On the Suitability of MODIS Time Series Metrics to Map Vegetation Types in Dry Savanna Ecosystems: A Case Study in the Kalahari of NE Namibia

Abstract

Abstract:The characterization and evaluation of the recent status of biodiversity in Southern Africa's Savannas is a major prerequisite for suitable and sustainable land management and conservation purposes. This paper presents an integrated concept for vegetation type mapping in a dry savanna ecosystem based on local scale in-situ botanical survey data with high resolution (Landsat) and coarse resolution (MODIS) satellite time series. In this context, a semi-automated training database generation procedure using object-oriented image segmentation techniques is introduced. A tree-based Random Forest classifier was used for mapping vegetation type associations in the Kalahari of NE Namibia based on inter-annual intensity-and phenology-related time series metrics. The utilization of long-term inter-annual temporal metrics delivered the best classification accuracies (Kappa = 0.93) compared with classifications based on seasonal feature sets. The relationship between annual classification accuracies and bi-annual precipitation sums was conducted using data from the Tropical Rainfall Measuring Mission (TRMM). Increased error rates occurred in years with high rainfall rates compared to dry rainy seasons.Remote Sens. 2009, 1 621The variable importance was analyzed and showed high-rank positions for features of the Enhanced Vegetation Index (EVI) and the blue and middle infrared bands, indicating that soil reflectance was crucial information for an accurate spectral discrimination of Kalahari vegetation types. Time series features related to reflectance intensity obtained increased rank-positions compared to phenology-related metrics.

Mapping Bush Encroaching Species by Seasonal Differences in Hyperspectral Imagery

“…Information on vegetation phenology is increasingly used for distinguishing between vegetation types in remote sensing studies [22,56,57]. In this study, we were able to identify a phenological gradient of change using a constrained principal curve that was fitted to data from a vegetation survey constrained by a set of differenced spectral indices.…”

“…For remote sensing, phenology provides valuable information for distinguishing vegetation types. For example, Hüttich et al [22] used phenological differences based on MODIS data to better separate vegetation types with similar life forms. Resasco et al [23] were able to map an invasive shrub in understorey vegetation based on comparisons of seasonal differences in vegetation indices.…”

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

“…Other optical data include: the similar spatial resolution (i) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) (Reiche et al 2012;Lucas et al 2011) and (ii) Linear Imaging Self Scanning Sensor 3 (LISS-III) (Lucas et al 2011); the higher resolution (iii) Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) (Vaglio Laurin et al 2013) and (iv) High Resolution Geometric (HRG) instrument (Lucas et al 2011); and the very high resolution (VHR) (v) QuickBird and (vi) WorldView-2 (Petrou et al 2014;Adamo et al 2014) sensors. Although of lower spatial resolution, data time series from the Moderate Resolution Imaging Spectroradiometer (MODIS) have proven successful in mapping dry savanna vegetation, capturing phenological properties with inter-annual classification average user's and producer's accuracies reaching 94.86% and 97.73% for 12 classes, respectively (Hüttich et al 2009). Hyperspectral data features have shown high potential in discriminating vegetation types (Forzieri et al 2013;Vyas et al 2011;Chan et al 2012).…”

Exploiting Differential Vegetation Phenology for Satellite-Based Mapping of Semiarid Grass Vegetation in the Southwestern United States and Northern Mexico

“…A diverse set of analytical methods that exploit phenological information in time-series of multispectral images has been developed for distinguishing and mapping vegetation cover types [20][21][22][23][24][25], estimating and mapping biomass and vegetation fuel loads [26][27][28][29][30] and for investigating how vegetation and related ecosystem processes respond to management, disturbance, weather, drought and climate change [31][32][33][34][35][36][37]. These methods examine the seasonal growth dynamics of the vegetation through quantification and analysis of various metrics of the time signal observed within one or more years.…”