Revealing remote sensing blind spots in human-driven land transitions: a systematic evaluation of state-of-the-art land use and land cover datasets on the Tibetan Plateau

Multiple land use and land cover (LUC) datasets are available for the analysis of LUC changes (LUCC) in distinct territories. Sometimes, different LUCC results are produced to characterize these changes for the same territory and the same period. These differences reflect: (1) The different properties of LUC geoinformation (GI) used in the LUCC assessment, and (2) different criteria used for vector-to-raster conversion, namely, those deriving from outputs with different spatial resolutions. In this research, we analyze LUCC in mainland Portugal using two LUC datasets with different properties: Corine Land Cover (CLC 2006 and 2012) and LUC official maps of Portugal (Carta de Ocupação do Solo, COS 2007 and 2010) provided by the European Environment Agency (EEA) and the General Directorate for Territorial Development (DGT). Each LUC dataset has undergone vector-to-raster conversion, with different resolutions (10, 25, 50, 100, and 200 m). LUCC were analyzed based on the vector GI of each LUC dataset, and with LUC raster outputs using different resolutions. Initially, it was observed that the areas with different LUC types in two LUC datasets in vector format were not similar—a fact explained by the different properties of this type of GI. When using raster GI to perform the analysis of LUCC, it was observed that at high resolutions, the results are identical to the results obtained when using vector GI, but this ratio decreases with increased cell size. In the analysis of LUCC results obtained with raster LUC GI, the outputs with pixel size greater than 100 m do not follow the same trend of LUCC obtained with high raster resolutions or using LUCC obtained with vector GI. These results point out the importance of the factor form and the area of the polygons, and different effects of amalgamation and dilation in the vector-to-raster conversion process, more evident at low resolutions. These findings are important for future evaluations of LUCC that integrate raster GI and vector/raster conversions, because the different LUC GI resolution in line with accuracy can explain the different results obtained in the evaluation of LUCC. The present work demonstrates this fact, i.e., the effects of vector-to-raster conversions using various resolutions culminated in different results of LUCC.

Land use and cover changes on the Loess Plateau: A comparison of six global or national land use and cover datasets

Land use and land cover (LULC) information is an important component influencing watershed modeling with regards to hydrology and water quality in the river basin. In this study, the sensitivity of the Soil and Water Assessment Tool (SWAT) model to LULC datasets with three points in time and three levels of detail was assessed in a coastal subtropical watershed located in Southeast China. The results showed good agreement between observed and simulated values for both monthly and daily streamflow and monthly NH4+-N and TP loads. Three LULC datasets in 2002, 2007 and 2010 had relatively little influence on simulated monthly and daily streamflow, whereas they exhibited greater effects on simulated monthly NH4+-N and TP loads. When using the two LULC datasets in 2007 and 2010 compared with that in 2002, the relative differences in predicted monthly NH4+-N and TP loads were −11.0 to −7.8% and −4.8 to −9.0%, respectively. There were no significant differences in simulated monthly and daily streamflow when using the three LULC datasets with ten, five and three categories. When using LULC datasets from ten categories compared to five and three categories, the relative differences in predicted monthly NH4+-N and TP loads were −6.6 to −6.5% and −13.3 to −7.3%, respectively. Overall, the sensitivity of the SWAT model to LULC datasets with different points in time and levels of detail was lower in monthly and daily streamflow simulation than in monthly NH4+-N and TP loads prediction. This research provided helpful insights into the influence of LULC datasets on watershed modeling.

Downscaling of wind speeds with the Weather Research and Forecasting model (WRF) model requires inputs from datasets such as Meteorological and Land Use and Land Cover (LULC) datasets. The accuracy of these datasets is among the factors that significantly impact the accuracy of the wind speeds that are generated by the model. In this study, we assess the accuracy of wind speeds data that are downscaled for an area in coastal Ghana using six meteorological, and two global Land use and Land Cover (LULC) datasets as inputs to the WRF model. In contrast to the LULC datasets tested, model wind speeds for the area were more significantly impacted by the different meteorological datasets. Meteorological datasets that were produced with higher resolution forecasts combined with more advanced data assimilation techniques produced better estimates of wind speed, and vice versa. The JMA JRA55 Reanalysis, NCEP GFS Analysis data, and ECWMF ERA5 gave the relatively best combinations of wind speed error metrics and are therefore recommended for consideration for downscaling of wind speeds for wind resources assessment in the coastal regions of Ghana. However, the ECWMF ERA5 is preferred as its mean error margins are fairly constant and so should be easier to correct.

Explore the Important Role that the Semantics of Land Use and Land Cover Plays within a Broader Environmental Context Focused on the information semantics of land use and land cover (LULC) and providing a platform for reassessing this field, Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects presents a comprehensive overview of fundamental theories and best practices for applying semantics in LULC. Developed by a team of experts bridging relevant areas related to the subject (LULC studies, ontology, semantic uncertainty, information science, and earth observation), this book encourages effective and critical uses of LULC data and considers practical contexts where LULC semantics can play a vital role. The book includes work on conceptual and technological semantic practices, including but not limited to categorization; the definition of criteria for sets and their members; metadata; documentation for data reuse; ontology logic restrictions; reasoning from text sources; and explicit semantic specifications, ontologies, vocabularies, and design patterns. It also includes use cases from applicable semantics in searches, LULC classification, spatial analysis and visualization, issues of Big Data, knowledge infrastructures and their organization, and integration of bottom-up and top-down approaches to collaboration frameworks and interdisciplinary challenges such as EarthCube. This book: Centers on the link between planning goals, objectives, and policy and land use classification systems Uses examples of maps and databases to draw attention to the problems of semantic integration of land use/cover data Discusses the principles used in a categorization Explores the origins and impacts of semantic variation using the example of land cover Examines how crowd science and human perceptions can be used to improve the quality of land cover datasets, and more Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects offers an up-to-date account of land use/land cover semantics, looks into aspects of semantic data modeling, and discusses current approaches, ongoing developments, and future trends. The book provides guidance to anyone working with land use or land cover data, looking to harmonize categories, repurpose data, or otherwise develop or use LULC datasets.

The Portuguese territory has undergone a relevant land use and land cover changes (LUCC) over the past decades. The land use and land cover (LUC) datasets revision and new datasets production allow us to understand LUCC over time. This study goes further into this analysis using the most recent LUC datasets, covering the whole Portuguese continental area, from 1990 to 2012 and presents innovative knowledge that helps understanding LUC dynamics within this period. It shows that the trends inducing spatial variation within different LUC classes changed over time, revealing different spatial and temporal dynamics of these LUCC in the Portuguese territory. The main LUCC are related to the reduction of forest and certain types of agricultural areas, and an increase in urban areas, but the main LUCC assume different dynamics when taken at the regional scale (NUTS II). Future tendencies within LUCC are also estimated using CA-Markov, and the results point out the tendency of increase and decrease of the LUC types previously mentioned. The areas of LUC datasets with different properties were compared and large area discrepancies were observed for some LUC classes. These assessments are relevant results for the evaluation and understanding of LUCC and namely for the Portuguese planning process in the near future.

Land Use and Land Cover (LULC) datasets are widely used across disciplines, with many users demanding more and better information. Understanding the uncertainties and errors associated to the main LULC datasets is a required step to facilitate their correct use, as well as to identify what could be improved in the future production of these products. CORINE Land Cover is probably the most well-known and used LULC dataset in Europe, especially valuable for the rich time-series that it provides. Despite being produced through a change mapping first approach, which tries to avoid technical errors and uncertainties in the temporal analysis of LULC changes, the Copernicus Land Monitoring Service distributes status layers of CORINE (CSL), which are not valid for change analysis because of their associated errors and uncertainties. The CORINE layers of changes (CHA) remove a lot of these issues, but do not meet the needs of many users. In Portugal, the national authority in charge of producing CORINE, the DGT, has implemented a backdating approach to produce consistent CSL layers that allow change analysis with low levels of uncertainty. Throughout this paper, we evaluate the changes that can be analyzed through all available CORINE layers in Portugal: Copernicus CSL layers; the national DGT CSL layers; and CHA layers. To this end, we aim to assess what type of changes can be studied through each type of layer, their associated sources of uncertainty and the relevance and utility of the Portuguese backdating approach to produce a consistent time-series of LULC maps. The results prove how the Portuguese CORINE layers distributed by the Copernicus Land Monitoring Service contain important sources of uncertainty, which however have been removed through the national backdating methodology. This methodology can be therefore exported for the production of CORINE in other European countries.

Assessing the impact of modified LULC on extreme hydrological event over a complex terrain: A case study for kodagu 2018 flood event

Land use and land cover (LULC) data are a central component of most land-atmosphere interaction studies, but there are two common and highly problematic scale mismatches between LULC and climate data. First, in the spatial domain, researchers rarely consider the impact of scaling up fine-scale LULC data to match coarse-scale climate datasets. Second, in the temporal domain, climate data typically have sub-daily, daily, monthly, or annual resolution, but LULC datasets often have much coarser (e.g., decadal) resolution. We first explored the effect of three spatial scaling methods on correlations among LULC data and a land surface climatic variable, latent heat flux in China. Scaling by a fractional method preserved significant correlations among LULC data and latent heat flux at all three studied scales (0.5°, 1.0°, and 2.5°), whereas nearest-neighbor and majority-aggregation methods caused these correlations to diminish and even become statistically non-significant at coarser spatial scales (i.e., 2.5°). In the temporal domain, we identified fractional changes in croplands, forests, and grasslands in China using a recently developed and annually resolved time series of LULC maps from 1982 to 2012. Relative to common LULC change (LULCC) analyses conducted over two-time steps or several time periods, this annually resolved, 31-year time series of LULC maps enables robust interpretation of LULCC. Specifically, the annual resolution of these data enabled us to more precisely observe three key and statistically significant LULCC trends and transitions that could have consequential effects on land-atmosphere interaction: (1) decreasing grasslands to increasing croplands in the Northeast China plain and the Yellow river basin, (2) decreasing croplands to increasing forests in the Yangtze river basin, and (3) decreasing grasslands to increasing forests in Southwest China. Our study not only demonstrates the importance of using a fractional spatial rescaling method, but also illustrates the value of annually resolved LULC time series for detecting significant trends and transitions in LULCC, thus potentially facilitating a more robust use of remotely sensed data in land-atmosphere interaction studies.

Land Use and Land Cover (LULC) dynamics have a major impact on environmental sustainability, re-source management, and urban development. Effective decision-making depends on correct forecasting of these changes. This research predicts LULC changes with the Random Forest (RF) machine learning tech-nique using satellite-derived data from the National Remote Sensing Centre (NRSC) for the years from 2005 to 2023. The dataset includes various LULC categories such as built-up land, agricultural lands, plan-tation/orchards, forests, wetlands, grasslands, wastelands, and water bodies. This study focuses on an area of Tamilnadu, India, covered by NRSC’s LULC datasets from 2005 to 2023. The Random Forest model was first trained on data for the years from 2005 to 2017 to predict LULC for the next consecutive years 2018–2023, with validated against actual LULC values for Tamilnadu, India from the year 2018 to 2023, achieving high accuracy with a correlation coefficient (R > 0.97 in later years) and decreasing Mean Abso-lute Error. Based on the complete historical dataset from 2005 to 2023, the trained model is then applied to predict LULC changes for the years from 2024 to 2028. The results indicate a significant increase in urban development of 0.001 hectare annually and a consistent decrease in double/triple cropping areas of ap-proximately 1.5 hectare between 2024 and 2028, stable but slightly declining forest cover, and water body spread oscillations. The steady increase in built-up land underscores the importance of controlled urban expansion and the decline in double/triple cropping areas calls for policies that support sustainable farm-ing practices. With forest cover slightly declining, policymakers should strengthen conservation initia-tives, afforestation efforts, and enforce stricter land-use regulations to prevent degradation. The oscillating spread of water bodies highlights the need for improved watershed management strategies.

As an important type of terrestrial carbon sink, forests play a critical role in offsetting anthropogenic fossil fuel CO2 emissions which should help nations to achieve carbon neutrality goals worldwide. According to National Forest Inventory (NFI), China has experienced substantial increases in forest cover by benefiting from national afforestation projects initiated in the l980s. However, none of the current land use and land cover (LULC) data sets can reproduce the long‐term changes of forest cover derived from NFI in China. Here, by combining NFI and 20 LULC data sets, we developed a new method of reconstructing historical forest cover in China from 1980 to 2015 at 5‐year intervals. The new forest cover data set can accurately reproduce the historical changes of forest cover in China during this period. Validated against 3851 field survey samples covering the study period of 1985–2015, the data sets show high accuracy with overall accuracy varying from 76.9% to 99.4%. Accurate long‐term forest cover maps have great potential for use in estimating terrestrial carbon, tracking forest management, and other scientific research studies.

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.

Accurate estimation of Manning’s roughness coefficient is critical for reliable hydraulic modeling of dam-break floods. However, for this type of accident, the lack of historical flood data makes the definition of Manning’s roughness coefficient challenging. This study utilizes high-resolution land use and land cover (LULC) data to determine Manning’s roughness coefficient values for application in dam-breaking studies. This study assesses the influence of a regional high-resolution LULC dataset (MapBiomas) and two high-resolution LULC datasets (Dynamic World and ESRI 10 m Annual) on hydraulic parameters related to flood wave propagation and flood hazard assessment. The simulations indicated substantial variations in flood behavior across the Dynamic World generated predominant regions with elevated Manning values, resulting in expanded flood zones and heightened flow attenuation simulations. Conversely, the ESRI 10 m Annual exhibited predominant regions of lower roughness, leading to simulations with diminished flood areas, reduced propagation times, and decreased attenuation of peak flows. Compared with the other LULC datasets, MapBiomas demonstrated a balanced representation of the Manning coefficient’s domains and yielded intermediate outcomes. These discrepancies highlight the challenges associated with accurately determining Manning’s values to ensure precise outcomes in flood modeling. The quality of this modeling is critical for identifying risks, formulating emergency responses, and implementing effective mitigation strategies in downstream regions.

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.

In India, human population has increased six-fold from 200 million to 1200 million that coupled with economic growth has resulted in significant land use and land cover (LULC) changes during 1880–2010. However, large discrepancies in the existing LULC datasets have hindered our efforts to better understand interactions among human activities, climate systems, and ecosystem in India. In this study, we incorporated high-resolution remote sensing datasets from Resourcesat-1 and historical archives at district (N=590) and state (N=30) levels to generate LULC datasets at 5arc minute resolution during 1880–2010 in India. Results have shown that a significant loss of forests (from 89millionha to 63millionha) has occurred during the study period. Interestingly, the deforestation rate was relatively greater under the British rule (1880–1950s) and early decades after independence, and then decreased after the 1980s due to government policies to protect the forests. In contrast to forests, cropland area has increased from 92millionha to 140.1millionha during 1880–2010. Greater cropland expansion has occurred during the 1950–1980s that coincided with the period of farm mechanization, electrification, and introduction of high yielding crop varieties as a result of government policies to achieve self-sufficiency in food production. The rate of urbanization was slower during 1880–1940 but significantly increased after the 1950s probably due to rapid increase in population and economic growth in India. Our study provides the most reliable estimations of historical LULC at regional scale in India. This is the first attempt to incorporate newly developed high-resolution remote sensing datasets and inventory archives to reconstruct the time series of LULC records for such a long period in India. The spatial and temporal information on LULC derived from this study could be used by ecosystem, hydrological, and climate modeling as well as by policy makers for assessing the impacts of LULC on regional climate, water resources, and biogeochemical cycles in terrestrial ecosystems.