Towards an open and synergistic framework for mapping global land cover

Zhao, Jiyao; Yu, Le; Liu, Han; Huang, Huabing; Wang, Jie; Gong, Peng

doi:10.7717/peerj.11877

Cited by 10 publications

(21 citation statements)

References 41 publications

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“…The dataset was further improved by applying a spatial temporal consistency model known as "Markov a Posterior Random Fields (MAP-MRF"). The overall accuracy of the maps was about 75% [43].…”

Section: Datasetsmentioning

confidence: 96%

“…The LULC datasets were produced by Zhao et al [43] of Tsinghua University and some of the authors of the current manuscript. To produce the datasets, we developed and applied an open and synergistic framework to combine multiple thematic LULC datasets (e.g., European Space Agency Climate Change Initiative (ESA_CCI)[44], Finer Resolution Observation and Monitoring of Global Land Cover (FROM_GLC)) [40] and other EO LULC data available in Google Earth Engine (GEE) cloud-computing platform).…”

Section: Datasetsmentioning

confidence: 99%

“…The analysis in this paper was conducted at ten years (decade) intervals to minimize spurious changes and inter-annual variability that may be introduced by residual noise in the input time series. The subsequent sections of this paper and the article by Zhao et al [43] give details of the entire process for the development of the LULC data.…”

Section: Datasetsmentioning

confidence: 99%

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Spatiotemporal Patterns in Land Use/Land Cover Observed by Fusion of Multi-Source Fine Resolution Data in West Africa

Barnieh¹,

Li²,

Menenti³

et al. 2023

Preprint

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Land Use /Land Cover (LULC) change is a major global concern and a topic of scientific debate. In West Africa, the key among the past changes is the loss of natural vegetation related to changes in different Land Use/Land Cover (LULC) categories, e.g., water bodies, wetland and bare soil. However, not all detected changes in these LULC categories are relevant for LULC change management intervention in a resource-constrained continent, as a massive change in the dominant LULC types may be due to errors in the LULC maps. Previous LULC change analysis detected large discrepancies in the existing LULC maps in the Africa continent. Here we applied an open and synergistic framework to update and improve the existing LULC maps at every five years intervals from 1990 to 2020 in West Africa at a finer spatial resolution of 30m. Next, we detected spatial temporal pattern in past and present LULC changes with the intensity analysis framework in the periods of 1990–2000, 2000–2010, and 2010–2020. A faster annual rate of overall transition was detected in 1990–2000 (first period) and 2010–2020 (recent period) than in 2000–2010 (middle). We observed consistent gains in rangeland (shrubland and grassland) in all the periods, which confirms the observed re-greening of rangeland in the West Africa continent. By contrast, forestland areas experienced consistent losses for the entire periods, which indicate deforestation and degradation. As regards cropland, a net loss was observed in the drought period and net gains in the subsequent periods. The settlement category also gained actively in all periods. Net losses of wetland and bare land categories were also observed in all the periods. We observed net gains in water bodies in 1990–2000 and net losses in the last period. We highlighted the active forestland losses as systematic and hence a clarion call for an intervention. The simultaneous active gross loss and gain intensity of cropland raises food security concern and must send an early warning signal to policy makers that the food security of marginal geographic locations is under threat despite the massive expansion of cropland observed in this study area. The intensity analysis framework was vital to identify the settlement category as relevant for LULC change management intervention across time and space in West Africa instead of focusing on dynamics of all the LULC categories that may be irrelevant and hence a cost effective LULC change management approach. Policies to control the expansion of settlements will indirectly regulate the expansion of cropland field because we observed that as settlement encroached on cropland, simultaneously cropland encroached on natural vegetation to meet the food security requirements of the inhabitants.

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Section: Datasetsmentioning

confidence: 96%

Section: Datasetsmentioning

confidence: 99%

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Spatiotemporal Patterns in Land Use/Land Cover Observed by Fusion of Multi-Source Fine Resolution Data in West Africa

Barnieh¹,

Li²,

Menenti³

et al. 2023

Preprint

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show abstract

“…We utilized the global land cover product (GLC), freely available at http://data.ess. tsinghua.edu.cn/index.html (accessed on 22 September 2021) to map the natural vegetated areas and mask croplands that are vulnerable to human interference [29]. This product consists of 17 land cover types, among which the developed land types and non-vegetated land types were masked to generate natural vegetated areas.…”

Section: Land Cover Mapmentioning

confidence: 99%

“…This product consists of 17 land cover types, among which the developed land types and non-vegetated land types were masked to generate natural vegetated areas. The accuracy for 2010, 2015 and 2020 are 86.39% ± 9.05%, 86.44% ± 8.99% and 84.83% ± 10.19%, respectively [29]. We aggregated the original land cover dataset from 2015 to 0.05 • to match the spatial resolution of the SIF and EVI datasets in this study.…”

Section: Land Cover Mapmentioning

confidence: 99%

Comparison of Vegetation Phenology Derived from Solar-Induced Chlorophyll Fluorescence and Enhanced Vegetation Index, and Their Relationship with Climatic Limitations

et al. 2022

Remote Sensing

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Satellite-based vegetation datasets enable vegetation phenology detection at large scales, among which Solar-Induced Chlorophyll Fluorescence (SIF) and Enhanced Vegetation Index (EVI) are widely used proxies for detecting phenology from photosynthesis and greenness perspectives, respectively. Recent studies have revealed the divergent performances of SIF and EVI for estimating different phenology metrics, i.e., the start of season (SOS) and the end of season (EOS); however, the underlying mechanisms are unclear. In this study, we compared the SOS and EOS of natural ecosystems derived from SIF and EVI in China and explored the underlying mechanisms by investigating the relationships between the differences of phenology derived from SIF and EVI and climatic limiting factors (i.e., temperature, water and radiation). The results showed that the differences between phenology generated using SIF and EVI were diverse in space, which had a close relationship with climatic limitations. The increasing climatic limitation index could result in larger differences in phenology from SIF and EVI for each dominant climate-limited area. The phenology extracted using SIF was more correlated with climatic limiting factors than that using EVI, especially in water-limited areas, making it the main cause of the difference in phenology from SIF and EVI. These findings highlight the impact of climatic limitation on the differences of phenology from SIF and EVI and improve our understanding of land surface phenology from greenness and photosynthesis perspectives.

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Changes in vegetation greenness related to climatic and non-climatic factors in the Sudano-Sahelian region

Zeng

Menenti

et al. 2023

Reg Environ Change

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The potential drivers of vegetation changes in the Sudano-Sahelian region of Africa remain poorly understood due to complex interactions between climatic and anthropogenic processes. In this study, we analyzed the vegetation greenness trends in relation to rainfall variability that we considered the essence of climatic effects on vegetation in a well-known water-limited environment by using time series of satellite data in the Sudano-Sahelian region during 2001–2020. We quantified in more detail the relative contributions of rainfall variability (climatic factor), land use/land cover (LULC) change, and fire occurrence change (non-climatic factors) to vegetation greenness trends in selected sub-regions. The results showed that vegetation greening was widespread (26.9% of the total study area), while vegetation browning was more clustered in central West Africa (5% of the total study area). About half of the vegetation greening area can be explained by long-term rainfall variability during 2001–2020, but most of the area characterized by a browning trend was unrelated to rainfall variability. An analysis of the relative importance showed that LULC changes had significant local effects on vegetation greenness and that these changes were characterized by a strong spatial heterogeneity in specific sub-regions. Gains in cropland and natural vegetation related to positive land management were probably the dominant drivers of greening in Senegal and Ethiopia. Also, the combined impacts of rainfall variability and LULC changes contributed to greening trends in the arid zone, particularly in Mali and Sudan. In contrast, vegetation browning in central West Africa appeared to be driven by cropland gain and natural vegetation loss associated with extensive agricultural production activities. Furthermore, we found that repeated fires for agricultural expansion in central West Africa intensified vegetation browning. These results advanced our understanding of vegetation dynamics in response to climatic and non-climatic factors in Sudano-Sahelian drylands characterized by increasing pressures on land resources.

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Towards an open and synergistic framework for mapping global land cover

Cited by 10 publications

References 41 publications

Spatiotemporal Patterns in Land Use/Land Cover Observed by Fusion of Multi-Source Fine Resolution Data in West Africa

Spatiotemporal Patterns in Land Use/Land Cover Observed by Fusion of Multi-Source Fine Resolution Data in West Africa

Comparison of Vegetation Phenology Derived from Solar-Induced Chlorophyll Fluorescence and Enhanced Vegetation Index, and Their Relationship with Climatic Limitations

Changes in vegetation greenness related to climatic and non-climatic factors in the Sudano-Sahelian region

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