Next Article in Journal
Spatio-Temporal Variations of Carbon Use Efficiency in Natural Terrestrial Ecosystems and the Relationship with Climatic Factors in the Songnen Plain, China
Next Article in Special Issue
Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data
Previous Article in Journal
Evaluating Resilience-Centered Development Interventions with Remote Sensing
Open AccessArticle

Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series

1
DYNAFOR, Université de Toulouse, INRA, 31326 Castanet-Tolosan, France
2
CESBIO, Université de Toulouse, CNES/CNRS/INRA/IRD/UPS, 31401 Toulouse, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2512; https://doi.org/10.3390/rs11212512
Received: 30 September 2019 / Revised: 21 October 2019 / Accepted: 24 October 2019 / Published: 26 October 2019
(This article belongs to the Special Issue Mapping Tree Species Diversity)
Mapping forest composition using multiseasonal optical time series remains a challenge. Highly contrasted results are reported from one study to another suggesting that drivers of classification errors are still under-explored. We evaluated the performances of single-year Formosat-2 time series to discriminate tree species in temperate forests in France and investigated how predictions vary statistically and spatially across multiple years. Our objective was to better estimate the impact of spatial autocorrelation in the validation data on measurement accuracy and to understand which drivers in the time series are responsible for classification errors. The experiments were based on 10 Formosat-2 image time series irregularly acquired during the seasonal vegetation cycle from 2006 to 2014. Due to lot of clouds in the year 2006, an alternative 2006 time series using only cloud-free images has been added. Thirteen tree species were classified in each single-year dataset based on the Support Vector Machine (SVM) algorithm. The performances were assessed using a spatial leave-one-out cross validation (SLOO-CV) strategy, thereby guaranteeing full independence of the validation samples, and compared with standard non-spatial leave-one-out cross-validation (LOO-CV). The results show relatively close statistical performances from one year to the next despite the differences between the annual time series. Good agreements between years were observed in monospecific tree plantations of broadleaf species versus high disparity in other forests composed of different species. A strong positive bias in the accuracy assessment (up to 0.4 of Overall Accuracy (OA)) was also found when spatial dependence in the validation data was not removed. Using the SLOO-CV approach, the average OA values per year ranged from 0.48 for 2006 to 0.60 for 2013, which satisfactorily represents the spatial instability of species prediction between years. View Full-Text
Keywords: tree species; forest; biodiversity; time series; spatial autocorrelation; cross-validation; accuracy tree species; forest; biodiversity; time series; spatial autocorrelation; cross-validation; accuracy
Show Figures

Graphical abstract

MDPI and ACS Style

Karasiak, N.; Dejoux, J.-F.; Fauvel, M.; Willm, J.; Monteil, C.; Sheeren, D. Statistical Stability and Spatial Instability in Mapping Forest Tree Species by Comparing 9 Years of Satellite Image Time Series. Remote Sens. 2019, 11, 2512.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop