Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China

Zhao, Fen; Xu, Bin; Xiao, Yang; Jin, Ye; Li, Jinya; Xia, Lang; Shi, Chuang; Ma, Hailong

doi:10.3390/rs6065368

Cited by 100 publications

(59 citation statements)

References 25 publications

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“…NDVI has been known for monitoring vegetation growth status and the estimation of biomass accumulation [60][61][62]. Precipitation also significantly influences the spatial and temporal dynamics of NDVI in which the biomass accumulation follows the precipitation gradient [63].…”

Section: Ndvi Precipitation Correlation Analysismentioning

confidence: 99%

Identifying Categorical Land Use Transition and Land Degradation in Northwestern Drylands of Ethiopia

Zewdie

Csaplovics

2016

Remote Sensing

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Land use transition in dryland ecosystems is one of the major driving forces to landscape change that directly impacts the welfare of humans. In this study, the support vector machine (SVM) classification algorithm and cross tabulation matrix analysis are used to identify systematic and random processes of change. The magnitude and prevailing signals of land use transitions are assessed taking into account net change and swap change. Moreover, spatiotemporal patterns and the relationship of precipitation and the Normalized Difference Vegetation Index (NDVI) are explored to evaluate landscape degradation. The assessment showed that 44% of net change and about 54% of total change occurred during the study period, with the latter being due to swap change. The conversion of over 39% of woodland to cropland accounts for the existence of the highest loss of valuable ecosystem of the region. The spatial relationship of NDVI and precipitation also showed R 2 of below 0.5 over 55% of the landscape with no significant changes in the precipitation trend, thus representing an indicative symptom of land degradation. This in-depth analysis of random and systematic landscape change is crucial for designing policy intervention to halt woodland degradation in this fragile environment.

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Section: Ndvi Precipitation Correlation Analysismentioning

confidence: 99%

Identifying Categorical Land Use Transition and Land Degradation in Northwestern Drylands of Ethiopia

Zewdie

Csaplovics

2016

Remote Sensing

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“…The NPP is the stored carbon after subtracting the maintenance and growth respiration from the total amount of organic matter fixed by plants [23,24]. It can be utilized as a measure of crop yield, grass biomass, and forest productivity [25].…”

Section: Npp Datamentioning

confidence: 99%

“…The algorithm of MOD17A2 NPP relies on the basic light-use efficiency (LUE) model. The MOD17A2 NPP data are computed from the PAR, fraction of absorbed photosynthetically active radiation (fAPAR) and leaf area index (LAI) obtained from another MODIS product (MOD15A2), meteorological data from the Data Assimilation Office (DAO) and biome-wise coefficients of biome properties lookup table (the BPLUT) [23].…”

Section: Npp Datamentioning

confidence: 99%

Spatio-Temporal Variation in AOD and Correlation Analysis with PAR and NPP in China from 2001 to 2017

Liang

Cheng

2020

Remote Sensing

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Atmospheric aerosols can elicit variations in how much solar radiation reaches the ground surface due to scattering and absorption, which may affect plant photosynthesis and carbon uptake in terrestrial ecosystems. In this study, the spatio-temporal variations in aerosol optical depth (AOD) are compared with that in photosynthetically active radiation (PAR) and net primary productivity (NPP) during 2001-2017 in China using multiple remote sensing data. The correlations between them are analyzed at different scales. Overall, the AOD exhibited a northeast-to-southwest decreasing pattern in space. A national increasing trend of 0.004 year −1 and a declining trend of −0.007 year −1 of AOD are observed during 2001-2008 and 2009-2017. The direct PAR (PAR dir ) and diffuse PAR (PAR dif ) present consistent and opposite tendency with AOD during two periods, respectively. The total PAR (PAR total ) shows a similar variation pattern with PAR dir . In terms of annual variation, the peaks of AOD coincide with the peaks of PAR dif and the troughs of PAR dir , indicating that aerosols have a significant positive impact on PAR dir and a negative impact on PAR dif . Furthermore, the PAR dir has a stronger negative association with AOD than the positive correlation between PAR dif and AOD at national and regional scales, indicating that PAR dir is more sensitive to aerosol changes. The NPP has higher values in the east than in the west and exhibits a significant increasing trend of 0.035 gCm −2 day −1 after 2008. The NPP has a negative correlation (−0.4-0) with AOD and PAR dif and a positive correlation (0-0.4) with PAR dir in most areas of China. The area covered by forests has the highest NPP-PAR correlation, indicating that NPP in forests is more sensitive to the PAR than is the NPP in grasslands and croplands. This study is beneficial for interpreting the aerosol-induced PAR impact on plant growth and for predicting plant production on haze days.Many studies have been conducted in terms of the spatio-temporal variations in AOD in different scales and time periods over the past decades. He, et al.[5] studied the AOD trend in China during 2002-2015 and analyzed the impact factors including terrain, vegetation and socio-economic indices. That study showed an increasing trend in AOD during 2002-2007 and a decreasing trend during 2008-2015. In addition, AOD was found to have a weak relation with NDVI and a strong relation with elevation and population. Wang, et al. [6] calculated the trend in AOD in north China in the period of 2001-2016 and found various trends in different regions. By using MODIS and MERRA-2 datasets from 1980 to 2017, Qin, et al. [7] found an increasing trend in southeastern China and a decrease trend in northernmost China. That study also showed that the dust and SO 4 are the main constituents of aerosols. et al. [8] analyzed the AOD trend over the global ocean during 2000-2015.Results showed AOD has a trend of 0.002 decade −1 and is highly correlated with shortwave aerosol radiative effect (SWARE), which ...

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“…High-resolution airborne images have been used for ground cover estimation in forests, and grasslands [19,20] and for plant disease monitoring in a range of crops [21]. They are also used to estimate the impact of natural disasters [22], forest vegetation classification [23] and to estimate grassland distribution [24].…”

Section: Introductionmentioning

confidence: 99%

An Object-Based Image Analysis Approach to Assess Persistence of Perennial Ryegrass (Lolium perenne L.) in Pasture Breeding

et al. 2019

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Perennial ryegrass (Lolium perenne L.) is one of the most important forage grass species in temperate regions of the world, but it is prone to having poor persistence due to the incidence of abiotic and biotic stresses. This creates a challenge for livestock producers to use their agricultural lands more productively and intensively within sustainable limits. Breeding perennial ryegrass cultivars that are both productive and persistent is a target of forage breeding programs and will allow farmers to select appropriate cultivars to deliver the highest profitability over the lifetime of a sward. Conventional methods for the estimation of pasture persistence depend on manual ground cover estimation or counting the number of surviving plants or tillers in a given area. Those methods are subjective, time-consuming and/or labour intensive. This study aimed to develop a phenomic method to evaluate the persistence of perennial ryegrass cultivars in field plots. Data acquisition was conducted three years after sowing to estimate the persistence of perennial ryegrass using high-resolution aerial-based multispectral and ground-based red, green and blue(RGB) sensors, and subsequent image analysis. There was a strong positive relationship between manual ground cover and sensor-based ground cover estimates (p < 0.001). Although the manual plant count was positively correlated with sensor-based ground cover (p < 0.001) intra-plot plant size variation influenced the strength of this relationship. We conclude that object-based ground cover estimation is most suitable for use in large-scale breeding programs due to its higher accuracy, efficiency and repeatability. With further development, this technique could be used to assess temporal changes of perennial ryegrass persistence in experimental studies and on a farm scale.

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Remote Sensing Estimates of Grassland Aboveground Biomass Based on MODIS Net Primary Productivity (NPP): A Case Study in the Xilingol Grassland of Northern China

Cited by 100 publications

References 25 publications

Identifying Categorical Land Use Transition and Land Degradation in Northwestern Drylands of Ethiopia

Identifying Categorical Land Use Transition and Land Degradation in Northwestern Drylands of Ethiopia

Spatio-Temporal Variation in AOD and Correlation Analysis with PAR and NPP in China from 2001 to 2017

An Object-Based Image Analysis Approach to Assess Persistence of Perennial Ryegrass (Lolium perenne L.) in Pasture Breeding

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