Climate Change Initiative Land Cover Research Articles

Non-Ignorable Differences in NIRv-Based Estimations of Gross Primary Productivity Considering Land Cover Change and Discrepancies in Multisource Products

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm−2yr−1 across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP.

High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe

Abstract. Anthropogenic land use and land cover change (LULCC) is a major driver of environmental changes. The biophysical impacts of these changes on the regional climate in Europe are currently being extensively investigated within the World Climate Research Program (WCRP) Coordinated Downscaling Experiment (CORDEX) Flagship Pilot Study (FPS) Land Use and Climate Across Scales (LUCAS) using an ensemble of different regional climate models (RCMs) coupled with diverse land surface models (LSMs). In order to investigate the impact of realistic LULCC on past and future climates, high-resolution datasets with observed LULCC and projected future LULCC scenarios are required as input for the RCM–LSM simulations. To account for these needs, we generated the LUCAS Land Use and land Cover change (LUC) dataset version 1.1 at 0.1∘ resolution for Europe with annual LULC maps from 1950 to 2100 (https://doi.org/10.26050/WDCC/LUC_hist_EU_v1.1, Hoffmann et al., 2022b, https://doi.org/10.26050/WDCC/LUC_future_EU_v1.1, Hoffmann et al., 2022a), which is tailored to use in state-of-the-art RCMs. The plant functional type (PFT) distribution for the year 2015 (i.e. the Modelling human LAND surface Modifications and its feedbacks on local and regional cliMATE – LANDMATE – PFT dataset) is derived from the European Space Agency Climate Change Initiative Land Cover (ESA-CCI LC) dataset. Details on the conversion method, cross-walking procedure, and evaluation of the LANDMATE PFT dataset are given in the companion paper by Reinhart et al. (2022b). Subsequently, we applied the land use change information from the Land-Use Harmonization 2 (LUH2) dataset, provided at 0.25∘ resolution as input for Coupled Modelling Intercomparison Project Phase 6 (CMIP6) experiments, to derive LULC distributions at high spatial resolution and at annual time steps from 1950 to 2100. In order to convert land use and land management change information from LUH2 into changes in the PFT distribution, we developed a land use translator (LUT) specific to the needs of RCMs. The annual PFT maps for Europe for the period 1950 to 2015 are derived from the historical LUH2 dataset by applying the LUT backward from 2015 to 1950. Historical changes in the forest type changes are considered using an additional European forest species dataset. The historical changes in the PFT distribution of LUCAS LUC follow closely the land use changes given by LUH2 but differ in some regions compared to other annual LULCC datasets. From 2016 onward, annual PFT maps for future land use change scenarios based on LUH2 are derived for different shared socioeconomic pathway (SSP) and representative concentration pathway (RCP) combinations used in the framework of CMIP6. The resulting LULCC maps can be applied as land use forcing to the new generation of RCM simulations for downscaling of CMIP6 results. The newly developed LUT is transferable to other CORDEX regions worldwide.

Global analysis of land-use changes in karst areas and the implications for water resources

Karst areas contain valuable groundwater resources and high biodiversity, but are particularly vulnerable to climate change and human impacts. Land-use change is the cause and consequence of global environmental change. The releases of the Climate Change Initiative-Land Cover (CCI-LC) and World Karst Aquifer Map (WOKAM) datasets have made it possible to explore global land-use changes in karst areas. This paper firstly analyses the global karst land-use distribution in 2020, as well as the land-use transition characteristics between 1992 and 2020. Then, two indicators, proportion of land-use change and dominant type of land-use change, are proposed to identify the spatial characteristics of land-use change in global karst areas. Finally, three examples of land-use change in karst areas are analyzed in detail. Land-use types and proportions of the global karst areas from large to small are as follows: forest (31.78%), bare area (27.58%), cropland (19.02%), grassland (10.87%), shrubland (7.21%), wetland (1.67%), ice and snow (1.16%) and urban (0.71%). The total area of global karst land-use change is 1.30 million km2, about 4.85% of global karst surface. The land-use change trend of global karst is dominated by afforestation, supplemented by scattered urbanization and agricultural reclamation. The tropical climate has a higher intensity of land-use change. Regions of agricultural reclamation are highly consistent with the population density. These results reflect the impact of human activities and climate change on land-use changes in global karst areas, and serve as a basis for further research and planning of land resource management.

Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model

Abstract. Plant functional types (PFTs) are used to represent vegetation distribution in land surface models (LSMs). Previous studies have shown large differences in the geographical distribution of PFTs currently used in various LSMs, which may arise from the differences in the underlying land cover products but also the methods used to map or reclassify land cover data to the PFTs that a given LSM represents. There are large uncertainties associated with existing PFT mapping methods since they are largely based on expert judgement and therefore are subjective. In this study, we propose a new approach to inform the mapping or the cross-walking process using analyses from sub-pixel fractional error matrices, which allows for a quantitative assessment of the fractional composition of the land cover categories in a dataset. We use the Climate Change Initiative (CCI) land cover product produced by the European Space Agency (ESA). Previous work has shown that compared to fine-resolution maps over Canada, the ESA-CCI product provides an improved land cover distribution compared to that from the GLC2000 dataset currently used in the CLASSIC (Canadian Land Surface Scheme Including Biogeochemical Cycles) model. A tree cover fraction dataset and a fine-resolution land cover map over Canada are used to compute the sub-pixel fractional composition of the land cover classes in ESA-CCI, which is then used to create a cross-walking table for mapping the ESA-CCI land cover categories to nine PFTs represented in the CLASSIC model. There are large differences between the new PFT distributions and those currently used in the model. Offline simulations performed with the CLASSIC model using the ESA-CCI-based PFTs show improved winter albedo compared to that based on the GLC2000 dataset. This emphasizes the importance of accurate representation of vegetation distribution for realistic simulation of surface albedo in LSMs. Results in this study suggest that the sub-pixel fractional composition analyses are an effective way to reduce uncertainties in the PFT mapping process and therefore, to some extent, objectify the otherwise subjective process.

Spatiotemporal Evolution of Arid Ecosystems Using Thematic Land Cover Products

The pathway, direction, and potential drivers of the evolution in global arid ecosystems are of importance for maintaining the stability and sustainability of the global ecosystem. Based on the Climate Change Initiative Land Cover dataset (CCILC), in this study, four indicators of land cover change (LCC) were calculated, i.e., regional change intensity (RCI), rate of change in land cover (CR), evolutionary direction index (EDI), and artificial change percentage (ACP), to progressively derive the intensity, rate, evolutionary direction, and anthropogenic interferences of global arid ecosystems. The LCC from 1992 to 2020 and from 28 consecutive pair-years was observed at the global, continental, and country scales to examine spatiotemporal evolution in the Earth’s arid ecosystems. The following main results were obtained: (1) Global arid ecosystems experienced positive evolution despite complex LCCs and anthropogenic interferences. Cautious steps to avoid potential issues caused by rapid urbanization and farmland expansion are necessary. (2) The arid ecosystems in Australia, Central Asia, and southeastern Africa generally improved, as indicated by EDI values, but those in North America were degraded, with 41.1% of LCCs associated with urbanization or farming. The arid ecosystems in South America also deteriorated, but 83.4% of LCCs were in natural land covers. The arid ecosystems in Europe slightly improved with overall equivalent changes in natural and artificial land covers. (3) Global arid ecosystems experienced three phases of change based on RCI values: ‘intense’ (1992–1998), ‘stable’ (1998–2014), and ‘intense’ (2014–2020). In addition, two phases of evolution based on EDI values were observed: ‘deterioration’ (1992–2002) and ‘improvement’ (2002–2020). The ACP values indicated that urbanization and farming activities contributed increasingly less to global dryland change since 1992. These findings provide critical insights into the evolution of global arid ecosystems based on analyses of LCCs and will be beneficial for sustainable development of arid ecosystems worldwide within the context of ongoing climate change.

The Verification of Land Cover Datasets with the Geo-Tagged Natural Scene Images

Land cover is important for global change studies, and its accuracy and reliability are usually verified by field sampling, which costs a lot. A method was proposed for the verification of land cover datasets with the geo-tagged natural scene images using a convolutional neural network. The nature scene images were firstly collected from the Land Use and Cover Area frame Survey (LUCAS) and global crowdsourcing images platform Flickr, then classified according to the Land Cover Classification System. The Nature Scene Image Classification (NSIC) model based on the GoogLeNet Inception network for recognition of natural scene images was then constructed. Finally, in the UK, as a verification area, the European Space Agency Climate Change Initiative Land Cover (ESA CCI-LC) datasets and the Global land-cover product with fine classification system (GLC-FCS) were verified using the NSIC-Inception model with the nature scene image set. The verification results showed that the overall accuracy verified by LUCAS was very close to the accuracy of the land cover product, which was 94.41% of CCI LC and 92.89% of GLC-FCS, demonstrating the feasibility of using geo-tagged images classified by the NSIC model. In addition, the VGG16 and ResNet50 were compared with GoogLeNet Inception. The differences in verification between LUCAS and Flickr images were discussed regarding the image’s quantity, the spatial distribution, the representativeness, and so on. The uncertainties of verification arising from differences in the spatial resolution of the different datasets were explored by CCI LC and GCL-FCS. The application of the method has great potential to support and improve the efficiency of land cover verification.

Impact of armed conflict on land use and land cover changes in global border areas

AbstractNational borderlands as the spatial carrier and forefront for exerting geopolinomical influence and implementing geopolinomical strategy, most have the same or similar eco‐geographical structure characteristics, such as land cover. However, there are different development trends in different national relationships (e.g., cooperation and conflict), and land use and land cover changes (LUCC), especially in apparent in border areas. In general, armed conflict in border areas is accompanied by severe, immediate and long‐lasting LUCC‐like forest loss. Here, by means of geospatial analysis, the Uppsala Conflict Data Program and Climate Change Initiative Land Cover dataset were used to investigate the linkages between the armed conflict and LUCC or the patterns and processes of LUCC before, during and after armed conflict around the globe in borderlands from 1992 to 2020. The main and key insights are: (1) nearly 50% of global armed conflict has occurred and re‐occurred in these borderlands since 1992, leading to about 16.5% of LUCC globally; (2) the impact of armed conflict on LUUC is hysteretic, based on the intensity of conflict, especially in borderlands; (3) the transfer rate of land use is high during the conflict and tends to be stable in the post‐conflict period; (4) the absolute amount of armed conflict damage to forest land is large, but the changes rate of impervious surfaces is far more than the former, nearly 18 times greater. These findings improve the understanding of the impact of armed conflict on LUCC, in border areas. This study could provide a proxy and benchmark for exploring the cause‐effect and impact‐response between geopolinomical relations and the borderlands LUCC.

Global deforestation revisited: The role of weak institutions

Linking weak governance and forest degradation has received increasing attention in scientific and political spheres. Deforestation remains a global matter as a major agent of greenhouse gas (GHG) emissions, for endangering the lives of several plant and animal species, and for triggering political disputes involving land tenure and rural violence. Political factors are acknowledged to have a direct impact on forest resources management. Corruption and weak governance are able to deflect policies to private interests, and encourage illegal logging and unlawful allowances to forest degradation even in protected areas. However, the effects of corruption and weak institutions in forest management are still uncertain. This paper offers empirical-based evidence about the relationship between institutional factors and forest cover conversion. The role of weak institutions is explored by employing a logistic model of recent high-resolution global remote sensing data from the European Space Agency (ESA) Climate Change Initiative Land Cover (CCI-LC) from 1992 and 2015. We assess the cross-country associations of the Corruption Perception Index (CPI) and the World Bank Government Effectiveness (GE) index while controlling for physiographic and structural variables. Results are robust and show, as expected, that difficult access areas pose considerable barriers to forest conversion, and regions of high agricultural suitability are more likely to be converted from forests to agricultural fields. Furthermore, higher government effectiveness with stronger political enforcement, policy design, and lower corruption perception are significantly related to a lower probability of deforestation. Further elaborating governance and corruption indicators with emphasis on forest management/conservation can potentially improve the accuracy of local and cross-country quantitative land use studies. Our findings support the continuous understanding of weak institutions in deforestation debates. The paper highlights the need to fight corruption and to build strong institutions into effective policy strategies.

Evaluation of MODIS, Climate Change Initiative, and CORINE Land Cover Products Based on a Ground Truth Dataset in a Mediterranean Landscape

Land cover can reflect global environmental changes if their associated transitions are quantitatively and correctly analysed, thus helping to assess the drivers and impacts of climate change and other applied research studies. It is highly important to acquire accurate spatial land cover information to perform multidisciplinary analyses. This work aims at estimating the accuracy of three widely used land cover products, the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1), the European Space Agency Climate Change Initiative land cover (ESA-CCI-LC), and the EU CORINE land cover (CLC), all for the reference year of 2018, by comparing them against a fine resolution land cover dataset created for this study with combined ground surveys and high-resolution Large Scale Orthophotography (LSO 25/2015). Initially, the four datasets had their land cover classes harmonized and all were resampled to the same spatial resolution. The accuracy metrics used to conduct the comparisons were Overall Accuracy, Producer’s Accuracy, User’s Accuracy, and the Kappa Coefficient. Comparisons with the reference dataset revealed an underestimation of the forested areas class in all three compared products. Further analysis showed that the accuracy metrics were reasonably high for the broad classes (forest vs. non-forest), with an overall accuracy exceeding 70% in all examined products. On the contrary, in the detailed classification (total land cover mapping), the comparison of the reference dataset with the three land cover products highlighted specific weaknesses in the classification results of the three products, showing that CLC depicted more precisely the landscape characteristics than the two other products, since it demonstrated the highest overall accuracy (37.47%), while MODIS and ESA-CCI-LC revealed a percentage that did not exceed 22%.

Land cover change and multiple remotely sensed datasets consistency in China

Introduction Although numerous land cover datasets can act as references for understanding land cover change in China, the inconsistencies between the datasets can also provide understanding. Previous studies on the consistency between land cover datasets have mostly focused on land cover type consistencies and have ignored data consistencies in land cover change. Outcomes Therefore, we aim to analyse the consistencies in land cover changes through likelihood assessment methods. We compared the spatiotemporal changes in forest, grassland, cropland, and bare land in the Climate Change Initiative land cover dataset (CCI-LC), Moderate-resolution Resolution Imaging Spectroradiometer land cover dataset (MCD12Q1), China’s National Land Use and Cover Change (CNLUCC), Globeland30 and Global Land Cover Fine Surface Covering 30 (GLC-FCS30) datasets in 2010. The results showed that the percentages and changes in each land cover type in MCD12Q1 were different from those in the other datasets. Discussion For example, the proportion of grassland in MCD12Q1 was the highest, reaching 48.04%. The places with high consistency were the places where the land cover types were concentrated, and the bare land had the highest consistency. However, the consistency of China’s land cover change was quite low, and the percentage of low consistency was more than 87% from 2000-2018. Comparison of the data with the global artificial impervious area (GAIA) and Hansen-Global Forest Change (Hansen-GFC) datasets showed that the percentage of high construction gain consistency (38.83%) was higher than the forest change consistency, and the percentage forest loss high consistency (8.85%) was lower than the forest gain high consistency (12.76%). Conclusion The results not only provide a basis for the use of land cover datasets but also give a clearer understanding of the pattern of land cover changes.

High-resolution land use and land cover dataset for regional climate modelling: a plant functional type map for Europe 2015

Abstract. The concept of plant functional types (PFTs) is shown to be beneficial in representing the complexity of plant characteristics in land use and climate change studies using regional climate models (RCMs). By representing land use and land cover (LULC) as functional traits, responses and effects of specific plant communities can be directly coupled to the lowest atmospheric layers. To meet the requirements of RCMs for realistic LULC distribution, we developed a PFT dataset for Europe (LANDMATE PFT Version 1.0; http://doi.org/10.26050/WDCC/LM_PFT_LandCov_EUR2015_v1.0, Reinhart et al., 2021b). The dataset is based on the high-resolution European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset and is further improved through the additional use of climate information. Within the LANDMATE – LAND surface Modifications and its feedbacks on local and regional cliMATE – PFT dataset, satellite-based LULC information and climate data are combined to create the representation of the diverse plant communities and their functions in the respective regional ecosystems while keeping the dataset most flexible for application in RCMs. Each LULC class of ESA-CCI is translated into PFT or PFT fractions including climate information by using the Holdridge life zone concept. Through consideration of regional climate data, the resulting PFT map for Europe is regionally customized. A thorough evaluation of the LANDMATE PFT dataset is done using a comprehensive ground truth database over the European continent. The assessment shows that the dominant LULC types, cropland and woodland, are well represented within the dataset, while uncertainties are found for some less represented LULC types. The LANDMATE PFT dataset provides a realistic, high-resolution LULC distribution for implementation in RCMs and is used as a basis for the Land Use and Climate Across Scales (LUCAS) Land Use Change (LUC) dataset which is available for use as LULC change input for RCM experiment set-ups focused on investigating LULC change impact.

Analysis of the Relationship between Climate Change and Land Use Change using the ESA CCI Land Cover Maps in Sungai Kelantan Basin, Malaysia

Climate and land use land cover (LULC) are two major key drives that affect the hydrological cycle pattern within a river basin. This study aims to evaluate the capability of the European Space Agency Climate Change Initiative land cover (ESA CCI LC) product in representing land use pattern in Sungai Kelantan Basin (KRB), Malaysia; to analyze the changes of rainfall, maximum and minimum temperatures of the KRB from 1979 to 2018; and to assess the relationships between climate and land use changes within KRB. The results show that the ESA CCI LC product is applicable to represent LULC of the KRB, with an overall accuracy of 74.72% and a Kappa coefficient of 0.66. Trend analysis indicates that maximum and minimum temperatures of the KRB increased significantly at 95% confident level by 0.01 to 0.03 °C/year and 0.01 to 0.05 °C/year, respectively. Whereas annual precipitation at the Pos Hau, Pos Bihai, Pos Gob, and Kuala Krai stations increased significantly with the rates of 17.5 to 29.74 mm/year. The correlation shows that urban expansion in Gua Musang had a strong correlation with annual rainfall with a correlation coefficient (CC) value of -0.78. Furthermore, the conversion of forest into croplands over the Tanah Merah increased the minimum temperature significantly, with a CC value of -0.73. The findings show that the changes of land use in the middle basin contributed to the variations in temperature.

Consistency Assessments of the Land Cover Products on the Tibetan Plateau

Land cover and land cover change information are essential data in terrestrial surface research. However, the land cover products are highly inconsistent, especially in the mountainous area. Most land cover assessment studies focused on the consistency of spatial patterns, while ignoring the consistency of change and elevation heterogeneity. In this study, four land cover products were assessed for spatiotemporal consistency on the Tibetan Plateau (TP), including the Moderate resolution Imaging Spectroradiometer land cover (MCD12Q1), the Climate Change Initiative Land Cover (CCI-LC), the 30-meter Global Land Cover (Globeland30) and the Multi-Period Land Use/Land Cover Remote Sensing Monitoring Database in China (CNLUCC). The impact of elevation on the consistency of multiple land cover products across spatiotemporal scales was further analyzed. The spatial consistency of the three and more products was about 70%, with higher consistency for grassland and bare land and lower consistency for wetland and shrubland on the TP. Globeland30 and CCI-LC were better than others for overall monitoring, with the inconsistency of less than 45% by google earth dataset validation. The temporal change consistency of the four land cover products was less than 10%. With increasing elevation, the average spatial consistency decreased and the land cover change area and temporal change consistency increased. There is a high inconsistency of land cover changes on the TP in existing commonly used products, demonstrating the need to develop high-quality land cover products in long time series.

China's ecosystem service value in 1992–2018: Pattern and anthropogenic driving factors detection using Bayesian spatiotemporal hierarchy model

Maintaining ecosystem services (ESs) and reducing ecosystem degradation are important goals for achieving sustainable development. However, under the influence of various anthropogenic factors, the total ecosystem service value (ESV) of China continues to decline, and the detailed processes involved in this decline are unclear. In this paper, a new long-term annual land cover dataset (the Climate Change Initiative Land Cover or CCI-LC dataset) with a spatial resolution of 300 m was employed to estimate the ESV of China, and Bayesian spatiotemporal hierarchy models were built to examine the detailed patterns and anthropogenic driving factors. From 1992 to 2018, the total ESV of China fluctuated and decreased from 3265.3 to 3253.29 billion US$ at an average rate of 0.55 billion US$ per year. Furthermore, the model revealed the spatiotemporal variations in the ESV pattern, and simultaneously detected the influences of 9 variables related to economic factors, population, infrastructure, energy, agriculture and ecological restoration, providing a convenient and effective method for ESV spatiotemporal analysis. The results enrich our understanding of the detailed spatiotemporal variation and anthropogenic driving factors underlying the declining ESV in China. These findings have substantial guiding implications for adjusting ecological regulation policies.

Upgrading Land-Cover and Vegetation Seasonality in the ECMWF Coupled System: Verification With FLUXNET Sites, METEOSAT Satellite Land Surface Temperatures, and ERA5 Atmospheric Reanalysis.

In this study, we show that limitations in the representation of land cover and vegetation seasonality in the European Centre for Medium‐Range Weather Forecasting (ECMWF) model are partially responsible for large biases (up to ∼10°C, either positive or negative depending on the region) on the simulated daily maximum land surface temperature (LST) with respect to satellite Earth Observations (EOs) products from the Land Surface Analysis Satellite Application Facility. The error patterns were coherent in offline land‐surface and coupled land‐atmosphere simulations, and in ECMWF's latest generation reanalysis (ERA5). Subsequently, we updated the ECMWF model's land cover characterization leveraging on state‐of‐the‐art EOs—the European Space Agency Climate Change Initiative land cover data set and the Copernicus Global Land Services leaf area index. Additionally, we tested a clumping parameterization, introducing seasonality to the effective low vegetation coverage. The updates reduced the overall daily maximum LST bias and unbiased root‐mean‐squared errors. In contrast, the implemented updates had a neutral impact on daily minimum LST. Our results also highlighted the complex regional heterogeneities in the atmospheric sensitivity to land cover and vegetation changes, particularly with issues emerging over eastern Brazil and northeastern Asia. These issues called for a re‐calibration of model parameters (e.g., minimum stomatal resistance, roughness length, rooting depth), along with a revision of several model assumptions (e.g., snow shading by high vegetation).

Using Standardized Time Series Land Cover Maps to Monitor the SDG Indicator “Mountain Green Cover Index” and Assess Its Sensitivity to Vegetation Dynamics

SDG indicators are instrumental for the monitoring of countries’ progress towards sustainability goals as set out by the UN Agenda 2030. Earth observation data can facilitate such monitoring and reporting processes, thanks to their intrinsic characteristics of spatial extensive coverage, high spatial, spectral, and temporal resolution, and low costs. EO data can hence be used to regularly assess specific SDG indicators over very large areas, and to extract statistics at any given subnational level. The Food and Agriculture Organization of the United Nations (FAO) is the custodian agency for 21 out of the 231 SDG indicators. To fulfill this responsibility, it has invested in EO data from the outset, among others, by developing a new SDG indicator directly monitored with EO data: SDG indicator 15.4.2, the Mountain Green Cover Index (MGCI), for which the FAO produced initial baseline estimates in 2017. The MGCI is a very important indicator, allowing the monitoring of the health of mountain ecosystems. The initial FAO methodology involved visual interpretation of land cover types at sample locations defined by a global regular grid that was superimposed on satellite images. While this solution allowed the FAO to establish a first global MGCI baseline and produce MGCI estimates for the large majority of countries, several reporting countries raised concerns regarding: (i) the objectivity of the method; (ii) the difficulty in validating FAO estimates; (iii) the limited involvement of countries in estimating the MGCI; and (iv) the indicator’s limited capacity to account for forest encroachment due to agricultural expansion as well as the undesired expansion of green vegetation in mountain areas, resulting from the effect of global warming. To address such concerns, in 2020, the FAO introduced a new data collection approach that directly measures the indicator through a quantitative analysis of standardized land cover maps (European Space Agency Climate Change Initiative Land Cover maps—ESA CCI-LC). In so doing, this new approach addresses the first three of the four issues, while it also provides stronger grounds to develop a solution for the fourth issue—a solution that the FAO plans to present to the Interagency and Expert Group on SDG Indicators (IAEG-SDG) at its autumn 2021 session. This study (i) describes the new approach to estimate the MGCI indicator using ESA’s CCI-LC and products, (ii) assesses the accuracy of the new approach; (iii) reviews the limitations of the current SDG indicator definition to monitor progress towards SDG 15.4; and (iv) reflects on possible further adjustments of the indicator methodology in order to address them.

Global and regional impacts of land cover changes on isoprene emissions derived from spaceborne data and the MEGAN model

Abstract. Among the biogenic volatile organic compounds (BVOCs) emitted by plant foliage, isoprene is by far the most important in terms of both global emission and atmospheric impact. It is highly reactive in the air, and its degradation favours the generation of ozone (in the presence of NOx) and secondary organic aerosols. A critical aspect of BVOC emission modelling is the representation of land use and land cover (LULC). The current emission inventories are usually based on land cover maps that are either modelled and dynamic or satellite-based and static. In this study, we use the state-of-the-art Model of Emissions of Gases and Aerosols from Nature (MEGAN) model coupled with the canopy model MOHYCAN (Model for Hydrocarbon emissions by the CANopy) to generate and evaluate emission inventories relying on satellite-based LULC maps at annual time steps. To this purpose, we first intercompare the distribution and evolution (2001–2016) of tree coverage from three global satellite-based datasets, MODerate resolution Imaging Spectroradiometer (MODIS), ESA Climate Change Initiative Land Cover (ESA CCI-LC), and the Global Forest Watch (GFW), and from national inventories. Substantial differences are found between the datasets; e.g. the global areal coverage of trees ranges from 30 to 50×106 km2, with trends spanning from −0.26 to +0.03 % yr−1 between 2001 and 2016. At the national level, the increasing trends in forest cover reported by some national inventories (in particular for the US) are contradicted by all remotely sensed datasets. To a great extent, these discrepancies stem from the plurality of definitions of forest used. According to some local censuses, clear cut areas and seedling or young trees are classified as forest, while satellite-based mappings of trees rely on a minimum height. Three inventories of isoprene emissions are generated, differing only in their LULC datasets used as input: (i) the static distribution of the stand-alone version of MEGAN, (ii) the time-dependent MODIS land cover dataset, and (iii) the MODIS dataset modified to match the tree cover distribution from the GFW database. The mean annual isoprene emissions (350–520 Tg yr−1) span a wide range due to differences in tree distributions, especially in isoprene-rich regions. The impact of LULC changes is a mitigating effect ranging from 0.04 to 0.33 % yr−1 on the positive trends (0.94 % yr−1) mainly driven by temperature and solar radiation. This study highlights the uncertainty in spatial distributions of and temporal variability in isoprene associated with remotely sensed LULC datasets. The interannual variability in the emissions is evaluated against spaceborne observations of formaldehyde (HCHO), a major isoprene oxidation product, through simulations using the global chemistry transport model (CTM) IMAGESv2. A high correlation (R > 0.8) is found between the observed and simulated interannual variability in HCHO columns in most forested regions. The implementation of LULC change has little impact on this correlation due to the dominance of meteorology as a driver of short-term interannual variability. Nevertheless, the simulation accounting for the large tree cover declines of the GFW database over several regions, notably Indonesia and Mato Grosso in Brazil, provides the best agreement with the HCHO column trends observed by the Ozone Monitoring Instrument (OMI). Overall, our study indicates that the continuous tree cover fields at fine resolution provided by the GFW database are our preferred choice for constraining LULC (in combination with discrete LULC maps such as those of MODIS) in biogenic isoprene emission models.

Improvement of the ESA CCI Land cover maps for water balance analysis in tropical regions: A case study in the Muda River Basin, Malaysia

Study regionThe Muda River Basin (MRB), Malaysia. Study FocusThis study proposed a framework to improve the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) products through the integration with the Annual Oil Palm Dataset (AOPD). The improved land use land cover (LULC) maps were then used to produce five LULC scenarios as input maps into the Soil and Water Assessment Tool (SWAT) model for analyzing the impact of LULC changes on water balance in the MRB. New hydrological insights for the regionThe improved LULC maps have good performance in representing rubber and oil palm, with an overall accuracy up to 81 %. In addition, SWAT simulated monthly streamflow well for the MRB, with the highest R2 and NSE values of 0.84 and 0.86, respectively. During the 2001–2016 period, the MRB experienced an expansion of oil palm from 7.10%–17.36 %, a reduction of rubber from 34.93 % to 26.38 % and a slight decrease in forest from 54.23%–52.80 %. The urban expansion scenario showed significant increment in surface runoff, while the reforestation scenario helped to reduce surface runoff, while increase lateral flow and groundwater. Oil palm expansion led to a higher reduction in lateral flow and groundwater than rubber trees due to the higher soil water absorption rate. The proposed framework can be duplicated and applied in other tropical basins, particularly in Indonesia and Malaysia.

A Spatial and Temporal Assessment of Vegetation Greening and Precipitation Changes for Monitoring Vegetation Dynamics in Climate Zones over Africa

Vegetation is vital, and its greening depends on access to water. Thus, precipitation has a considerable influence on the health and condition of vegetation and its amount and timing depend on the climatic zone. Therefore, it is extremely important to monitor the state of vegetation according to the movements of precipitation in climatic zones. Although a lot of research has been conducted, most of it has not paid much attention to climatic zones in the study of plant health and precipitation. Thus, this paper aims to study the plant health in five African climatic zones. The linear regression model, the persistence index (PI), and the Pearson correlation coefficients were applied for the third generation Normalized Difference Vegetation Index (NDVI3g), with Climate Hazard Group infrared precipitation and Climate Change Initiative Land Cover for 34 years (1982 to 2015). This involves identifying plants in danger of extinction or in dramatic decline and the relationship between vegetation and rainfall by climate zone. The forest type classified as tree cover, broadleaved, deciduous, closed to open (>15%) has been degraded to 74% of its initial total area. The results also revealed that, during the study period, the vegetation of the tropical, polar, and warm temperate zones showed a higher rate of strong improvement. Although arid and boreal zones show a low rate of strong improvement, they are those that experience a low percentage of strong degradation. The continental vegetation is drastically decreasing, especially forests, and in areas with low vegetation, compared to more vegetated areas, there is more emphasis on the conservation of existing plants. The variability in precipitation is excessively hard to tolerate for more types of vegetation.

High agricultural water consumption led to the continued shrinkage of the Aral Sea during 1992-2015.

The shrinkage of the Aral Sea started in the 1960s, and it has been continued for decades due to arguably both human and natural causes. However, the change of the Aral Sea in the post-Soviet era and its correlations with other changes in the extent of the basin have yet to be fully investigated. Here, we studied the land cover dynamics of the entire Aral Sea basin during 1992-2015 from the perspective of the surrounding environment, in order to investigate the causes of the Aral Sea further shrinkage in recent years. We used the annual Climate Change Initiative (CCI) land cover dataset to provide a spatiotemporally consistent delineation of land cover throughout the period. We found that: (1) In recent years, the Aral Sea continued shrinkage, approximately 50.38% of its water area in 1992 had dried out and turned into bare land by 2015. (2) The cultivated land area remained stable with a slight increase during the period, suggesting that no large-scale abandonment or expansion of farming extent occurred in the post-Soviet era. (3) Among other land types, urban areas are small and slightly expand at a rate of 0.024×104km2/year, suggesting urbanization, and likely contribute to more water consumption. Our investigation also found that climate warming increased the upstream runoff, which has a positive effect on the water supply of the Aral Sea. The impact of human activity on the Aral Sea is more pronounced than climate change. Therefore, the continued shrinkage of the Aral Sea was likely due to high water consumption of agriculture continues to exert the influence that existed in the 1960s. Other factors, such as urbanization have exacerbated this effect. The study examined the continued shrinkage of the Aral Sea in post-Soviet era, to provide an insight into the driving factors of the complex and still controversial Aral Sea crisis.