TY - JOUR

T1 - Characterizing ecosystem change in wetlands using dense earth observation time series

AU - Kovács, Gyula Mate

AU - Horion, Stéphanie

AU - Fensholt, Rasmus

N1 - Funding Information: This work was supported by a research grant (34306) from VILLUM Foundation. The authors thank Wetlands International Sahel Office for providing acess to hydrometric measurments from the field. Funding Information: This work was supported by a research grant ( 34306 ) from VILLUM Foundation . The authors thank Wetlands International Sahel Office for providing acess to hydrometric measurments from the field. Publisher Copyright: © 2022 The Authors

PY - 2022

Y1 - 2022

N2 - Wetlands in drylands are vulnerable to degradation and disappearance due to the combined effects of increasing anthropogenic disturbances and climatic extremes. Such influences may drive non-linear shifts in surface responses that require long-term monitoring approaches for their study. Here, we used a piece-wise regression model to characterize long-term Ecosystem Change Types (ECT) in the surface water and vegetation dynamics of the Inner Niger Delta wetlands in Mali between 2000 and 2019. We also examined the added benefits of using a dense Landsat time series for such segmented trend analysis in comparison with MODIS products that are regularly used for ecosystem trends assessment. Our approach has found statistically significant (p < 0.05) long-term changes in wetland ecosystems, as calculated from Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI) image series on both the MODIS and the Landsat scales. The class-specific accuracies of the detected ECTs were evaluated through the validation of temporal trajectories based on the TimeSync logic at selected probability sample locations. Results showed higher user's, producer's, and overall accuracies (OA) when using a dense Landsat time series (OA = 0.89 ± 0.01), outperforming the MOD09A1 time series (OA = 0.37 ± 0.03). Our study provides a robust framework for long-term wetland monitoring that demonstrates the benefits of applying dense Landsat time-series imagery for accurate quantifications of linear and non-linear ecosystem responses in vast highly dynamic floodplain systems. Delivering such an improved assessment, in a spatial resolution that better resolves the characteristics of wetlands ecosystems, has the potential to support the information needs of global conservation and restoration efforts.

AB - Wetlands in drylands are vulnerable to degradation and disappearance due to the combined effects of increasing anthropogenic disturbances and climatic extremes. Such influences may drive non-linear shifts in surface responses that require long-term monitoring approaches for their study. Here, we used a piece-wise regression model to characterize long-term Ecosystem Change Types (ECT) in the surface water and vegetation dynamics of the Inner Niger Delta wetlands in Mali between 2000 and 2019. We also examined the added benefits of using a dense Landsat time series for such segmented trend analysis in comparison with MODIS products that are regularly used for ecosystem trends assessment. Our approach has found statistically significant (p < 0.05) long-term changes in wetland ecosystems, as calculated from Modified Normalized Difference Water Index (MNDWI) and Normalized Difference Vegetation Index (NDVI) image series on both the MODIS and the Landsat scales. The class-specific accuracies of the detected ECTs were evaluated through the validation of temporal trajectories based on the TimeSync logic at selected probability sample locations. Results showed higher user's, producer's, and overall accuracies (OA) when using a dense Landsat time series (OA = 0.89 ± 0.01), outperforming the MOD09A1 time series (OA = 0.37 ± 0.03). Our study provides a robust framework for long-term wetland monitoring that demonstrates the benefits of applying dense Landsat time-series imagery for accurate quantifications of linear and non-linear ecosystem responses in vast highly dynamic floodplain systems. Delivering such an improved assessment, in a spatial resolution that better resolves the characteristics of wetlands ecosystems, has the potential to support the information needs of global conservation and restoration efforts.

KW - BFAST01

KW - Change detection

KW - Dense time series

KW - Earth observation

KW - Ecosystem change

KW - Inner Niger Delta

KW - Lake Débo

KW - Landsat

KW - Sahel

KW - TimeSync

KW - Wetland

U2 - 10.1016/j.rse.2022.113267

DO - 10.1016/j.rse.2022.113267

M3 - Journal article

AN - SCOPUS:85137901076

VL - 281

JO - Remote Sensing of Environment

JF - Remote Sensing of Environment

SN - 0034-4257

M1 - 113267

ER -