Abstract
It is not yet clear whether there is any difference in using remote sensing data of different spatial resolutions and filtering methods to improve the above-ground biomass (AGB) estimation accuracy of alpine meadow grassland. In this study, field measurements of AGB and spectral data at Sangke Town, Gansu Province, China, in three years (2013–2015) are combined to construct AGB estimation models of alpine meadow grassland based on these different remotely-sensed NDVI data: MODIS, HJ-1B CCD of China and Landsat 8 OLI (denoted as NDVIMOD, NDVICCD and NDVIOLI, respectively). This study aims to investigate the estimation errors of AGB from the three satellite sensors, to examine the influence of different filtering methods on MODIS NDVI for the estimation accuracy of AGB and to evaluate the feasibility of large-scale models applied to a small area. The results showed that: (1) filtering the MODIS NDVI using the Savitzky–Golay (SG), logistic and Gaussian approaches can reduce the AGB estimation error; in particular, the SG method performs the best, with the smallest errors at both the sample plot scale (250 m × 250 m) and the entire study area (33.9% and 34.9%, respectively); (2) the optimum estimation model of grassland AGB in the study area is the exponential model based on NDVIOLI, with estimation errors of 29.1% and 30.7% at the sample plot and the study area scales, respectively; and (3) the estimation errors of grassland AGB models previously constructed at different spatial scales (the Tibetan Plateau, Gannan Prefecture and Xiahe County) are higher than those directly constructed based on the small area of this study by 11.9%–36.4% and 5.3%–29.6% at the sample plot and study area scales, respectively. This study presents an improved monitoring algorithm of alpine natural grassland AGB estimation and provides a clear direction for future improvement of the grassland AGB estimation and grassland productivity from remote sensing technology.
Highlights
As the largest terrestrial biome on the Earth’s surface [1], the grassland biome occupies approximately40% of the total land area [2], and its net primary productivity accounts for approximately 20% of Remote Sens. 2017, 9, 372; doi:10.3390/rs9040372 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 372 the production capability of the entire land biome [3]
Our results indicate that the yield per unit area estimated using the exponential model based on NDVIOLI (1518 kg DW/ha) is closest to the ground-measured value, and its estimation error is the lowest (30.7%), followed by NDVICCD (32.4%) and NDVIMOD (39.6%) (Table 7)
In this study, based on MOD13Q1, HJ-1B CCD and Landsat 8 OLI remote sensing data, grassland observation data in the Sangke grassland of Xiahe County during 2013–2015 are combined to construct a grassland above-ground biomass (AGB) estimation model based on different remote sensing data, and the influence of different filtering approaches for MODIS NDVI on the biomass estimation error of alpine meadow grassland is investigated
Summary
As the largest terrestrial biome on the Earth’s surface [1], the grassland biome occupies approximately40% of the total land area [2], and its net primary productivity accounts for approximately 20% of Remote Sens. 2017, 9, 372; doi:10.3390/rs9040372 www.mdpi.com/journal/remotesensingRemote Sens. 2017, 9, 372 the production capability of the entire land biome [3]. 40% of the total land area [2], and its net primary productivity accounts for approximately 20% of Remote Sens. Monitoring using remote sensing data is the most effective method for collecting continuous spatial and temporal data on the regional or global scale [10,11], because satellite remote sensing can provide large-scale, frequent, low cost and massive information [12]. It has gradually replaced traditional methods of ground biomass monitoring, which are inefficient and expensive. Since NDVI was first applied to study natural grasslands in the 1970s, research on the linkage between vegetation indices and AGB has had a history extending over several decades [13,14,15,16,17]
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