Crop-system characterization by multitemporal SPOT data in the South-East of France

Guérif, Martine; Gu, Xing Fa; Inra, Jean Pierre Guinot

doi:10.1080/01431169208904233

Cited by 3 publications

(1 citation statement)

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“…To leverage species-dependent differences in phenological development, the highest possible revisit frequencies are optimum. Very dense time series not only mitigate cloud effects but also permit to extract key phenological indicators such as start and end of season [50,60]. In our study, the available cloud-free scenes did not cover the entire year in regular intervals: mainly in July, only very few cloud-free data sets were acquired due to convective cloud formation; additionally, in spring and autumn, spells of bad weather prevented the acquisition of useful scenes.…”

Section: Acquisition Datementioning

confidence: 98%

Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data

et al. 2019

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Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution data for classification of five coniferous and seven broadleaved tree species in a diverse Central European Forest. To run the classification, 18 cloud-free S2 acquisitions were analyzed in a two-step approach. The available scenes were first used to stratify the study area into six broad land-cover classes. Subsequently, additional classification models were created separately for the coniferous and the broadleaved forest strata. To permit a deeper analytical insight in the benefits of multi-temporal datasets for species identification, classification models were developed taking into account all 262,143 possible permutations of the 18 S2 scenes. Each model was fine-tuned using a stepwise recursive feature reduction. The additional use of vegetation indices improved the model performances by around 5 percentage points. Individual mono-temporal tree species accuracies range from 48.1% (January 2017) to 78.6% (June 2017). Compared to the best mono-temporal results, the multi-temporal analysis approach improves the out-of-bag overall accuracy from 72.9% to 85.7% for the broadleaved and from 83.8% to 95.3% for the coniferous tree species, respectively. Remarkably, a combination of six–seven scenes achieves a model quality equally high as the model based on all data; images from April until August proved most important. The classes European Beech and European Larch attain the highest user’s accuracies of 96.3% and 95.9%, respectively. The most important spectral variables to distinguish between tree species are located in the Red (coniferous) and short wave infrared (SWIR) bands (broadleaved), respectively. Overall, the study highlights the high potential of multi-temporal S2 data for species-level classifications in Central European forests.

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Section: Acquisition Datementioning

confidence: 98%