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Cellular automata (CA)-based models have been extensively used in urban expansion modeling because of their simplicity, flexibility and intuitiveness. Previous studies on CA-based urban growth mode...

Taking the “One Million-Mu (666 km2)” Plain Afforestation (Phase I) Project (Phase I afforestation) in Beijing city as an example, we monitored the growth status of planted forest using long-term remote sensing images, and evaluated the impacts of afforestation on land use change and vegetation growth. We found there is a large space for improvement regarding the ecological benefits of the project. Moreover, we found forest patches with decreasing greenness after the afforestation were mainly converted from farmland with high greenness and low heterogeneity in terms of the normalized difference vegetation index (NDVI). This implies that those farmland patches are inappropriate for afforestation. According to the results from Phase I afforestation and the impact of urbanization on green space, we constructed a series of spatial variables and generated a suitability map for the next “New Round of One Million-Mu (666 km2) Afforestation project” (Phase II afforestation). We then modeled the spatial distribution of Phase II afforestation based on the derived suitability map. This study is crucial for the scientific evaluation of afforestation projects for space planning (e.g., urban green space planning). The evaluation and modeling framework built in this study can be used to support the decision making and policy implementation of afforestation projects in China.

Urban land use mapping is critical to understanding human activities in space. The first national mapping result of essential urban land use categories of China (EULUC-China) was released in 2019. However, the overall accuracies in some of the plain cities such as Beijing, Chengdu, and Zhengzhou were lower than 50% because many parcel-based mapping units are large with mixed land uses. To address this shortcoming, we proposed an area of interest (AOI)-based mapping approach, choosing Beijing as our study area. The mapping process includes two major steps. First, grids with different sizes (i.e., 300 m, 200 m, and 100 m) were derived from original land parcels to obtain classification units with a suitable size. Then, features within these grids were extracted from Sentinel-2 spectral data, point of interest (POI), and Tencent Easygo crowdedness data. These features were classified using a random forest (RF) classifier with AOI data, resulting in a 10-category map of EULUC. Second, we superimposed the AOIs layer on classified units to do some rectification and offer more details at the building scale. The overall accuracy of the AOI layer reached 98%, and the overall accuracy of the mapping results reached 77%. This study provides a fast method for accurate geographic sample collection, which substantially reduces the amount of fieldwork for sample collection and improves the classification accuracy compared to previous EULUC mapping. The detailed urban land use map could offer more support for urban planning and environmental policymaking.

Urban cellular automata (CA) models propagate and accumulate errors during the modeling process due to the model structure or stochastic processes involved. It is feasible to assimilate real-time observations into an urban CA model to reduce model uncertainties. However, the assimilation performance is sensitive to the spatio-temporal units in the assimilation algorithm, that is, spatial block size and window length (temporal interval). In this study, we coupled an assimilation model, an ensemble Kalman filter (EnKF) and a Logistic-CA model to simulate the urban dynamic in Beijing over a period of two decades. Our results indicate that the coupled EnKF-CA model outperforms the CA-alone counterpart by about 10% in terms of the figure of merit, which reflects the agreement of modeled pixels. We also find that the assimilation performance using a finer block (1 km) is better than that using a coarser block (5 km and 10 km) because of the better depiction of spatial heterogeneity using a finer block. ...

We report the world’s first all-season training and validation sample sets for global land cover classification with Landsat-8 data. Prior to this, such samples were only available at a single date primarily from the growing season. It is unknown how much limitation such a single-date sample has to mapping global land cover in other seasons of the year. To answer this question, we selected available Landsat-8 images from four seasons and collected training and validation samples from them. We compared the performances of training samples in different seasons using Random Forest algorithm. We found that the use of training samples from any individual season would result in the best overall classification accuracy when validated by samples in the same season. The global overall accuracy from combined best seasonal results was 67.2% when classifying the 11 Level-1 classes in the Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) classification system. The use of training samples from all seasons (named all-season training sample set hereafter) produced an overall accuracy of 67.0%. We also tested classification within 10° latitude 60° longitude zones using all-season training subsample within each zone and obtained an overall accuracy of 70.2%. This indicates that properly grouped subsamples in space can help improve classification accuracies. All the results in this study seem to suggest that it is possible to use an all-season training sample set to reach global optimality with universal applicability in classifying images acquired at any time of a year for global land cover mapping.

Along with the transformation of urban development and planning in China, the traditional macro-scale urban planning which takes function delimitation as the main working mode and land exploitation as the guiding principle has begun to stretch towards the micro-scale which focuses on the human. This new research tendency requires a new planning support system. Since there are existing decision support systems for both meso and macro scale urban planning, this research puts forward that a micro-scale support system for planning should be constructed. This system should considerate human’s subjective feelings and needs as the core, and should be organized in three categories: ecological elements in micro living environment, urban design elements in local space, and individual behavior elements. Based on this, three sub-systems are proposed. For the first, the natural environment and ecological factors involved in micro-scale urban planning are the research objects, and this sub-system is established according to the logic of “cause-effect-response” model. Secondly, by integrating quantitative research and evaluation methods for urban design, the sub-system of urban design and spatial layout provides support for micro urban design simulation and local area design optimization, from the dimensions of urban perception, spatial morphology and function optimization. Lastly, the sub-system of human behavior and community life is based on the urban space-time behavioral theory and is applied to urban space-time analysis residential area planning, functional area planning, traffic planning, etc. These sub-systems will play an essential role in the future vital work such as central-city-planning, urban-physical-examination and livable-city-construction.

Although many studies have been made on urban land use change, most of them focus on a specific aspect (e.g., landscape or policy) with fewer considerations of spatiotemporal dynamics of urban sprawl. In this study, we explored the urban sprawl process in Beijing over the past three decades (1984–2013) on an annual basis by linking spatial metrics and landscape networks to trace the dynamics of urban patches. First, we identified six main growth periods of urban expansion with different patterns, through a relatively comprehensive change analysis of different landscape matrix. Second, we used the urban landscape network to explain the spatiotemporal dynamics of urban patches, with a linkage to policies (or planning) behind each hotspot of urban expansion. We found the major trajectory of urban growth in Beijing started from the northern and southern parts of the central built-up region to its southeast side, which is different from the planned two axes (i.e. horizontal and vertical) along the core area of the city. Our results indicated that urban expansion is highly uneven both in time and space, which relies on more satellite observations for a better understanding of this process. Also, the identified urban growth periods and patterns are valuable to the development of urban sprawl model.

Light pollution, a phenomenon in which artificial nighttime light (NTL) changes the form of brightness and darkness in natural areas such as protected areas (PAs), has become a global concern due to its threat to global biodiversity. With ongoing global urbanization and climate change, the light pollution status in global PAs deserves attention for mitigation and adaptation. In this study, we developed a framework to evaluate the light pollution status in global PAs, using the global NTL time series data. First, we classified global PAs (30,624) into three pollution categories: non-polluted (5974), continuously polluted (8141), and discontinuously polluted (16,509), according to the time of occurrence of lit pixels in/around PAs from 1992 to 2018. Then, we explored the NTL intensity (e.g., digital numbers) and its trend in those polluted PAs and identified those hotspots of PAs at the global scale with consideration of global urbanization. Our study shows that global light pollution is mainly distributed within the range of 30°N and 60°N, including Europe, north America, and East Asia. Although the temporal trend of NTL intensity in global PAs is increasing, Japan and the United States of America (USA) have opposite trends due to the implementation of well-planned ecological conservation policies and declining population growth. For most polluted PAs, the lit pixels are close to their boundaries (i.e., less than 10 km), and the NTL in/around these lit areas has become stronger over the past decades. The identified hotspots of PAs (e.g., Europe, the USA, and East Asia) help support decisions on global biodiversity conservation, particularly with global urbanization and climate change.

High-quality training and validation samples are critical components of land-cover and land-use mapping tasks in remote sensing. For large area mapping it is much more difficult to build such sample sets due to the huge amount of work involved in sample collection and image processing. As more and more satellite data become available, a new trend emerges in land-cover mapping that takes advantage of images acquired beyond the greenest season. This has created the need for constructing sample sets that can be used in classifying images of multiple seasons. On the other hand, seasonal land-cover information is also becoming a new demand in land and climate change studies. Here we produce the first training and validation data sets with seasonal labels in order to support the production of seasonal land-cover data for entire Africa. Nonetheless, for the first time, two classification systems were created for the same set of samples. We adapted the finer resolution observation and monitoring of global land cover FROM-GLC and the Food and Agriculture Organization FAO Land Cover Classification System legends. Locations of training-sample units of FROM-GLC were repurposed here. Then we designed a process to enlarge the training-sample units to increase the density of samples in the feature space of spectral characteristics of Moderate Resolution Imaging Spectroradiometer MODIS time-series and Landsat imagery. Finally, we obtained 15,799 training-sample units and 7430 validation-sample units. The land-cover type at each point was recorded at the time of maximum greenness in addition to four seasons in a year. Nearly half of the sample units were also suitable for 500 m resolution MODIS data. We analysed the representativeness of the training and validation sets and then provided some suggestions about their use in improving classification accuracies of Africa.

Urban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting urban boundaries from satellite images is still a great challenge, especially at a global scale and a fine resolution. In this study, we developed an automatic delineation framework to generate a multi-temporal dataset of global urban boundaries (GUB) using 30 m global artificial impervious area (GAIA) data. First, we delineated an initial urban boundary by filling inner non-urban areas of each city. A kernel density estimation approach and cellular-automata based urban growth modeling were jointly used in this step. Second, we improved the initial urban boundaries around urban fringe areas, using a morphological approach by dilating and eroding the derived urban extent. We implemented this delineation on the Google Earth Engine platform and generated a 30 m resolution global urban boundary dataset in seven representative years (i.e. 1990, 1995, 2000, 2005, 2010, 2015, and 2018). Our extracted urban boundaries show a good agreement with results derived from nighttime light data and human interpretation, and they can well delineate the urban extent of cities when compared with high-resolution Google Earth images. The total area of 65 582 GUBs, each of which exceeds 1 km2, is 809 664 km2 in 2018. The impervious surface areas account for approximately 60% of the total. From 1990 to 2018, the proportion of impervious areas in delineated boundaries increased from 53% to 60%, suggesting a compact urban growth over the past decades. We found that the United States has the highest per capita urban area (i.e. more than 900 m2) among the top 10 most urbanized nations in 2018. This dataset provides a physical boundary of urban areas that can be used to study the impact of urbanization on food security, biodiversity, climate change, and urban health. The GUB dataset can be accessed from http://data.ess.tsinghua.edu.cn.

Artificial impervious areas are predominant indicators of human settlements. Timely, accurate, and frequent information on artificial impervious areas is critical to understanding the process of urbanization and land use/cover change, as well as of their impacts on the environment and biodiversity. Despite their importance, there still lack annual maps of high-resolution Global Artificial Impervious Areas (GAIA) with longer than 30-year records, due to the high demand of high performance computation and the lack of effective mapping algorithms. In this paper, we mapped annual GAIA from 1985 to 2018 using the full archive of 30-m resolution Landsat images on the Google Earth Engine platform. With ancillary datasets, including the nighttime light data and the Sentinel-1 Synthetic Aperture Radar data, we improved the performance of our previously developed algorithm in arid areas. We evaluated the GAIA data for 1985, 1990, 1995, 2000, 2005, 2010, and 2015, and the mean overall accuracy is higher than 90%. A cross-product comparison indicates the GAIA data are the only dataset spanning over 30 years. The temporal trend in GAIA agrees well with other datasets at the local, regional, and global scales. Our results indicate that the GAIA reached 797,076 km2 in 2018, which is 1.5 times more than that in 1990. China and the United States (US) rank among the top two in artificial impervious area, accounting for approximately 50% of the world's total in 2018. The artificial impervious area of China surpassed that of the US in 2015. By 2018, the remaining eight among the top ten countries are India, Russia, Brazil, France, Italy, Germany, Japan, and Canada. The GAIA dataset can be freely downloaded from http://data.ess.tsinghua.edu.cn .

High-resolution urban land use maps have important applications in urban planning and management, but the availability of these maps is low in countries such as China. To address this issue, we have developed a protocol to identify urban land use functions over large areas using satellite images and open social data. We first derived parcels from road networks contained in Open Street Map (OSM) and used the parcels as the basic mapping unit. We then used 10 features derived from Points of Interest (POI) data and two indices obtained from Landsat 8 Operational Land Imager (OLI) images to classify parcels into eight Level I classes and sixteen Level II classes of land use. Similarity measures and threshold methods were used to identify land use types in the classification process. This protocol was tested in Beijing, China. The results showed that the generated land use map had an overall accuracy of 81.04% and 69.89% for Level I and Level II classes, respectively. The map revealed significantly more details of the spatial pattern of land uses in Beijing than the land use map released by the government.

An adjacent vertex distinguishing proper edge coloring of a graph [Formula: see text] is a proper edge coloring of [Formula: see text] such that no pair of adjacent vertices meet the same set of colors. The minimum number of colors is called adjacent vertex distinguishing proper edge chromatic number of [Formula: see text]. In this paper, we present a new heuristic intelligent algorithm to calculate the adjacent vertex distinguishing proper edge chromatic number of graphs. To be exact, the algorithm establishes two objective subfunctions and a main objective function to find its optimal solutions by the conditions of adjacent vertex distinguishing proper edge coloring. Moreover, we test and analyze its feasibility, and the test results show that this algorithm can rapidly and efficiently calculate the adjacent vertex distinguishing proper edge chromatic number of graphs with fixed order, and its time complexity is less than [Formula: see text].