Phenological description of natural vegetation in southern Africa using remotely‐sensed vegetation data

Abstract

Abstract. Various attempts have been made to describe and map the vegetation of southern Africa with recent efforts having an increasingly ecologi cal context. Vegetation classification is usually based on vegetation physiognomy and floristic composition, but phenology is useful source of information which is rarely used, although it can contribute functional information on ecosystems. The objectives of this study were to identify a suite of variables derived from time‐series NDVI data that best describe the phenological phenomena of vegetation in southern Africa and, secondly, to assess a classification of pixels of the study area based on NDVI variables using a preexisting map of the biomes that was delimited on the basis of life forms and climate. A number of variables were derived from the satellite data for describing phenological phenomena, which were analysed by multivariate techniques to determine which variables best explained the variation in the satellite data. This set of variables was used to produce a phenological classification of the vegetation of southern Africa, the results of which are discussed in relation to their concordance with the existing biome boundaries.

Land Use and Land Cover Changes and Their Impacts on Hydroclimate, Ecosystems and Society

“…Second, the seasonal coeffi cient of variation (CV) is a measure of the intra-annual variation of photosynthetic activity, which has been used as an indicator of the seasonality of carbon fl uxes or the amplitude of the annual cycle (Oesterheld et al 1998 ;Potter and Brooks 1998 ;Guerschman et al 2003 ). Third, the phenology, or date of the absolute maximum of NDVI (DMAX), indicates the intra-annual distribution of the period with maximum photosynthetic activity (Lloyd 1990 ;Hoare and Frost 2004 ). These three metrics capture important features of ecosystem functioning for temperate ecosystems (Pettorelli et al 2005 ;Lloyd 1990 ;Paruelo and Lauenroth 1995 ;Nemani and Running 1997 ;Paruelo et al 2001 ;Virginia et al 2001 ) and up to 90 % of the variability of the NDVI temporal dynamics (Paruelo et al 2001 ;Alcaraz-Segura et al 2006.…”

Rapid primary productivity changes in one of the last coastal rainforests: the case of Kahua, Solomon Islands

“…It is indeed not clear how accurately the reported trend in NDVI represents ecological changes (Morissette et al 2004) in Kahua. As NDVI is very sensitive to cloud cover, it generally cannot discriminate between different vegetation types, and in tropical regions where canopy cover is dense the linear relationship between NDVI and primary productivity is weaker (Hoare & Frost 2004;Pettorelli et al 2005;Xiao et al 2006). These constraints might potentially lead to underestimated vegetation change occurring in the Kahua region.…”

Examining eco-environmental changes at major recreational sites in Kenting National Park in Taiwan by integrating SPOT satellite images and NDVI

“…Meanwhile, the use of satellite images allows for better handling of large-scale and multi-period environmental information while integrating grounded survey data, geographic information system (GIS), and vegetation indexes calculations (Giri, Defourny, & Shrestha, 2003;Hashemi, Chai, & Bayat, 2013;Hoare & Frost, 2004). For instance, Sarri on-Gavil an, Benítez-M arquez, and Mora-Rangel (2015) utilized GIS and ESDA (Exploratory Spatial Data Analysis) to analyze the tourism development in the Andalusian region of Spain and to efficiently monitor tourism activity in mountain areas.…”