It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and ...human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classification systems.
It is increasingly being recognized that land use and land cover changes driven by anthropogenic pressures are impacting terrestrial and aquatic ecosystems and their services, human society, and ...human livelihoods and well-being. This Special Issue contains 12 original papers covering various issues related to land use and land use changes in various parts of the world (see references), with the purpose of providing a forum to exchange ideas and progress in related areas. Research topics include land use targets, dynamic modelling and mapping using satellite images, pressures from energy production, deforestation, impacts on ecosystem services, aboveground biomass evaluation, and investigations on libraries of legends and classification systems.
AIM: For many applications in biodiversity and ecology, existing remote sensing‐derived land‐cover products have limitations due to among‐product inconsistency and their typically non‐continuous ...nature. Here we aim to help address these shortcomings by generating a 1‐km resolution global product that provides scale‐integrated and accuracy‐weighted consensus land‐cover information on an approximately continuous scale. LOCATION: Global. METHODS: Using a generalized classification scheme and an accuracy‐based integration approach, we integrated four global land‐cover products. We evaluated the performance of this product compared with inputs for estimating subpixel 30‐m resolution land cover. We also compared the accuracy of deductive and inductive species distribution models built with the different products for modelling the continental distributions of six avian habitat specialists. RESULTS: Our product offers accuracy‐weighted consensus information on the prevalence of 12 land‐cover classes within every nominal 1‐km pixel across the globe (except for Antarctica). Compared with the four base products, it better captures the land‐cover information contained in the fine‐grain validation data for all classes combined and for most individual classes. It also has the highest sensitivity and overall accuracy for detecting the presence of every fine‐grain land‐cover class. Both deductive and inductive models built with the consensus dataset have the highest or second highest accuracy for modelling bird species distributions. MAIN CONCLUSIONS: Our consensus product integrates the four base products and successfully maximizes accuracy and reduces errors of omission. Specifically, the consensus product reduces limitations caused by misclassifications, false absence rates and the categorical format of existing land‐cover products. Consequently, it surpasses single base products in the ability to capture subpixel land‐cover information and the utility for modelling species distributions. Both the presented methodology and the consensus product have multiple applications in biodiversity research and for understanding and modelling of global terrestrial ecosystems.
Distribution of land cover has a profound impact on the climate and environment; mapping the land cover patterns from global, regional to local scales are important for scientists and authorities to ...yield better monitoring of the changing world. Satellite remote sensing has been demonstrated as an efficient tool to monitor the land cover patterns for a large spatial extent. Nevertheless, the demand on land cover maps at a finer scale (especially in urban areas) has been raised with evidence by numerous biophysical and socio-economic studies. This paper reviews the small-footprint LiDAR sensor — one of the latest high resolution airborne remote sensing technologies, and its application on urban land cover classification. While most of the early researches focus on the analysis of geometric components of 3D LiDAR data point clouds, there has been an increasing interest in investigating the use of intensity data, waveform data and multi-sensor data to facilitate land cover classification and object recognition in urban environment. In this paper, the advancement of airborne LiDAR technology, including data configuration, feature spaces, classification techniques, and radiometric calibration/correction is reviewed and discussed. The review mainly focuses on the LiDAR studies conducted during the last decade with an emphasis on identification of the approach, analysis of pros and cons, investigating the overall accuracy of the technology, and how the classification results can serve as an input for different urban environmental analyses. Finally, several promising directions for future LiDAR research are highlighted, in hope that it will pave the way for the applications of urban environmental modeling and assessment at a finer scale and a greater extent.
•We review the airborne LiDAR technology to aid in urban land cover classification.•The use of LiDAR height, intensity, waveform, and multi-sensor data is summarized.•The LiDAR data classification and radiometric calibration techniques are identified.•Five other relevant urban environmental applications are presented.•We finally point out five forthcoming research areas that require further efforts.
Land‐cover change and ecosystem degradation may lead to biotic homogenization, yet our understanding of this phenomenon over large spatial scales and different biotic groups remains weak. We used a ...multi‐taxa dataset from 335 sites and 36 heterogeneous landscapes in the Brazilian Amazon to examine the potential for landscape‐scale processes to modulate the cumulative effects of local disturbances. Biotic homogenization was high in production areas but much less in disturbed and regenerating forests, where high levels of among‐site and among‐landscape β‐diversity appeared to attenuate species loss at larger scales. We found consistently high levels of β‐diversity among landscapes for all land cover classes, providing support for landscape‐scale divergence in species composition. Our findings support concerns that β‐diversity has been underestimated as a driver of biodiversity change and underscore the importance of maintaining a distributed network of reserves, including remaining areas of undisturbed primary forest, but also disturbed and regenerating forests, to conserve regional biota.
The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year ...intervals. The development of this product is part of an effort to meet the growing demand for longer temporal duration and more frequent, accurate, and consistent land cover and change information. To accomplish this, we designed a new land cover strategy and developed comprehensive methods, models, and procedures for NLCD 2016 implementation. Major steps in the new procedures consist of data preparation, land cover change detection and classification, theme-based postprocessing, and final integration. Data preparation includes Landsat imagery selection, cloud detection, and cloud filling, as well as compilation and creation of more than 30 national-scale ancillary datasets. Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. The land cover classification includes seven-date training data creation and 14-run classifications. Pools of training data for change and no-change areas were created before classification based on integrated information from ancillary data, change-detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis. In postprocessing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure the spatial and temporal coherence of land cover and land cover changes over 15 years. An initial accuracy assessment on four selected Landsat path/rows classified with this method indicates an overall accuracy of 82.0% at an Anderson Level II classification and 86.6% at the Anderson Level I classification after combining the primary and alternate reference labels. This methodology was used for the operational production of NLCD 2016 for the Conterminous United States, with final produced products available for free download.
High-density human activities have rapidly changed the pattern of urban land use/land cover (LULC), affecting the supply of urban ecosystem services (ESs). This poses a challenge to the balance of ...urban development and ecological protection, which may be more serious in ecologically fragile regions. This study identified key LULC change (LULCC) impacts on ESs in Ordos, an ecologically fragile region in Northwest China. The urbanization process of Ordos will continue in 2030 under the business-as-usual scenario due to its strategic positioning in the National Energy Base of China. The future LULC simulation results showed that developed land in eastern Ordos will continue to expand in 2030, and more forest and grassland will appear in western Ordos as a result of ecological restoration. The results of the InVEST model in calculating four important ESs showed that the ES supply in the densely populated areas (eastern Ordos) has declined, and more attention must be given to natural vegetation protection in the urbanization process. Although carbon storage in the western region has increased due to afforestation, this also reduces the water yield supply, which may exacerbate the water shortage in Ordos. Supported by this framework, more sustainable urban land use management can be undertaken to balance the conflict between ecological protection and urbanization. This will contribute to regional ecological health and sustainable urban development.
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•Ecosystem services (ESs) need specific attention in ecologically fragile regions.•Urbanization will continue in 2030 under the business-as-usual scenario in Ordos.•Beneficial ESs declined due to transfer from natural vegetation to developed land.•Ecological protection is particularly important in densely populated eastern counties.•Afforestation may reduce water yield which is challenging for water-scarce cities.
In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial ...resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-cover dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high confidence are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. To obtain a pixel-wise land-cover classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, Sentinel-2A, and Google Earth platform data, show encouraging results and demonstrate the applicability of the proposed scheme to land-cover classification with multi-source HRRS images.
•A method to learn transferable deep model for 5-class land-cover (LC) classification.•A labeled dataset consisting of 150 Gaofen-2 images for LC classification.•It improves LC classification performance about 20% using multi-source RS images.•The method shows good transferability on different sensors and geolocations.
•The socio-political transformation in Hungary and Poland after 1989 have contributed significantly to the land use change.•Surface areas of individual land use types in Hungary and Poland show ...similar trends but the degree of change is different.•A reduction of agricultural land and increased forest and uncultivated land coverage was observed in Poland and Hungary.•The most intensive anthropogenic changes were identified near the cities (Budapest and Kraków).•The decrease in agricultural land noted in the land cover of the study area coincided with European and global trends.
This study has analysed changes in land use and land cover (LUCC) in two post-socialist countries – Hungary and Poland, based upon cadastral data, statistical data, and CORINE Land Cover data. This is a new approach aimed to perform a synergy analysis carried out in accordance with the ‘top-down’ principle, i.e. from the level of countries (in the case of land use) to the level of provinces/counties (land cover). This approach facilitates more reliable results. The authors analysed LUCC in northern Hungary (the Pest County) and southern Poland (the Małopolska Province) using GIS and statistical methods. The main aim of the research was to identify and assess the ongoing LUCC changes and compare them in the assumed field of research. The results indicate the existence of a trend in each time series. A downward trend was revealed for agricultural land in Hungary and Poland as well as for uncultivated land in Hungary. An upward trend was found in other variables (uncultivated land in Poland and forest land in both countries). Changes in land cover in the Pest County and the Małopolska Province (in 2000 and 2012) show a decrease in agricultural areas and an increase in artificial surfaces, forest and semi-natural areas with the change in Poland being more intensive than that in Hungary.
Earth observation (EO) sensors deliver data at daily or weekly intervals. Most land use and land cover classification (LULC) approaches, however, are designed for cloud-free and mono-temporal ...observations. The increasing temporal capabilities of today’s sensors enable the use of temporal, along with spectral and spatial features.Domains such as speech recognition or neural machine translation, work with inherently temporal data and, today, achieve impressive results by using sequential encoder-decoder structures. Inspired by these sequence-to-sequence models, we adapt an encoder structure with convolutional recurrent layers in order to approximate a phenological model for vegetation classes based on a temporal sequence of Sentinel 2 (S2) images. In our experiments, we visualize internal activations over a sequence of cloudy and non-cloudy images and find several recurrent cells that reduce the input activity for cloudy observations. Hence, we assume that our network has learned cloud-filtering schemes solely from input data, which could alleviate the need for tedious cloud-filtering as a preprocessing step for many EO approaches. Moreover, using unfiltered temporal series of top-of-atmosphere (TOA) reflectance data, our experiments achieved state-of-the-art classification accuracies on a large number of crop classes with minimal preprocessing, compared to other classification approaches.
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