A potential to monitor nutrients as an indicator of rangeland quality using space borne remote sensing

8th International Symposium of the Digital Earth

地球科学;计算机科学

Ramoelo, A.^1,2 ; Cho, M.A.^2 ; Madonsela, S.^1 ; Mathieu, R.^1 ; Van Der Korchove, R.^2 ; Kaszta, Z.^2 ; Wolf, E.^2

Natural Resource and Environment, Council for Scientific and Industrial Research (CSIR), Pretoria, South Africa^1

IGEAT, Université Libre de Bruxelles (ULB), Belgium^2

关键词: Agricultural productions;    Conventional methods;    Ecosystem functioning;    High spatial resolution;    Normalized difference vegetation index;    Spaceborne remote sensing;    Vegetation index;    Vegetation parameters;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/18/1/012094/pdf
DOI  :  10.1088/1755-1315/18/1/012094

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

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.