کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4459295 1621287 2011 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات کامپیوتر در علوم زمین
پیش نمایش صفحه اول مقاله
Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring
چکیده انگلیسی

Land-cover is an important parameter in analyzing the state and dynamics of natural and anthropogenic terrestrial ecosystems. Land-cover classes related to semi-arid savannas currently exhibit among the greatest uncertainties in available global land cover datasets. This study focuses on the Kalahari in northeastern Namibia and compares the effects of different composite lengths and observation periods with class-wise mapping accuracies derived from multi-temporal MODIS time series classifications to better understand and overcome quality gaps in mapping semi-arid land-cover types. We further assess the effects of precipitation patterns on mapping accuracy using Tropical Rainfall Measuring Mission (TRMM) observation data. Botanical field samples, translated into the UN Land Cover Classification System (LCCS), were used for training and validation. Different sets of composites (16-day to three-monthly) were generated from MODIS (MOD13Q1) data covering the sample period from 2004 to 2007. Land-cover classifications were performed cumulatively based on annual and inter-annual feature sets with the use of random forests. Woody vegetation proved to be more stable in terms of omission and commission errors compared to herbaceous vegetation types. Generally, mapping accuracy increases with increasing length of the observation period. Analyses of variance (ANOVA) verified that inter-annual classifications significantly improved class-wise mapping accuracies, and confirmed that monthly composites achieved the best accuracy scores for both annual and inter-annual classifications. Correlation analyses using piecewise linear models affirmed positive correlations between cumulative mapping accuracy and rainfall and indicated an influence of seasonality and environmental cues on the mapping accuracies. The consideration of the inter-seasonal variability of vegetation activity and phenology cycling in the classification process further increases the overall classification performance of savanna classes in large-area land-cover datasets. Implications for global monitoring frameworks are discussed based on a conceptual model of the relationship between observation period and accuracy.

Research highlights
► Effects of composite length and observation period were assessed for semi-arid land-cover mapping.
► Mapping accuracy increases with increasing observation period.
► Using small composite period lengths leads to increased mapping accuracies.
► Mapping accuracy is a function of precipitation input and change magnitude between land-cover stages.
► We suggest the integration of multi-annual time series for mapping in semi-arid savannas.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 115, Issue 10, 17 October 2011, Pages 2445–2459
نویسندگان
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