Harnessing big data to track progress towards SDG 15: Life on Land

Sustainable Development Goal 15 (SDG 15) seeks to protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. SDG 15 is a very ambitious goal – it encompasses all types of land-based ecosystems and biodiversity, with 12 targets and 14 indicators. The year 2023 marks the mid-point of the 15-year period envisaged to achieve SDG 15, unfortunately, all status-related indicators are off track (e.g. 15.1.1) and that many are going in reverse (e.g. 15.5.1), although some policies related indicators (e.g. 15.8.1) made some progress (United Nations 2023).

The implementation of SDG 15 needs the support of accurate and detailed data where we are. Now, a tier system developed by the Inter-Agency Expert Group on SDG indicators (IAEG-SDGs) is used to assist in evaluating the availability of data for all indicators at global level. The latest Tier classification released on March 2023 now has 11 indicators classified as Tier I, 2 as Tier II indicators and 1 indicator that have multiple tiers classifications (IAEG-SDGs 2023), which shows most SDG 15 indicators have mature and internationally recognized methods. While these achievements are worthy of celebration, we cannot ignore the long-standing gaps that continue to challenge our data environment. Geographic coverage, timeliness and disaggregation remain areas of concern. For SDG 15, 42% indicators are faced with insufficient data to report progress at the global level in 2022 (UNEP 2023). This grim reality reminds us that we must prioritize acquiring effective datasets through science and technology innovation.

In recent years, big data provide new and innovative opportunities to collect and process information for SDGs (MacFeely 2019), particularly those that are related to health and biodiversity (Allen et al., 2021). Among of multiple big data sources, earth observation and citizen science were mostly utilized with advanced analytical techniques for SDG 15, since it need information on land cover, vegetation productivity and habitat and species at the large scale. In general, big data mainly address existing gaps in SDG-related data in the following ways. For example, they can be helpful in developing new datasets for Tier II indicators or by providing more timely and disaggregated datasets to fill gaps in time series and improve spatial coverage for Tier I indicators. Similarly, the existing official indicators can also be made more comprehensive by developing new indicators and datasets to add further depth.

This special issue ‘Big Earth Data in support of SDG 15, Life on Land’ aims to capture the recent advancements in SDG 15 indicator progress evaluation by using cutting-edge technologies, such as earth observation, artificial intelligence and citizen science. The targets of 15.1, 15.2, 15.3 and 15.5 were covered by 10 studies.

SDG 15.1&15.2

Zhang et al. (2023a) employed GF-1 and GF-2 data with enhanced texture information to map forest cover, while time series Landsat data is used to estimate forest above-ground biomass (AGB) across the whole territory of China, which support the calculation of SDG 15.1.1 and SDG 15.2.1.

Wei et al. (2023) assessed the spatio-temporal trend of African forests from 2000 to 2020, using a 250-m resolution fractional tree cover product (GLOBMAP Fractional Tree Cover product) that can capture the variation of forest density in the widespread mixed vegetation landscapes of the continent. Hotspots of forest gain and loss were identified, which would help African countries to monitor forest change and promote forest management to achieve the SDGs.

Xu et al. (2023) employed Planet & NICFI and Sentinel-1/2 data for mapping the high-resolution global oil palm plantation by using the image-oriented classification and regression tree (CART) algorithm. Specially, the young industrial and highly irregular small-holder plantations were successfully mapped, which are mostly unmapped and not included in official FAO statistics. The results provide data to support SDG 15.1 by assisting future oil palm-related development planning and monitoring in the world’s major oil palm-growing countries.

SDG 15.3

Shen et al. (2023) developed a tool (HiLPD-GEE) to calculate 30 m Land Productivity Dynamics (LPD) by fusing Landsat and MODIS data based on Google Earth Engine (GEE). The tool can calculate 30 m LPD in any spatial range within any time window after 2013, supporting global land degradation monitoring. The application in African Great Green Wall proves that the 30 m LPD product generated by HiLPD-GEE could reflect the land productivity change effectively and reflect more spatial details.

Hong, Wang, and Han (2023) construct a three-dimensional feature space (NDVI-Alberdo-SI) to automatic map the land salinization in the Yellow River Basin from 2015 to 2020 based on Landsat 8 images, which provide a solution for the land salinization assessment at the large scale.

Yan et al. (2023) quantitatively explored global grassland degradation trends from 2000 to 2020 by coupling vegetation growth (NPP) and its response to climate change and analyzed the driving factors with multiple and partial regression techniques, especially in the hotspot regions. They found that the improvement in global grassland has been remarkable since 2000 and human activities played a crucial role in reversing the trend of grassland degradation.

Zhang et al. (2023b) proposed a workflow for sand and dust storms (SDSs) monitoring with the dust storm detection index based on MODIS remote sensing data. The annual distribution of spring SDSs (March to May) from 2000 to 2021 on the Mongolian Plateau were obtained, the dynamic analysis shows the cross-border regions between China and Mongolia appear to be SDS intensity centers, particularly those in southern Mongolia.

Jia et al. (2023) estimated the carbon storage of desert ecosystems in China using MODIS-NDVI data in combination with ground survey, soil census, and literature statistical data and evaluated, which provides important evidence for the understanding of the carbon storage of desert ecosystems in China. The result proves that the ecosystem restoration would facilitate both SDG 15.3 and SDG 13, even in the desert ecosystems.

SDG 15.5

Duan et al. (2023) characterized the effects of conservation efforts on shorebird diversity, habitat area and quality using long-term remote sensing data, and shorebird survey data in the Yellow River Delta. They found that conservation actions maintained the stability of waterbird populations and their habitat, and the citizen science has a great potential for species biodiversity investigation.

Li et al. (2023) analyzed the threatened status of Chinese higher plants based on the two most recent assessments of Chinese higher plants in 2013 and 2020, which support the Red List Index dynamic evaluation for higher plants. They inferred that China’s threatened plant species were likely/relatively effectively protected. However, attention should also be given to the non-threatened species in the future as an additional strategy for their conservation, to maintain their non-threatened status.

Acknowledgements

This special issue was supported by the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals (Grant no. CBAS2022IRP07), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19090124).

Additional information

Funding

This special issue was supported by the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals [grant no. CBAS2022IRP07], the Strategic Priority Research Program of the Chinese Academy of Sciences [grant no. XDA19090124].