1970sto64.82%in1980sbutdecreasedto54.90%inearly1990s.AstothechangeofNorthTibetlakes,thewaterareaoftheNamuLakedecreasedby38.58km2fromyear1970to1988,withadecreasingrateof2.14km2?a-1.
简介:基于遥感(RemoteSensing,RS)、地理信息系统(GeographicInformationSystem,GIS)和全球定位系统(GlobalPositionSystem,GPS),即3S技术,应用面向对象的自动分类与人机交互分类结合判读方式,以SPOT、TM影像为背景,结合地面调查样地及历史资料,准确获得岗巴县草原类型空间分布现状图件及统计数据。根据本次调查结果表明:岗巴县草原总面积为365222.89hm2,占岗巴县国土面积86.94%。该县草原类型共划分4个草原大类、6个草原亚类、21个草原型。各类草原类型面积最大为高寒草原类,占总草原面积58.66%;其次是高寒草甸类,占38.61%;第三位是高寒草甸草原类占2.56%;低地草甸类面积最少,占0.18%。该县草原退化、沙化、盐渍化面积为3809.91hm2,占该县草原面积的46.36%,其中草原退化面积31.44%;草原沙化占12.96%;草原盐渍化占1.96%。草原畜牧业是该县农牧民收入的重要来源,科技与政策支持是恢复草原资源,改善生态环境及可持续发展畜牧业的重要对策。
简介:多胺是植物体内参与生长发育及各种逆境胁迫响应的一类重要化合物,其合成过程受多个基因的共同调控,S-腺苷甲硫氨酸脱羧酶(S-adenosylmethioninedecarboxylase,SAMDC)基因参与多胺的合成过程并发挥关键作用,为研究该基因在红麻(HibiscuscannabinusL.)抗旱和耐盐过程中的作用,通过红麻转录组数据库筛选到SAMDC基因的核心片段,利用染色体步移技术获得了SAMDC基因cDNA全长,命名为HcSAMDC,生物信息学分析表明:HcSAMDC基因编码序列长1137bp,编码378个氨基酸,相对分子量为41.8kD,等电点为4.86。红麻HcSAMDC与其它植物的SAMDC同源性较高,其中与可可树氨基酸相似性为78%、与棉花SAMDC的氨基酸相似性为82%,该研究对下一步分析该基因功能、阐明麻类SAMDC基因的逆境调控机制和抗逆基因工程育种具有重要意义。
简介:Weusedgeographicinformationsystemapplicationsandstatisticalanalysestoclassifyyoung,prematureforestareasinsoutheasternGeorgiausingcombineddatafromLandsatTM5satelliteimageryandgroundinventorydata.Wedefinedprematurestandsasforestswithtreesupto15yearsold.Weestimatedtheprematureforestareasusingthreemethods:maximumlikelihoodclassification(MLC),regressionanalysis,andk-nearestneighbor(kNN)modeling.Overallaccuracy(OA)ofclassifyingtheprematureforestusingMLCwas82%andtheKappacoefficientofagreementwas0.63,whichwasthehighestamongthemethodsthatwehavetested.ThekNNapproachrankedsecondinaccuracywithOAof61%andaKappacoefficientofagreementof0.22.RegressionanalysisyieldedanOAof57%andaKappacoefficientof0.14.WeconcludethatLandsatimagerycanbeeffectivelyusedforestimatingprematureforestareasincombinationwithimageprocessingclassifierssuchasMLC.