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首页> 外文会议>Asian conference on remote sensing;ACRS >QUANTIFYING GROSS PRIMARY PRODUCTIVITY OF AN INDIAN MANGROVE FOREST USING GEO-LEO SATELLITE DATA
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QUANTIFYING GROSS PRIMARY PRODUCTIVITY OF AN INDIAN MANGROVE FOREST USING GEO-LEO SATELLITE DATA

机译：利用GEO-LEO卫星数据量化印度红树林森林的主要生产力

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Gross primary productivity (GPP) is total carbon assimilation by plants through the process of photosynthesis. In view of increasing anthropogenic influences and global changes, quantification of carbon assimilation through photosynthesis has gained tremendous significance. Precise estimation of GPP is essential part of several ecosystem models. Mangrove ecosystem, that offers significant protection to coastal environment, is governed by changes in salinity and other micro-environment factors. Globally mangroves are facing serious threat and are undergoing degradation due to anthropogenic pressure. In view of global changes, an assessment of carbon assimilation potential of mangroves is required for developing into a carbon sink and conservation of this fragile ecosystem is highly essential. In the present study, estimation and validation of mangrove GPP was carried out in Bhitarkanika national park (Odisha, India). Light Use Efficiency (LUE) was modelled from seasonal, diurnal in-situ photosynthetic rate observations on 11 dominant mangrove species. For estimation of GPP, 'vegetation photosynthetic model' framework was modified using water, temperature and salinity scalars derived from IRS Resourcesat 2 LISS-4, a Low earth orbit (LEO) satellite data. The incident Photosynthetically Active Radiation (PAR) was derived from insolation product obtained from Geostationary (GEO) satellite KALPANA-1 VHRR for the observation period. Amongst all the species, highest LUE was found m Excoecaria agallocha in winter and summer (5 53 and 0.55 g C m~(-2) MJ~(-1), respectively), and in Aegiceras corniculatum in post-monsoon season (0.58 g C m~(-2) MJ~(-1)). Seasonal 8-day average GPP was found to vary from 3.41 g Cm~(-2) to 14.4 g C m~(-2), with the highest in winter. Comparison of modelled estimates showed fairly good agreement with MODIS GPP (r = 0.89; n=118) having comparable coefficient of variation (41.8% in modelled and 49.5% in MODIS GPP). The present modelling approach of estimating GPP through GEO-LEO satellite can be used to quantify carbon sink in other Indian mangrove ecosystems.
机译：总初级生产力（GPP）是植物通过光合作用过程吸收的总碳。鉴于越来越多的人为影响和全球变化，通过光合作用对碳同化的量化具有重要意义。 GPP的精确估算是几种生态系统模型的重要组成部分。红树林生态系统为沿海环境提供了重要的保护，它受盐度和其他微环境因素变化的制约。在全球范围内，红树林正面临严重威胁，并由于人为压力而退化。鉴于全球变化，需要评估红树林的碳同化潜力才能发展为碳汇，而保护这一脆弱的生态系统至关重要。在本研究中，红树GPP的估计和验证是在Bhitarkanika国家公园（印度奥里萨邦）进行的。光利用效率（LUE）是根据对11种主要红树林物种的季节性，每日原位光合作用速率观测结果建模的。为了估算GPP，使用了从IRS Resourcesat 2 LISS-4（低地球轨道（LEO）卫星数据）获得的水，温度和盐度标量对“植被光合作用模型”框架进行了修改。入射光合有效辐射（PAR）来源于观测期间从地球静止（GEO）卫星KALPANA-1 VHRR获得的日照产物。在所有物种中，冬季和夏季在Excoecaria agallocha中分别发现了最高的LUE（分别为5 53和0.55 g C m〜（-2）MJ〜（-1）），以及季风后季节的芒草（Aegiceras corniculatum）中的LUE最高（0.58）。 g C m〜（-2）MJ〜（-1））。季节性8天平均GPP在3.41 g Cm〜（-2）和14.4 g C m〜（-2）之间变化，冬季最高。建模估计值的比较显示，与具有可比变异系数（建模为41.8％，MODIS GPP为49.5％）的MODIS GPP（r = 0.89; n = 118）具有相当好的一致性。通过GEO-LEO卫星估算GPP的当前建模方法可用于量化其他印度红树林生态系统中的碳汇。

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