Abstract
Global change consisting of land use and climate change could have huge impacts on food security and the health of various ecosystems. Leaf nitrogen (N) is one of the key factors limiting agricultural production and ecosystem functioning. Leaf N can be used as an indicator of rangeland quality which could provide information for the farmers, decision makers, land planners and managers. Leaf N plays a crucial role in understanding the feeding patterns and distribution of wildlife and livestock. Assessment of this vegetation parameter using conventional methods at landscape scale level is time consuming and tedious. Remote sensing provides a synoptic view of the landscape, which engenders an opportunity to assess leaf N over wider rangeland areas from protected to communal areas. Estimation of leaf N has been successful during peak productivity or high biomass and limited studies estimated leaf N in dry season. The objective of this study is to monitor leaf N as an indicator of rangeland quality using WorldView 2 satellite images in the north-eastern part of South Africa. Series of field work to collect samples for leaf N were undertaken in the beginning of May (end of wet season) and July (dry season). Several conventional and red edge based vegetation indices were computed. Simple regression was used to develop prediction model for leaf N. Using bootstrapping, indicator of precision and accuracy were analyzed to select a best model for the combined data sets (May and July). The may model for red edge based simple ratio explained over 90% of leaf N variations. The model developed from the combined data sets with normalized difference vegetation index explained 62% of leaf N variation, and this is a model used to estimate and map leaf N for two seasons. The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability.
Highlights
The Global change consisting of land use and climate change could have huge impacts on food security and the health of various ecosystems, with a potential to exacerbate poverty especially in rural communities
During dry periods the nutrients are generally translocated from the leaves to the roots
The combined data sets showed about 65% variability of leaf N which combines both dry and late wet season
Summary
The Global change consisting of land use and climate change could have huge impacts on food security and the health of various ecosystems, with a potential to exacerbate poverty especially in rural communities. The study area is part of the transect where we are developing techniques to map various vegetation attributes such as grass biomass, woody biomass and species maps using multi-scale remote sensing data sets. Two geological types such as gabbro and granite, which underpins various soil fertility levels occurs in this study. Data collection and sampling WorldView 2 images for April and July 2012 were acquired and have a 2m x 2m spatial resolution Studies such as Ramoelo et al [6] and Skidmore et al [9] showed that the best period to estimate grass nutrients is during peak productivity. The grass and tree leaf samples were dried (800C for 24 hours) and taken to the laboratory for chemical analysis to retrieve leaf N at Bemlab PTY (LTD), Strand, Western Cape, South Africa
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