Evaluating the Conservation Efforts of Multi-Projects Using Remote Sensing and Light Use Efficiency Model: A Case of Nyungwe Forest National Park, Rwanda

This paper investigates the effectiveness of conservation efforts in the Nyungwe Forest National Park (Nyungwe). The forest is one of the six key landscapes identified for conservation in the Albertine Rift because it hosts many threatened species. As such, a number of different stakeholders have been involved in its conservation since 1987;yet, studies that emphasize and evaluate the success of these conservation efforts are limited. We combined a rapid and relatively low cost remotely-sensed data and the Light Use Efficiency model to generate forest conservation indicators such as NDVI, forest canopy Net Primary Productivity and carbon sequestered from 1986 to 2010. The influence of topographic and climatic factors on these indicators was examined. The supervised classifier was used to catalogue the area into Forest, Wetland, and Bareland. The forest was the major category (above 90%) of Nyungwe relative to wetland and bareland. Based on degradation intensity, two distinctive periods were realised;the first period spans 8 years (1986-1994) whereas the second spans 16 years (1994-2010). The former degradation intensity period is 10 times higher than the latter period. Although the size of forest recovered up to 90%, the daily NPP and carbon sequestration capacity decreased by 37.1% (i.e. NPP 6.5 Mg tons in 1986 to 4.1 Mg tons in 2010). Areas of the forest that are physically constrained (high altitude) had a higher degradation. Guided by our indicators, there is an overall success in conservation efforts, but efforts were mostly concentrated in accessible areas. Therefore, conservation efforts that aim to respond to degradation of the inaccessible areas of the forest should be stressed in the management plan of the park.

Simulating Alpine Vegetation Net Primary Productivity by Remote Sensing in Qinghai Province,China

Primary productivity of ecosystem is important indicator about ecological assessment. Remote sensing technology has been used to monitor net primary productivity （NPP） of ecological system for several years. In this paper, the remotely sensed NPP simulation model of alpine vegetation in Qinghai Province of Tibet Plateau was set up based on the theory of light use efficiency. Firstly a new approach based on mixed pixels and Support Vector Machine （SVM） algorithm were used to correct simulated NPP values derived from Moderate Resolution Imaging Spectroradiometer （MODIS） data. Finally, spatial distribution and monthly variation characteristics of NPP in Qinghai Province detail. The result showed in 2006 were analyzed in that NPP of vegetation in Qinghai Province in 2006 ranged from o to 422 gC/m2/a and the average NPP was 151 gC/m2/a. NPP gradually increased from northwest to southeast. NPP of different vegetation types were obviously different. The average NPP of broad-leaved forest was the largest （314 gC/m2/a）, and sparse shrub was the smallest （101 gC/m2/a）. NPP in Qinghai Province significantly changed with seasonal variation. The accumulation of NPP was primarily in the period （from April to September） with better moist and heat conditions. In July, the average NPP of vegetation reached the maximum value （43 gC/m2）. In our model, the advantage of traditional LUE models was adopted, and our study fully considered typicalcharacteristics of alpine vegetation light use efficiency and environmental factors in the study area. Alpine vegetation is the most important ecological resource of Tibet Plateau, exactly monitoring its NPP value by remote sensing is an effective protection measure.

Spatial scaling of net primary productivity model based on remote sensing

Spatial scaling for net primary productivity （NPP） refers to the transferring process of establishing quantitative correlation between simulated NPP derived from data at different spatial resolutions. How to transfer NPP at one scale by the algorithm with smaller error to at another is the urgent problem. Nonlinearity and effects from land cover type are two main problems in NPP scaling. In this paper, the contextural approach based on mixed pixels and support vector machine （SVM） algorithm are used to make the scaling model from the fine resolution （TM） to the coarse resolution （MODIS）. Spatial scaling from NPP retrieved from fine resolution data to NPP derived from coarse resolution images is performed, and the correction of scale effect to NPP retrieved from coarse resolution data of MODIS is accomplished. The result shows that the correlation between Rj_coereted of the correction factor for scale effect and 1-Fmiddle dessity grassland estimated by SVM regression model is higher （R2=0.81）. Before the correction for scale effect, the correlation between NPPMODIS and NPPTM is lower （R2=0.69; RMSE=3.47）, while the correlation between NPPTM and corrected NPPMODIS_corrected is higher （R2=0.84; RMSE= 1.87）. Therefore, NPP corrected for scale effect has been greatly improved in both correlation and error.

叶绿素荧光及碳氧同位素信号在光合作用研究中的应用

叶绿素a荧光和碳氧稳定同位素信号常用于研究光合作用的光能及水分利用效率。本文综述了叶片光合作用过程中的叶绿素荧光和碳氧稳定同位素信号的机理和研究进展,并阐述了叶片内部光环境和二氧化碳浓度的不均一性对这两种信号的影响,最后我们在展望中建议建立基于叶片三维结构及光合机理的模型,以支持对叶绿素荧光及稳定同位素信号的物理解释,促进其在光合作用研究中的应用。

不同叶面积指数遥感数据模拟中国总初级生产力的时空差异

陆地生态系统总初级生产力(GPP)反映了植物吸收固定大气中CO2的能力,是碳循环过程中的重要环节。光能利用率(LUE)模型被广泛应用于GPP模拟。叶面积指数(LAI)数据是LUE模型的重要输入数据,不同的LAI数据差异较大,从而导致GPP模拟存在很大差异。利用3种常用的卫星遥感LAI数据(MCD15、GLASS和GlobMap)和气象数据模拟中国2003~2017年的GPP,比较了3种LAI数据在中国区域的时空差异,分析不同LAI数据模拟的中国GPP的时空差异。研究结果表明:3种LAI数据在中国区域的年平均值和LAI变化趋势的空间分布格局存在明显差异,森林区域的差异较大;2003~2017年间,中国区域3种LAI年平均值均呈显著增加趋势(p<0.01),但不同LAI数据年平均值的年际变化差异明显;站点尺度GLASS LAI模拟的GPP与观测值相关性较好;不同LAI数据模拟的中国GPP总量多年平均值差异明显,最大值为7.46 Pg C a-1(GLASS),最小值为6.39 Pg C a-1(GlobMap);3种LAI数据模拟的中国GPP总量在2003~2017年呈显著增加趋势(p<0.05),但不同的LAI数据模拟的中国GPP年总量的年际变化差异明显;不同LAI数据模拟的年均GPP和GPP变化趋势的空间分布格局存在明显差异,森林和农田区域的差异较大。研究结果有助于评估由于LAI数据造成的区域GPP模拟结果的不确定性。

日光诱导叶绿素荧光估算中国典型生态系统总初级生产力的能力

陆地生态系统总初级生产力(GPP,Gross Primary Productivity)是陆地生态系统碳循环的重要分量,提高其估算精度具有重要的科学意义。由于受多种因子的影响,GPP的时空变异明显,其估算结果存在较大的不确定性。日光诱导叶绿素荧光(SIF,Sun-Induced Chlorophyll Fluorescence)与GPP密切相关,近年来被应用于估算区域和全球GPP,但其在中国生态系统的适用性尚不清楚。以中国8个典型植被生态系统为研究对象,驱动两叶光能利用率模型(TL-LUE,TwoLeaf Light Use Efficiency Model)模拟以站点为中心0.5°×0.5°区域内的月GPP,验证SIF估算GPP的能力。结果表明,SIF具有监测中国典型植被生态系统GPP的能力,月SIF与TL-LUE模拟的月GPP之间显著相关,其中5个生态系统中两者的R2高于0.8,最高达到0.91,GPP与SIF变化的斜率随生态系统类型变化。模拟的GPP与SIF遥感数据的季节变化特征相同,两者之间的一致性在生长季节好于非生长季节;SIF能更好地监测农田GPP的季节变化。

Potential promoted productivity and spatial patterns of medium-and low-yield cropland land in China

With a continuously increasing population and better food consumption levels, im- proving the efficiency of arable land use and increasing its productivity have become funda- mental strategies to meet the growing food security needs in China. A spatial distribution map of medium- and low-yield cropland is necessary to implement plans for cropland improvement In this study, we developed a new method to identify high-, medium-, and low-yield cropland from Moderate Resolution Imaging Spectroradiometer （MODIS） data at a spatial resolution of 500 m. The method could be used to reflect the regional heterogeneity of cropland productiv- ity because the classification standard was based on the regionalization of cropping systems in China. The results showed that the proportion of high-, medium-, and low-yield cropland in China was 21%, 39%, and 40%, respectively. About 75% of the low-yield cropland was lo- cated in hilly and mountainous areas, and about 53% of the high-yield cropland was located in plain areas. The five provinces with the largest area of high-yield cropland were all located in the Huang-Huai-Hai region, and the area amounted to 42% of the national high-yield cropland area. Meanwhile, the proportion of high-yield cropland was lower than 15% in Hei- Iongjiang, Sichuan, and Inner Mongolia, which had the largest area allocated to cropland in China. If all the medium-yield cropland could be improved to the productive level of high-yield cropland and the low-yield cropland could be improved to the level of medium-yield cropland, the total productivity of the land would increase 19% and 24%, respectively.

Large-scale estimates of gross primary production on the Qinghai-Tibet plateau based on remote sensing data

Vegetation gross primary production(GPP)is an important variable for the carbon cycle on the Qinghai-Tibetan Plateau(QTP).Based on the measurements from 12 eddy covariance flux sites,we validated a light use efficiency model(i.e.EC-LUE)to evaluate the spatial-temporal patterns of GPP and the effect of environmental variables on QTP.In general,EC-LUE model performed well in predicting GPP at different time scale over QTP.Annual GPP over the entire QTP ranged from 575 to 703 Tg C,and showed a significantly increasing trend from 1982 to 2013.However,there were large spatial heterogeneities in long-term trends of GPP.Throughout the entire QTP,air temperature increase had a greater influence than solar radiation and precipitation(PREC)changes on productivity.Moreover,our results highlight the large uncertainties of previous GPP estimates due to insufficient parameterization and validations.When compared with GPP estimates of the EC-LUE model,most Coupled Model Intercomparison Project(CMIP5)GPP products overestimate the magnitude and increasing trends of regional GPP,which potentially impact the feedback of ecosystems to regional climate changes.

Estimation of Terrestrial Net Primary Productivity in China from Fengyun-3D Satellite Data

Currently,the satellite data used to estimate terrestrial net primary productivity(NPP)in China are predominantly from foreign satellites,and very few studies have based their estimates on data from China’s Fengyun satellites.Moreover,despite their importance,the influence of land cover types and the normalized difference vegetation index(NDVI)on NPP estimation has not been clarified.This study employs the Carnegie–Ames–Stanford approach(CASA)model to compute the fraction of absorbed photosynthetically active radiation and the maximum light use efficiency suitable for the main vegetation types in China in accordance with the finer resolution observation and monitoring-global land cover(FROM-GLC)classification product.Then,the NPP is estimated from the Fengyun-3D(FY-3D)data and compared with the Moderate Resolution Imaging Spectroradiometer(MODIS)NPP product.The FY-3D NPP is also validated with existing research results and historical field-measured NPP data.In addition,the effects of land cover types and the NDVI on NPP estimation are analyzed.The results show that the CASA model and the FY-3D satellite data estimate an average NPP of 441.2 g C m^(−2) yr^(−1) in 2019 for China’s terrestrial vegetation,while the total NPP is 3.19 Pg C yr^(−1).Compared with the MODIS NPP,the FY-3D NPP is overestimated in areas of low vegetation productivity and is underestimated in high-productivity areas.These discrepancies are largely due to the differences between the FY-3D NDVI and MODIS NDVI.Compared with historical field-measured data,the FY-3D NPP estimation results outperformed the MODIS NPP results,although the deviation between the FY-3D NPP estimate and the in-situ measurement was large and may exceed 20%at the pixel scale.The land cover types and the NDVI significantly affected the spatial distribution of NPP and accounted for NPP deviations of 17.0%and 18.1%,respectively.Additionally,the total deviation resulting from the two factors reached 29.5%.These results show that accurate NDVI products and land cover types are important prerequisites for NPP estimation.