Land Use And Land Cover Datasets Research Articles

Non‐Radiative Effect of Land Use/Land Cover Differences on Land Surface Temperature Simulated by Noah‐MP Land Surface Model Over East Asia

AbstractBiophysical effects of land cover changes on land surface temperature (Ts), including two effects, that is, radiative effects (caused by surface albedo changes) versus non‐radiative effects (caused by changes in the energy flux distribution), have attracted the attention of many researchers. However, the uncertainties of land use and land cover (LULC) and vegetation schemes on non‐radiative effects, and the roles of non‐radiative effects on Ts, remain uncertain. In this study, we investigate the local impact of LULC differences by comparing Noah with multiparameterization (Noah‐MP) land model simulations with different LULC data sets and those with vegetation schemes whether they include interactive canopies. We attribute Ts changes induced by LULC differences obtained from Noah‐MP simulations to radiative and non‐radiative effects over East Asia based on the decomposed temperature metric theory. The results indicate that vegetation cools Ts over urban regions by 4.0–6.0°C. Grassland and cropland decrease the Ts over barren regions. Forests are warmer (cooler) than grassland and cropland in high latitudes (in mid and low latitudes). Grassland is cooler than cropland. However, the magnitude of Ts changes induced by these differences is lower than that induced by differences between vegetation and urban. The non‐radiative effect generally explains more than 50% of the local changes in Ts. The interactive canopies enlarge Ts changes for afforestation or replacements of urban region by vegetation. The effect of uncertainties in LULC data sets is negligible. This study highlights the importance of non‐radiative biophysical effects and interactive canopies on local changes in Ts.

Dealing with the uncertainty of technical changes in the CORINE Land Cover dataset: The Portuguese approach

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.

Reconstructing Long‐Term Forest Cover in China by Fusing National Forest Inventory and 20 Land Use and Land Cover Data Sets

AbstractAs 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.

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

The livelihoods and biodiversity in the Kodagu district of Karnataka are greatly altered by the growing urbanization through huge deforestation. The changes in the Land Use and Land Cover (LULC) patterns have important implications on the heavy rainfall activity due to significant alterations in the spatial distribution of existing land-surface properties. Hence, in order to make region (like Kodagu) more climate resilient; it is utmost important to construct a novel coupled framework for hydro-meteorological prediction to assess the impact of changing LULC (degradation of forest cover owing to urban expansion) on extreme rainfall events. Only a well-calibrated, validated, and optimized coupled high-resolution hydrological-atmospheric modelling system can be one of the best tools to study such complex problems. Thus in the present study, we have enhanced the forecasting capability of the state-of-the-art Weather Research and Forecasting (WRF) model by coupling with a physically based, fully distributed, WRF-Hydro. The evaluation of the proposed model configurations (stand-alone and coupled mode) is done with respect to two LULC datasets (USGS, 1992: less urbanized scenario and ISRO, 2017: more urbanized scenario) to explore the underlying causes and hydrological responses behind the 2018 Kodagu flood event. The simulations have been contended with both the stand-alone and coupled WRF/WRF-hydro models with different LULC datasets and a comparative evaluation have been carried out. The simulated variables (rainfall, runoff, surface fluxes, and peak stream discharge) are evaluated and compared with observation to assess the impact of changing LULC pattern during the flood event.

Impact of land-use land-cover datasets and urban parameterization on weather simulation over the Jakarta Metropolitan Area

Human-caused changes in land-use and land-cover (LULC) are most visible in metropolitan areas, when the majority of the land has been converted to urban land or built up. This study presents a modeling approach for simulating the spatiotemporal distribution of urban microclimate with the Weather Research and Forecasting (WRF) model using four urban parameterization schemes, namely a bulk, urban canopy model (UCM), building effect parameterization (BEP), and building energy model (BEM). The WRF model is set-up at 1 km spatial resolutions over the Jakarta Metropolitan Area to study the model’s sensitivity to the usage of alternative LULC datasets, the default MODIS and its modification 2017. The results show that the UCM and BEM schemes appear to be reliable in mapping urban weather conditions for all meteorological parameters examined. Given that the LULC categories in urban areas remained unchanged, changing the LULC in the model did not result in a large difference in error. The LULC dataset, on the other hand, can be used as information related to suburban areas that continue to grow in concurrently with urbanization. LULC updates can provide insight into how much temperature rise is occurring in urban areas and how it affects climate change.

Long-Term Changes of Land Use and Land Cover in the Yangtze River Basin from 1990–2020 Landsat Data

Economic development and climate change drive the land use and land cover (LULC) change globally. Annual robust maps of LULC are critical for studying climate change and land–climate interaction. However, the current existing methods for optimizing and expanding the publicly available China land cover data set (CLCD) are limited. In this article, 30-m annual LULC changes are obtained from 1990 to 2020 in the Yangtze River basin (YRB). The results show an overall accuracy rate of 82.66% and better performances on Geo-Wiki test samples when compared to similar products. Based on our 30-m annual LULC data set, the drastic LULC changes are found in YRB over a 30-year period, where impervious surface area more than tripled, cropland area decreased by 6.12%, and water area decreased by 6.09%. In addition, through the geographically and temporally weighted regression method, a fitting model with a goodness of fit of 0.91 well reveals that human activity plays a driving role in the LULC change of YRB.

Evaluating the relation between land use changes and the 2018 landslide disaster in Kerala, India

Land use and land cover changes (LULCCs) in mountainous areas may increase the susceptibility to landslides due to modifications of topography, vegetation, and material characteristics. Understanding the relation between LULCCs and landslide occurrences is important for landslide prevention and land resources management. In this study, these changes were analyzed for the landslides that were triggered during the 2018 monsoon event in Kerala, India. The changes in land use and land cover (LULC) that took place in a period of eight years prior to the 2018 event were analyzed for 4,728 landslide initiation points in the entire state, and for a subset of 2,223 landslides in the most affected district of Idukki. Apart from this, the initiation points were compared to those in steep areas and landslide susceptible zones. For these comparisons, we used LULC datasets for the period between 2000 and 2018 that were obtained from national organizations, derived from satellite image classification and visual interpretation. The LULC datasets lacked coherent classification schemes, so a standardization of LULC types was made. The results of these comparisons reveal that more than half of the landslides (58%) occurred in densely vegetated areas, and that 50% of the landslides that caused damage to buildings and roads originated in forest plantations, followed by built-up areas (25%). For most of the landslide locations (90% in Kerala and 83% in Idukki) there was no noticeable change in major LULC in the period prior to the event. Results of this study indicate that LULCCs for the the period 2010-2018 had less influence on the landslides occurred in 2018.

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

Image classification often produces large deviations between land use and land cover (LULC) datasets and the ‘real’ changes, leading to uncertainty in the results of LULC related assessments and the propagated impacts through modelling. LULC products are widely used as input for various large-scale climatic, ecological and hydrological models, but the accuracy and authenticity associated with data quality are rarely fully considered. In the study, six widely used global or national LULC datasets, MODIS-MCD12Q1, EAS CCI-LC, GlobeLand30, GLASS-GLC, CAS-CLUDs and ChinaCover, are used to assess the consistency and reliability of LULC on the Loess Plateau, where land cover has undergone major changes due to “Grain to Green” Project. Results show that MODIS and GLASS products have low quality, with the overall accuracy of 55.3~58.2% and 34.7~39.4% respectively, and the areal and spatial results cannot reflect the real changes of the Loess Plateau. Large areas of croplands in MODIS-MCD12Q1 are classified as natural grassland. Croplands in GLASS-GLC are overestimated in the central parts of the Loess Plateau. Both of MODIS and GLASS products are hard to separate woodlands from grasslands. ESA CCI-LC has higher classification accuracy (73.9%~74.2%) than the released MODIS and GLASS products. The woodlands in ESA CCI-LC is relatively underestimated than that of CAS-CLUD and ChinaCover, and the conversion feature from cropland to forest and grasses is almost absent on ESA CCI-LC maps. Although GlobeLand30 has a high overall accuracy at 86. 6~86.7%, it is inadequate to get the characteristic of returning of cropland to forest and grasses. The most similar land covers are CAS-CLUDs and ChinaCover, which are considered to have highest classification accuracy ranging from 89.4% to 91.6% and can reflect the actual LULC status and its changes on the Loess Plateau. A blending LULC dataset is developed and the overall accuracies for all classes can be improved by 1.63–7.49%.

Accuracy comparison and driving factor analysis of LULC changes using multi-source time-series remote sensing data in a coastal area

Land use and land cover (LULC) products derived from remote sensing data are fundamental to the earth's surface ecological environment studies. Various LULC products have different data sources with large uncertainties and varying spatio-temporal resolutions. In this study, our objectives are to understand the accuracy of the current LULC products at the coastal area of Tianjin city. We conducted accuracy verification and comparative analysis of the LULC datasets available from different sources, MCD12Q1, CGLS-LC100, FROM_GLC, GLC_FCS30 and GlobeLand30 for the years of 2010, 2015, 2017 and 2020, at varying spatial resolutions 500–100–30–10 m. Using land cover dataset for these years, we conducted time-series analysis of land cover changes and driving factors. Results showed that GLC_FCS30-2020 has the highest classification accuracy (72.57%), followed by GlobeLand30-2020 (72.23%) and GlobeLand30-2010 (70.10%). The classification accuracy is related to the spatial resolution of original data source and the classification system. The verification accuracies of all products are found to be lower than their official announcements. Additionally, the accuracy comparison among different land types indicated that the classification accuracy of agricultural land, impervious surface, and water bodies is high, while that of forest, grassland, and shrubs is low. According to the spatio-temporal pattern analysis of land cover changes in Tianjin during 2010–2020, the area of agricultural land is slowly decreasing, and construction land is increasing, and the increase results mainly from the conversion of agricultural land and grassland. The rapid population growth, economic development and policy guidance are the main driving factors.

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.

Comparison of ESA climate change initiative land cover to CORINE land cover over Eastern Europe and the Baltic States from a regional climate modeling perspective

High-quality land use and land cover (LULC) information is of crucial importance for the performance of regional climate models (RCMs), in particular at high spatial resolutions down to convection permitting scales below 4 km. Several satellite-based high-resolution products are currently available for implementation into RCMs. One of the most recent products is the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) dataset. While the ESA CCI LC has been assessed globally, an evaluation against regional, independent LULC datasets is necessary to identify LULC inaccuracies in the respective region of interest and to give regional climate modelers estimates for the uncertainty in the land use forcing. In the present work the ESA CCI LC dataset is compared to the COoRdination and INformation on the Environment (CORINE) Land Cover (CLC). Agreement between the datasets is assessed by proportional area comparison (PAC). The resulting agreement measures are compared to the results of a majority approach (MA) to explore possible differences between the methods. Three timesteps of ESA CCI LC matching the timesteps of CLC are assessed to take a change in agreement over time into account. In addition to the quantification of agreement, spatial patterns of possible issues with ESA CCI LC are identified through utilization of geospatial information systems (GIS). Using the PAC, the agreement of ESA CCI LC with CLC is found to be ∼76 % for the research area (RA). Although the agreement decreases slightly using the PAC, no substantial differences in agreement measures were found compared to the results of the MA. Dominant LULC categories agriculture and forest show an agreement of over 80 % with CLC. A few major issues were found for grassland, wetlands, settlements, and water bodies in the RA of which some might influence RCM performance if the dataset is implemented without adjustment. We highly recommend to apply the PAC to other regions in Europe and further globally to investigate if the found issues are also found elsewhere. The use of more independent regional and specified datasets for validation but also for possible improvement of the ESA CCI LC dataset is suggested.

Understanding Completeness and Diversity Patterns of OSM-Based Land-Use and Land-Cover Dataset in China

OpenStreetMap (OSM) data are considered essential for land-use and land-cover (LULC) mapping despite their lack of quality. Most relevant studies have employed an LULC reference dataset for quality assessment, but such a reference dataset is not freely available for most countries and regions. Thus, this study conducts an intrinsic quality assessment of the OSM-based LULC dataset (i.e., without using a reference LULC dataset) by examining the patterns of both its completeness and diversity. With China chosen as the study area, an OSM-based LULC dataset of the country was first generated and validated by using various accuracy measures. Both its completeness and diversity patterns were then mapped and analyzed in terms of each prefecture-level division of the country. The results showed the following: (1) While the overall accuracy was as high as 82.2%, most complete regions of China were not mapped well owing to a lack of diverse LULC classes. (2) In terms of socioeconomic factors and the number of contributors, higher correlations were noted for diversity patterns than completeness patterns; thus, the diversity pattern is a better reflection of socioeconomic factors and the spatial patterns of contributors. (3) Both the completeness and the diversity patterns can be combined to better understand an OSM-based LULC dataset. These results indicate that it is useful to consider diversity as a supplement for intrinsically assessing the quality of an OSM-based LULC dataset. This analytical method can also be applied to other countries and regions.

Stratified even sampling method for accuracy assessment of land use/land cover classification: a case study of Beijing, China

ABSTRACT Selecting samples with high representativeness is very important in the accuracy assessment of land use and land cover (LULC) classification. By optimizing sampling sites in both feature space and geographical space, this paper presented a stratified even sampling method for accuracy assessment of the GlobeLand30 dataset for Beijing, China. In this method, spatial stratification and area-weighted proportion sampling assignment method were adopted to achieve optimal coverage in feature space; spatial-simulated annealing (SSA) and the minimization of the mean of the shortest distances (MMSD) criterion were used to optimize sampling sites in geographical space. Six sample sets with sizes of 150, 300, 450, 600, 750 and 900 were drawn using the proposed method, spatial even sampling method, stratified random sampling method and simple random sampling method, and their overall accuracy (OA), root-mean-square error (RMSE) and standard deviation (STDEV) values were evaluated. The results suggested that the OA, RMSE and STDEV results of the proposed method were 71.36%-73.91% (Mean, 72.26%), 0.90% and 0.96%, respectively. Compared with the other sampling methods, the average OA of the proposed method was much closer to the true OA and corresponding RMSE and STDEV were much lower than the other three sampling methods, respectively. It can improve the representativeness of both feature and geographical space, and provide a robust operational tool for the validation of LULC datasets.

Assessing Changes in Ecosystem Service Values over 1985–2050 in Response to Land Use and Land Cover Dynamics in Abaya-Chamo Basin, Southern Ethiopia

This study evaluated the effect of Land Use and Land Cover (LULC) dynamics on the value of ecosystem services in Abaya-Chamo basin over 1985–2050. The main objectives of the study were to estimate the value of ecosystem services of Abaya-Chamo basin using local and global ecosystem service value coefficients, assess how it changes over time, and develop tools to inform policy and public decision-making to protect lands and waters in the region. The study utilized observed (1985 and 2010) and predicted (2030 and 2050) LULC datasets and ecosystem service value coefficients obtained from publications in peer-reviewed scientific journals. The results indicated that the total ecosystem service value of Abaya-Chamo basin was 12.13 billion USD in 1985 and 12.45 billion USD in 2010. The value is predicted to increase to 12.47 billion USD by the year 2050, which is 2.84% (344.5 million USD) higher than the total value of ecosystem services of the basin in 1985. Although the total ecosystem service value of the basin showed a slight increase over the study period, it was observed that the total value of services obtained from natural ecosystems is expected to decline by 36.24% between 1985 and 2050. The losses of services obtained from natural ecosystems, such as water regulation and erosion control, are major concern as the consequence has already been reported in the basin in the form of reduced water quality and productivity of the lakes due to an increased soil erosion and sediment transport in the basin. Therefore, special attention should be given to the rehabilitation of degraded ecosystems and the protection of remaining natural vegetation and water bodies to enhance natural capital and ecosystem services in the basin. A large-scale dissemination of eco-agricultural land use practices, which provide multiple ecosystem services (such as agroforestry and heterogeneous agricultural areas) in the basin, needs to be considered in the future.

Pathways of demographic and urban development and their effects on land take and ecosystem services: The case of Lisbon Metropolitan Area, Portugal

Land use and land cover (LULC) changes, particularly land take by urbanization, can jeopardize ecosystems and their capacity to provide humans with numerous benefits, known as ecosystem services. A better understanding of the connections between land take, changes in complex LULC patterns and ecosystem services is still needed. Especially needed are forward-looking analyses that can support spatial planning in the face of targets like “no net land take,” as set in Europe. The aim of this research is to gain insight into the possible consequences of alternative pathways of demographic and urban development, in terms of land take and ecosystem services supply. Using the Lisbon Metropolitan Area in Portugal as case study, four contrasting scenarios for 2030 were developed that cover major determinants of land take (with a focus on residential development) and priority ecosystem services for the region, as well as deal with climate regulation, recreation and food production. Our findings suggest that the effects of urban development on land take are positive for a “compact city” and negative for an “urban sprawl” pattern, even for opposite demographic developments (growth versus decline). However, each pattern can have both positive and negative effects on the supply of ecosystem services. Hence, tensions can exist between the “compact city” model of urban development and the provision of ecosystem services to urban dwellers, especially across planning levels. We argue that the way land take is defined can influence the outcome of this kind of assessment, depending on which LULC dataset is used to support it. Exploring the approach developed in this research with different stakeholders while adopting more extreme scenario assumptions can provide additional insights on the topic.

The Effects of Land Use and Land Cover Geoinformation Raster Generalization in the Analysis of LUCC in Portugal

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.

Development of land-use scenarios using vegetation inventories in Japan

Changes in land use and land cover (LULC) have major effects on biodiversity and ecosystem services. Land change models can simulate future trends of ecosystem services under different scenarios to inform the actions of decision makers towards building a more sustainable society. LULC data are essential inputs for predicting future land changes. It is now possible to derive high-resolution LULC maps from satellite data using remote sensing techniques. However, the classification of land categories in these maps is too limited to sufficiently assess biodiversity and ecosystem services. This study aims to develop land-use scenarios, using an appropriate LULC map, to enable assessment of biodiversity and ecosystem services at the national scale. First, we developed an LULC dataset using vegetation inventories based on field records of vegetation collected throughout the country in the periods 1978–1987, 1988–1998 and 1999–2014. The vegetation maps consist of over 905 vegetation categories, from which we aggregated the most prevalent categories into 9 LULC categories. Second, we created a business-as-usual scenario and plausible future scenarios on the land use change maps using the Land Change Model tool. In the process of developing the model, we considered key drivers including biophysical and socio-economic factors. The results showed some key land changes as consequences of intensive/extensive land-use interventions. These derived scenario maps can be used to assess the impacts of future land change on biodiversity and ecosystem services.

MODELLING THE LAND USE AND LAND COVER CHANGES IN PORTUGAL: A MULTI-SCALE AND MULTI-TEMPORAL APPROACH

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.

Reducing Uncertainties in Applying Remotely Sensed Land Use and Land Cover Maps in Land-Atmosphere Interaction: Identifying Change in Space and Time

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.

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

ABSTRACTAn accurate information on the amount and location of Land use and land cover (LULC) changes is necessary to develop and implement a sustainable-urban planning.This research investigates the potential of an integrated Multi-Layer Perceptron and Markov Chain Analysis (MLP-MCA) to map and accurately predict the future LULC change scenarios in Lagos Metropolitan Region of Nigeria. Multi-temporal LULC datasets derived from remotely sensed Landsat images from 1984, 2000 and 2015 were used for modeling, validation and prediction. Predicted LULC changes for 2030 and 2050 were performed based on the LULC map of 2015 using MLP-MCA method. The result reveals a significant expansion of built-up areas during the whole study period. Analysis of LULC distribution in Lagos metropolitan region shows that about 50% of urban land expansion happened beyond the administrative boundary of Lagos State during the period of 2000–2015. It is predicted that more than 75% of future urban growth will occur across the border of Lagos State, in the neighbouring Ogun State by 2050. These results imply that a strong and consistent collaboration between different states is crucial to establish an effective regional planning framework and ensure a proper planned growth of the metropolitan region.