Research On Grassland Biomass And Carbon Storage Monitoring By Remote Sensing Based On '3S' Technology

Grassland ecosystem is the largest terrestrial ecosystem of China. Grassland productivity is thematerial base for maintaining grassland ecosystem, then the most direct index to reflect grassland statusand the decisive role to function of grassland ecosystem. It has been the important and hot spot ofterrestrial ecology to research grassland biomass. It is the important significance for sustainable usingand management of grassland to accurately and rapidly investigate the temporal-spatial distribution ofgrassland production and master the dynamic law of grassland. At present, whether on the large scale orglobal scale and the regional or grassland type to estimate grassland biomass and carbon storage, thedata of modeling and verification is less. It is difficult to ensure the reliability and precision of model.The relative deficiency of belowground biomass has long been one of the weak links of ecological study.Especially, there is a lack of systemic and through study on the relationship between below-andabove-ground biomass and their dynamic change under different utilization types and grasslanddegradation and desertification.In this study, we selected Zhenglan Banner, Inner Mongolia as our study area. Based on '3S'technology method and special database, we built Remote Sensing estimation model for grasslandbiomass. We illustrated temporal-spatial distribution of grassland biomass during2005to2010, theninvestigated the main control factors of temporal-spatial distribution of grassland biomass, and revealedabove-and below-ground biomass and allocation, then further estimated grassland biomass and carbonstorage under the different desertification grade. The main results and progress are summarized asfollows:(1) The estimating model of aboveground biomass based on RS was established by traditionalexperience statistical method and B-P neural network method using ground quadrat biomass data.Through precision verification, the optimal model was B-P neural network model and model precisionwas79%. By B-P neural network model, grassland aboveground biomass was estimated at annual mean798,620ton, biomass density at797.36kg/hm~2. In region spatial distribution pattern was "middle higherand sides lower". Precipitation was one of key factors for temporal distribution of grassland biomass.(2) Based on TM image at Zhenglan Banner in2010, referencing for the index system of grasslanddesertification grade, grassland was divided into nonsandy grassland, slight type grassland, moderatetype grassland, severe type grassland. Classification precision was80%. The results showed thatgrassland desertification mainly centralized on Northern region. Desertification area was4,483.95km~2,accounted for45.88%of total grassland area.(3) Mean aboveground biomass density of nonsandy grassland, slight type grassland, moderatetype grassland and severe type grassland was estimated at214.72g/m~2,135.41g/m~2,91.42g/m~2,24.01g/m~2, respectively. Mean belowground biomass density was estimated at2601.35g/m~2,2318.45g/m~2,413.25g/m~2,117.25g/m~2, respectively. The ratio of below-and above-ground biomass was between4.5and17.2. With aggravation of desertification degree, it made the soil texture coursing and soil fertility reduced. Then it inhibited vegetation growth and reduced grassland biomass.(4) On the basis of grassland desertification classification and the estimation of abovegroundbiomass by Remote Sensing, combined with the ratio of below-and above-ground biomass, grasslandbiomass and carbon storage were estimated at7.55million ton and3.4TgC, respectively. Carbondensity was between0.99MgC/hm~2and11.12MgC/hm~2. Among them, mean carbon density ofnonsandy grassland was at4.39MgC/hm~2, carbon storage was at2.31TgC. Mean carbon density ofslight type grassland was at5.61MgC/hm~2, carbon storage was at0.55TgC. Mean carbon density ofmoderate type grassland was at1.58MgC/hm~2, carbon storage was at0.33TgC. Mean carbon density ofsevere type grassland was at1.53MgC/hm~2, carbon storage was at0.22TgC. The above resultsindicated that grassland desertification has significantly negative effect of carbon fixation. Theaggravation of the desertification may lead to reduce the ability of storage carbon.(5) In order to complete all the above research objects and contents, taking annual meantemperature and precipitation in2010for example, selecting51meteorological station data aroundZhenglan Banner and comparing analysis of advantages and disadvantages of kriging interpolation andANUSPLIN interpolation method, we finally chose ANUSPLIN interpolation method to calculate eachmeteorological element(including annual mean temperature, annual mean precipitation, growing seasontemperature, growing season precipitation) of Zhenglan Banner during2005to2010. Annual meantemperature was between1.3℃and3.9℃, annual mean precipitation was between200mm and450mm,inter-annual temperature and precipitation had large fluctuation. Growing season temperature wasbetween13.5℃and18.5℃. The change range was smaller in different years. Growing seasonprecipitation was between130mm and380mm. It accounted for more80%of total precipitation.There are innovations in tow aspects as follows:(1) Through comprehensively main factors effecting on grassland biomass, the estimating model ofgrassland biomass by Remote Sensing was established using B-P neural network method. This papermade a profound analysis on space-time dynamic change of grassland biomass during2005to2010.Meanwhile, six years continuous observation data was selected to establish model. It reduced thefluctuation infuluences of biomass annually and effectively improved the stability of model.(2) Referencing for the index system of grassland desertification grade, grassland was divided intononsandy grassland, slight type grassland, moderate type grassland, severe type grassland. Weinvestigated above-and below-ground biomass and allocation on the different desertification grade, thenestimated grass carbon storage on the different desertification grade, and further revealed the effect oncarbon cycle in grassland ecosystem and improved estimation accuracy of grassland biomass and carbonstorage.