Relationship between herbaceous biomass and 1‐km² Advanced Very High Resolution Radiometer (AVHRR) NDVI in Kruger National Park, South Africa

Abstract

The relationship between multi-year (1989-2003), herbaceous biomass and 1-km 2 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data in Kruger National Park (KNP), South Africa is considered. The objectives were: (1) to analyse the underlying relationship between NDVI summed for the growth season (SNDVI) and herbaceous biomass in field sites (n5533) through time and (2) to investigate the possibility of producing reliable herbaceous biomass maps for each growth season from the satellite SNDVI observations. Landsat Enhanced Thematic Mapper Plus (ETM + ) and Thematic Mapper (TM) data were used to identify highly heterogeneous field sites and exclude them from the analyses. The average R 2 for the SNDVI-biomass relationship at individual sites was 0.42. The growth season mean biomass and SNDVI of most landscape groups were strongly correlated with rainfall and each other. Although measured tree cover and MODIS estimates of tree cover did not have a detectable effect on the SNDVI-biomass relationship, other observations suggest that tree cover should not be ignored. The SNDVI was successful at estimating inter-annual variations in the biomass at single sites, but on an annual basis the relationship derived from all the sites was not strong enough (average R 2 50.36) to produce reliable growth season biomass maps. This was mainly attributed to the fact that the biomass data were sampled from very small field sites that were not fully representative of 1-km 2 AVHRR pixels. Supplementary field surveys that sample a larger area for each field site (e.g. 1 km 2 or larger) should account for the variability in biomass and may improve the strength of SNDVI-biomass relationships observed in a single growth season.

Determination of green aboveground biomass in desert steppe using litter-soil-adjusted vegetation index

“…As shown in many studies, accurate estimation of green aboveground biomass using remotely sensed data remains a challenge in arid and semiarid grassland due to sparse green vegetation cover and litter [Svoray and Shoshany, 2003;He et al, 2006;Wessels et al, 2006;Beeri et al, 2007]. Based on our field observations, green vegetation cover in our study site was less than 30%, and mean value of litter mass in all sampling plots was 75.8 g m -2 , while mean value of green aboveground biomass was 59. in a large-scale field campaign in the desert steppe of Sonid Zuoqi and Sonid Youqi.…”

“…Vegetation indices calculated from red and near-infrared (NIR) bands are good indicators of vegetation photosynthetic activity [Myneni and Los, 1995;Liu et al, 2013;Marino and Alvino, 2014], and are well correlated to green aboveground biomass in grassland [Schino et al, 2003;Ren et al, 2011]. The well-known vegetation index, now widely used for green aboveground biomass estimation in grassland [Wessels et al, 2006;An et al, 2013;Gao et al, 2013;Jin et al, 2014;Xia et al, 2014], is normalized difference vegetation index (NDVI) [Rouse et al, 1974]. However, the index loses its utility for estimating green aboveground biomass in sparse vegetation canopy situation because of prominent contribution of soil background [Boschetti et al, 2007].…”

Monitoring rangeland degradation using a novel local NPP scaling based scheme over the “Three-River Headwaters” region, hinterland of the Qinghai-Tibetan Plateau

“…This method takes non-degraded rangeland as a reference through the comparison of characteristic parameters observed directly (such as biomass, vegetation coverage, edible forage, NDVI, NPP, soil physical and chemical properties indices) to analyze the degradation/restoration of rangeland (Numata et al, 2007;Liu and Zha, 2004;Röder et al, 2008); (3) Monitoring rangeland degradation based on time series analysis of remote sensing. In recent years, these methods have caught widespread attention, and mainly include rainfall use efficiency (RUE) (Wessels et al, 2006;Prince et al, 2004;Paruelo et al, 1999;Holm et al, 2003;Gao et al, 2005;Bai et al, 2008a) and residual trends (RESTREND) (Evans and Geerken, 2004;Wessels et al, 2007;Xu et al, 2010;Cao, 2006;Eckert et al, 2015); (4) Local NPP (the actual Net Primary Productivity) Scaling (Wessels et al, 2007;Wessels et al, 2008;Prince et al, 2009). …”

Spatial Autocorrelation and Uncertainty Associated with Remotely-Sensed Data

“…Regression is extensively used to model various phenomena such as land use and land cover [4,11], NDVI [12,13], urban heat island [4], and landslide susceptibility [14], but spatial autocorrelation has been barely accommodated in modeling remotely-sensed data. Remotely-sensed data has a strong positive spatial autocorrelation in most cases: even one with a fragmented (e.g., land use) pattern with a coarse resolution (e.g., 250 m of MODIS).…”