Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series

Souverijns, Niels; Buchhorn, Marcel; Horion, Stéphanie; Fensholt, Rasmus; Verbeeck, Hans; Verbesselt, Jan; Herold, Martin; Tsendbazar, Nandin‐Erdene; Bernardino, Paulo; Somers, Ben; Kerchove, Ruben Van De

doi:10.3390/rs12223817

Cited by 23 publications

(25 citation statements)

References 72 publications

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“…Tree monitoring was previously focused on areas defined as forest, ignoring most of the trees present in the Sahel (Brandt et al 2020). In the mid-2000s, observations of increases in vegetation since the early 1980s lead to a narrative of "re-greening" of large parts of the Sahel (Fensholt et al 2009, Herrmann et al 2005, Olsson et al 2005, Souverijns et al 2020. Over the period 2000-2015, there was no significant trend in total vegetation in most parts of the Sahel, but an increase in 16% of the area (Leroux et al 2017), whereas woody vegetation was in general increasing, primarily in areas with low anthropogenic pressure (Brandt et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Ecosystem services in Sahelian village landscapes 1952–2016: estimating change in a data scarce region

Sinare¹,

Peterson²,

Börjeson³

et al. 2022

E&S

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Burkina Faso and the wider Sahel region have experienced substantial changes in rainfall, population, and landscape use. These changes have altered ecosystem services, the benefits that people receive from ecosystems, and rural livelihoods. However, it is difficult to assess the magnitude of these changes because of missing and fragmented social, agricultural, and ecological data. We estimated changes in 10 key provisioning ecosystem services in rural Burkina Faso between 1952 and 2016. We used a simple model of plausible social-ecological changes to make a historical extrapolation that bridges these data gaps, and assessed historical changes. Our approach combined the interpretation of historic aerial photographs and satellite images, with field observations and interviews. We applied the approach for six villages in two administrative regions for six points in time. We modeled the use of historic ecosystems by analyzing a range of estimates of changes in the generation of each service and its value to people. We found that cultivated ecosystem services have increased 1.5-23 times over the study period, while the non-cultivated ecosystem services firewood, construction material, and medicine have decreased to 66-20% of their previous values. Per capita production of cultivated ecosystem services has remained relatively stable, while the per capita production of all other ecosystem services has decreased, to 54-11% of their 1952 values. Although alternatives are available for some ecosystem services, such as medicine and construction material, there are currently limited alternatives available for other services, such as firewood. Decline in wild food availability and consumption is likely to reduce the nutritional value of rural people's food. Our analysis of changes demonstrates that shrubs and trees on fields generate many ecosystem services that are key to rural livelihoods, and that efforts to enhance crop yields should maintain shrubs and trees. Our approach for estimating historical ecosystem services may also be useful to apply in other data scarce regions.

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Section: Introductionmentioning

confidence: 99%

Ecosystem services in Sahelian village landscapes 1952–2016: estimating change in a data scarce region

Sinare¹,

Peterson²,

Börjeson³

et al. 2022

E&S

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“…A total of 39 EO data types were used in the reviewed studies. This reflects a wide range of EO applications for savanna mixed pixel analysis [47,105,[113][114][115][116][117][118]. Of these, 26 (89%) were optical, 11 (7%) radar, 2 (3%) LiDAR, and 1 (1%) were a combination of optical hyperspectral and LiDAR.…”

Section: Type Of Earth Observation Data Usedmentioning

confidence: 89%

“…A few of the regional scale studies were conducted in African savanna mosaics along the Sahel drylands of West Africa with several countries, such as Mali and Niger, included in the study area [102][103][104]. For example, Souverijns et al [105] estimated fractional land cover over a period of 30 years at a regional scale in Senegal, Burkina Faso, Nigeria, Niger, and Sudan. In other regions, Guan et al [106] estimated vegetation fractional land cover over tropical savanna regional areas covering Ethiopia, Kenya, Tanzania, Malawi, Zambia, Zimbabwe, and Botswana.…”

Section: Geographic and Spatial Scalementioning

confidence: 99%

Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

et al. 2021

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Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.

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“…Many descriptive metrics can be used to calculate STMs from the time series. Based on the good performance in the previous studies (Potapov et al, 2017;Souverijns et al, 2020), we calculated the mean, as well as the 10th, 25th, 50th, 75th, 90th, 90th-10th, and 75th-25th percentiles of the spectral bands and VIs (Table 3).…”

Section: Spectral-temporal Metricsmentioning

confidence: 99%

Impact of Preprocessing on Tree Canopy Cover Modelling: Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

Tang

Adhikari

Pellikka

et al. 2022

Front. Remote Sens.

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Preprocessing of Landsat images is a double-edged sword, transforming the raw data into a useful format but potentially introducing unwanted values with unnecessary steps. Through recovering missing data of satellite images in time series analysis, gap-filling is an important, highly developed, preprocessing procedure, but its necessity and effects in numerous Landsat applications, such as tree canopy cover (TCC) modelling, are rarely examined. We address this barrier by providing a quantitative comparison of TCC modelling using predictor variables derived from Landsat time series that included gap-filling versus those that did not include gap-filling and evaluating the effects that gap-filling has on modelling TCC. With 1-year Landsat time series from a tropical region located in Taita Hills, Kenya, and a reference TCC map in 0–100 scales derived from airborne laser scanning data, we designed comparable random forest modelling experiments to address the following questions: 1) Does gap-filling improve TCC modelling based on time series predictor variables including the seasonal composites (SC), spectral-temporal metrics (STMs), and harmonic regression (HR) coefficients? 2) What is the difference in TCC modelling between using gap-filled pixels and using valid (actual or cloud-free) pixels? Two gap-filling methods, one temporal-based method (Steffen spline interpolation) and one hybrid method (MOPSTM) have been examined. We show that gap-filled predictors derived from the Landsat time series delivered better performance on average than non-gap-filled predictors with the average of median RMSE values for Steffen-filled and MOPSTM-filled SC’s being 17.09 and 16.57 respectively, while for non-gap-filled predictors, it was 17.21. MOPSTM-filled SC is 3.7% better than non-gap-filled SC on RMSE, and Steffen-filled SC is 0.7% better than non-gap-filled SC on RMSE. The positive effects of gap-filling may be reduced when there are sufficient high-quality valid observations to generate a seasonal composite. The single-date experiment suggests that gap-filled data (e.g. RMSE of 16.99, 17.71, 16.24, and 17.85 with 100% gap-filled pixels as training and test datasets for four seasons) may deliver no worse performance than valid data (e.g. RMSE of 15.46, 17.07, 16.31, and 18.14 with 100% valid pixels as training and test datasets for four seasons). Thus, we conclude that gap-filling has a positive effect on the accuracy of TCC modelling, which justifies its inclusion in image preprocessing workflows.

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Thirty Years of Land Cover and Fraction Cover Changes over the Sudano-Sahel Using Landsat Time Series

Cited by 23 publications

References 72 publications

Ecosystem services in Sahelian village landscapes 1952–2016: estimating change in a data scarce region

Ecosystem services in Sahelian village landscapes 1952–2016: estimating change in a data scarce region

Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

Impact of Preprocessing on Tree Canopy Cover Modelling: Does Gap-Filling of Landsat Time Series Improve Modelling Accuracy?

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