Xuecao Li

A multi-resolution global land cover dataset through multisource data aggregation

Recent developments of 30 m global land characterization datasets (e.g., land cover, vegetation continues field) represent the finest spatial resolution inputs for global scale studies. Here, we present results from further improvement to land cover mapping and impact analysis of spatial resolution on area estimation for different land cover types. We proposed a set of methods to aggregate two existing 30 m resolution circa 2010 global land cover maps, namely FROM-GLC (Finer Resolution Observation and Monitoring-Global Land Cover) and FROM-GLC-seg (Segmentation), with two coarser resolution global maps on development, i.e., Nighttime Light Impervious Surface Area (NL-ISA) and MODIS urban extent (MODIS-urban), to produce an improved 30 m global land cover map—FROM-GLC-agg (Aggregation). It was post-processed using additional coarse resolution datasets (i.e., MCD12Q1, GlobCover2009, MOD44W etc.) to reduce land cover type confusion. Around 98.9% pixels remain 30 m resolution after some post-processing to this dataset. Based on this map, majority aggregation and proportion aggregation approaches were employed to create a multi-resolution hierarchy (i.e., 250 m, 500 m, 1 km, 5 km, 10 km, 25 km, 50 km, 100 km) of land cover maps to meet requirements for different resolutions from different applications. Through accuracy assessment, we found that the best overall accuracies for the post-processed base map (at 30 m) and the three maps subsequently aggregated at 250 m, 500 m, 1 km resolutions are 69.50%, 76.65%, 74.65%, and 73.47%, respectively. Our analysis of area-estimation biases for different land cover types at different resolutions suggests that maps at coarser than 5 km resolution contain at least 5% area estimation error for most land cover types. Proportion layers, which contain precise information on land cover percentage, are suggested for use when coarser resolution land cover data are required.

Mapping Urban Land Use by Using Landsat Images and Open Social Data

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.

Meta-discoveries from a synthesis of satellite-based land-cover mapping research

Since the launch of the first land-observation satellite (Landsat-1) in 1972, land-cover mapping has accumulated a wide range of knowledge in the peer-reviewed literature. However, this knowledge has never been comprehensively analysed for new discoveries. Here, we developed the first spatialized database of scientific literature in English about land-cover mapping. Using this database, we tried to identify the spatial temporal patterns and spatial hotspots of land-cover mapping research around the world. Among other findings, we observed (1) a significant mismatch between hotspot areas of land-cover mapping and areas that are either hard to map or rich in biodiversity; (2) mapping frequency is positively related to economic conditions; (3) there is no obvious temporal trend showing improvement in mapping accuracy; (4) images with more spectral bands or a combination of data types resulted in increased mapping accuracies; (5) accuracy differences due to algorithm differences are not as large as those due to various types of data used; and (6) the complexity of a classification system decreases its mapping accuracy. We recommend that one way to improve our understanding of the challenges, advances, and applications of previous land-cover mapping is for journals to require area-based information at the time of manuscript submission. In addition, building a standard protocol for systematic assessment of land-cover mapping efforts at the global scale through international collaboration is badly needed.

A new research paradigm for global land cover mapping

ABSTRACT In this paper, we introduced major challenges in mapping croplands, settlements, water and wetlands, and discussed challenges in the use of multi-temporal and multi-sensor data. We then summarized some of the on-going efforts in improving qualities of global land cover maps. Existing technologies provide sufficient data for better map making if extra efforts can be made instead of harmonizing and integrating various global land cover products. Developing and selecting better algorithms, including more input variables (new types of data or features) for classification, having representative training samples are among conventional measures generally believed effective in improving mapping accuracies at local scales. We pointed out that data were more important in improving mapping accuracies than algorithms. Finally, we proposed a new paradigm for global land cover mapping, which included a view of vegetation classes based on their types and form, canopy cover and height. The new paradigm suggests that a universally applicable training sample set is not only possible but also effective in improving land cover classification at the continental and global scales. To ensure an easy transition from traditional land cover mapping to the new paradigm, we recommended that an all-in-one data management and analysis system be constructed.

Urban mapping using DMSP/OLS stable night-time light: a review

ABSTRACT The Defense Meteorological Satellite Program/Operational Linescane System (DMSP/OLS) stable night-time light (NTL) data showed great potential in urban extent mapping across a variety of scales with historical records dating back to 1990s. In order to advance this data, a systematic methodology review on NTL-based urban extent mapping was carried out, with emphases on four aspects including the saturation of luminosity, the blooming effect, the intercalibration of time series, and their temporal pattern adjustment. We think ancillary features (e.g. land surface conditions and socioeconomic activities) can help reveal more spatial details in urban core regions with high digital number (DN) values. In addition, dynamic optimal thresholds are needed to address issues of different exaggeration of NTL data in the large scale urban mapping. Then, we reviewed three key aspects (reference region, reference satellite/year, and calibration model) in the current intercalibration framework of NTL time series, and summarized major reference regions in literature that were used for intercalibration, which is critical to achieve a globally consistent series of NTL DN values over years. Moreover, adjustment of temporal pattern on intercalibrated NTL series is needed to trace the urban sprawl process, particularly in rapidly developing regions. In addition, we analysed those applications for urban extent mapping based on the new generation NTL data of Visible/Infrared Imager/Radiometer Suite. Finally, we prospected the challenges and opportunities including the improvement of temporally inconsistent NTL series, mitigation of spatial heterogeneity of blooming effect in NTL, and synthesis of different NTL satellites, in global urban extent mapping.

Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions

Agricultural droughts can create serious threats to food security. Tools for dynamic prediction of drought impacts on yields over large geographical regions can provide valuable information for drought management. Based on the DeNitrification-DeComposition (DNDC) model, the current research proposes a Drought Risk Analysis System (DRAS) that allows for the scenario-based analysis of drought-induced yield losses. We assess impacts on corn yields using two case studies, the 2012 U.S.A. drought and the 2000 and 2009 droughts in Liaoning Province, China. The results show that the system is able to perform daily simulations of corn growth and to dynamically evaluate the large-scale grain production in both regions. It is also capable of mapping the up-to-date yield losses on a daily basis, the additional losses under different drought development scenarios, and the yield-based drought return periods at multiple scales of geographic regions. In addition, detailed information about the water-stress process, biomass development, and the uncertainty of drought impacts on crop growth at a specific site can be displayed in the system. Remote sensing data were used to map the areas of drought-affected crops for comparison with the modeling results. Beyond the conventional drought information from meteorological and hydrological data, this system can provide comprehensive and predictive yield information for various end-users, including farmers, decision makers, insurance agencies, and food consumers. A Drought Risk Analysis System for daily evaluating and predicting large-scale drought-induced yield losses during the 2012 U.S. drought.Detailed information available for the crop water-stresses processes, biomass development, and uncertainties of drought impacts.Dynamic and scale-dependent drought return periods according to yield losses demonstrated in the case study of Liaoning, China.Remotely sensed data provided essential information for mapping and verifying the drought-affected areas.

Integrating ensemble-urban cellular automata model with an uncertainty map to improve the performance of a single model

Transition rules are the core of urban cellular automata (CA) models. Although the logistic cellular automata (Logistic-CA) is commonly used for rules extraction, it cannot always achieve satisfactory performance because of the spatial heterogeneity and the inherent complexity of urban expansion. This article presents an ensemble-urban cellular automata (Ensemble-CA) model to achieve better transition rules. First, an uncertainty map that assesses the performance of transition rules spatially was achieved. Then, two auxiliary models (i.e. classification and regression tree, CART; and artificial neural network, ANN), both of which have been stabilized with a Bagging algorithm, were prepared for integration using a proposed self-adaptive -nearest neighbors (-NN) combination algorithm. Thereafter, those unconfident sites were replaced with the ensemble output. This model was applied to Guangzhou, China, for an urban growth simulation from 2003 to 2008. Static validation confirmed that this ensemble framework (i.e. without substitution of uncertain sites) can achieve better performance (0.87) in terms of receiver operating characteristic (ROC) statistics (area under the curve, AUC), and outperformed the best single model (ANN, 0.82) and other common strategies (e.g. weighted average, 0.83). After the substitution of unconfident sites, the AUC of Logistic-CA was elevated from 0.78 to 0.81. Subsequently, two urban growth mechanisms (i.e. pixel- and patch-based) were implemented separately based on the integrated transition rules. Experimental results revealed that the accuracy obtained from simulation of the Ensemble-CA increased considerably. The obtained kappa outperformed the single model, with improvements of 1.74% and 2.76% for pixel- and patch-based approaches, respectively. Correspondingly, landscape similarity index (LSI) improvements of these two mechanisms were 4.24% and 1.82%.

The surface urban heat island response to urban expansion: A panel analysis for the conterminous United States

Urban heat island (UHI), the phenomenon that urban areas experience higher temperatures compared to their surrounding rural areas, has significant socioeconomic and environmental impacts. With current and anticipated rapid urbanization, improved understanding of the response of UHI to urbanization is important for developing effective adaptation measures and mitigation strategies. Current studies mainly focus on a single or a few big cities and knowledge on the response of UHI to urbanization for large areas is limited. As a major indicator of urbanization, urban area size lends itself well for representation in prognostic models. However, we have little knowledge on how UHI responds to urban area size increase and its spatial and temporal variation over large areas. In this study, we investigated the relationship between surface UHI (SUHI) and urban area size in the climate and ecological context, and its spatial and temporal variations, based on a panel analysis of about 5000 urban areas of 10km2 or larger, in the conterminous U.S. We found statistically significant positive relationship between SUHI and urban area size, and doubling the urban area size led to a SUHI increase as high as 0.7°C. The response of SUHI to the increase of urban area size shows spatial and temporal variations, with stronger SUHI increase in Northern U.S., and during daytime and summer. Urban area size alone can explain as much as 87% of the variance of SUHI among cities studied, but with large spatial and temporal variations. Urban area size shows higher association with SUHI in regions where the thermal characteristics of land cover surrounding the urban area are more homogeneous, such as in Eastern U.S., and in the summer months. This study provides a practical approach for large-scale assessment and modeling of the impact of urbanization on SUHI, both spatially and temporally.

Aggregative model-based classifier ensemble for improving land-use/cover classification of Landsat TM Images

This article proposes a new approach to improve the classification performance of remotely sensed images with an aggregative model based on classifier ensemble (AMCE). AMCE is a multi-classifier system with two procedures, namely ensemble learning and predictions combination. Two ensemble algorithms (Bagging and AdaBoost.M1) were used in the ensemble learning process to stabilize and improve the performance of single classifiers (i.e. maximum likelihood classifier, minimum distance classifier, back propagation neural network, classification and regression tree, and support vector machine (SVM)). Prediction results from single classifiers were integrated according to a diversity measurement with an averaged double-fault indicator and different combination strategies (i.e. weighted vote, Bayesian product, logarithmic consensus, and behaviour knowledge space). The suitability of the AMCE model was examined using a Landsat Thematic Mapper (TM) image of Dongguan city (Guangdong, China), acquired on 2 January 2009. Experimental results show that the proposed model was significantly better than the most accurate single classification (i.e. SVM) in terms of classification accuracy (i.e. from 88.83% to 92.45%) and kappa coefficient (i.e. from 0.8624 to 0.9088). A stepwise comparison illustrates that both ensemble learning and predictions combination with the AMCE model improved classification.

Using a global reference sample set and a cropland map for area estimation in China

A technically transparent and freely available reference sample set for validation of global land cover mapping was recently established to assess the accuracies of land cover maps with multiple resolutions. This sample set can be used to estimate areas because of its equal-area hexagon-based sampling design. The capabilities of these sample set-based area estimates for cropland were investigated in this paper. A 30-m cropland map for China was consolidated using three thematic maps (cropland, forest and wetland maps) to reduce confusion between cropland and forest/wetland. We compared three area estimation methods using the sample set and the 30 m cropland map. The methods investigated were: (1) pixel counting from a complete coverage map, (2) direct estimation from reference samples, and (3) model-assisted estimation combining the map with samples. Our results indicated that all three methods produced generally consistent estimates which agreed with cropland area measured from an independent national land use dataset. Areas estimated from the reference sample set were less biased by comparing with a National Land Use Dataset of China (NLUD-C). This study indicates that the reference sample set can be used as an alternative source to estimate areas over large regions.