Yongxian Su

Study on the cooling effects of urban parks on surrounding environments using Landsat TM data: a case study in Guangzhou, southern China

The temperature cooling effects of ten urban parks on surrounding environments in Guangzhou, southern China, are analysed and quantified using Landsat Thematic Mapper data. The results show that there is a temperature rise (about 1.74°C) between green spaces of parks and bare-ground areas of the surroundings. For those parks whose green area percentage is more than 69% and length:width ratio is close to 1, the average temperature differences between boundaries and surrounding sites of parks have linear relationships with the green areas of parks (R 2 > 0.82). Moreover, the nonlinear relationship between the average cooling distance of parks and green areas can be simulated very well using a logarithmic curve (R 2 > 0.93). When the green areas of parks are smaller than 10 566 m2, parks will have no temperature cooling effects on their surrounding environments. When the green areas of parks reach 740 000 m2, the increase of temperature cooling distance is less than 1 m per 10 000 m2 increase of the green area. The most appropriate size of green areas of urban parks should fall between 10 566 and 740 000 m2. For those parks with water areas larger than 128 889 m2, the temperature cooling effects are usually more remarkable. When the length:width ratios of the green areas of urban parks are more than or equal to 2, their temperature cooling distances are always larger than those with length:width ratios equal to 1 given similar green area. Parks with larger green areas (37 163 m2) or larger water areas (>128 889 m2) will have more significant temperature cooling effects in June than in October.

A new method for extracting built-up urban areas using DMSP-OLS nighttime stable lights: a case study in the Pearl River Delta, southern China

The nighttime stable light (NSL) images on board the Operational Line-scan System (OLS) of the Defense Meteorological Satellite Program (DMSP) are useful for extracting large-scale built-up urban areas. However, most NSL-based studies are presently empirical threshold-based approaches. They always overestimate the areas of built-up land in urban regions because of the ‘blooming’ effect of NSL; and overlook small patches in developing towns where the NSL is much lower. In this study, a neighborhood statistics analysis (NSA) method is developed on the basis of the relative spatial variations between neighboring built-up and non-built-up pixels in DMSP-OLS images. It is applied to extract the built-up areas of eight cities in the Pearl River Delta in 1996, 2000, 2005, and 2009. The validations indicate that the total areas of the NSA-mapped results are highly correlated with those from Landsat TM/ETM+ data (R2 = 0.94; p < 0.001). The comparison results, which are evaluated by landscape indices (the landscape shape index (LSI), the contiguity index (CONTIG), and the perimeter area ratio (PARA)), also show good correlations (R2 > 0.46; p < 0.001). In addition, the total NSL of the built-up urban areas is significantly correlated with the statistical population data (R2 = 0.62; p < 0.001), which indirectly confirms the effectiveness of our proposed method.

Retrieving China’s surface soil moisture and land surface temperature using AMSR-E brightness temperatures

Based on the passive microwave radiative transfer equation, we developed a single-band model for retrieving surface soil moisture () and land surface temperature () using Aqua satellite advanced microwave scanning radiometer (AMSR-E) C-band brightness temperatures () without any ancillary data. National-scale and of China in 2009 were jointly mapped. The average and retrieval errors are 0.037 g cm−3 and 2.00K when validated by in situ measurements and approximately 0.025 g cm−3 and 2.34K when compared with AMSR-E L3 and moderate-resolution imaging spectroradiometer daily products. In particular, most and estimation errors are under 0.37 g cm−3 and 2.00K, respectively, for regions with the microwave polarization difference index (MPDI) between 0.01 and 0.05, but increased clearly larger for those with MPDI smaller than 0.01 or larger than 0.05, which are mostly dense-vegetated regions in Southwestern China or plateaus and dry deserts such as Inner Mongolia plateau, Qinghai-Tibet Plateau, Taklimakan Desert and Turpan basin. In summary, the proposed retrieval algorithm might provide a new way to monitor and conditions for penetrable vegetation canopy regions, but further investigations are still needed, especially for specific plateau, desert and frozen regions.

Construction of an ecological resistance surface model and its application in urban expansion simulations

Urban expansion models are useful tools to understand urbanization process and have been given much attention. However, urban expansion is a complicated socio-economic phenomenon that is affected by complex and volatile factors involving in great uncertainties. Therefore, the accurate simulation of the urban expansion process remains challenging. In this paper, we make an attempt to solve such uncertainty through a reversal process and view urban expansion as a process wherein the urban landscape overcomes resistance from other landscapes. We developed an innovative approach derived from the minimum cumulative resistance (MCR) model that involved the introduction of a relative resistance factor for different source levels and the consideration of rigid constraints on urban expansion caused by ecological barriers. Using this approach, the urban expansion ecological resistance (UEER) model was created to describe ecological resistance surfaces suitable for simulating urban expansion and used to simulate urban expansion in Guangzhou. The study results demonstrate that the ecological resistance surface generated by the UEER model comprehensively reflects ecological resistance to urban expansion and indicates the spatial trends in urban expansion. The simulation results from the UEER-based model were more realistic and more accurately reflected ecological protection requirements than the conventional MCR-based model. These findings can enhance urban expansion simulation methods.

Monitoring litchi canopy foliar phosphorus content using hyperspectral data

Abstract Phosphorus (P) is an important element to litchi yield and fruit quality in addition to nitrogen (N) and potassium (K). This study was undertaken to explore the ability to predict P content using canopy reflectance. Some published indices and two ratio spectral indices (Ratio of reflectance index, RRI; Ratio of reflectance difference index, RRDI) developed by band interactive-optimization algorithms were investigated to determine their performance in predicting litchi canopy foliar P content. The results showed that optimal spectral indices selected by correlation analyses reached the highest level of accuracy in the retrieval of P content at each growth stage (R2cv = 0.54–0.98, RMSEcv = 0.02–0.03). The particular wavelengths of importance in the significant RRIs and RRDIs changed with the growing stages, cultivars and planting conditions. The sensitive wavebands ranged from the visible to the short-wave infrared (SWIR) regions, which are related to the absorption features of pigments (e.g., anthocyanin, chlorophyll), proteins, nitrogen, starch, sugar, oil, cellulose, and lignin. And the wavebands in SWIR region were used in the optimal RRIs and RRDIs for growth stages. This study demonstrates that the optimal RRDI is useful in predicting litchi foliar P content. The successes of use of SWIR in foliar nutrient monitoring is important for precision agriculture.

Decarbonizing China’s Urban Agglomerations

China’s urban agglomerations contribute 64 percent to China’s energy-related CO2 emissions and thus play a vital role in determining the future of climate change. There is little information available about city-level energy consumption and CO2 emissions; thus, we employ spatiotemporal modeling using Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) nighttime light imagery. Our findings show that such agglomerations have in fact experienced a remarkable decline in CO2 emission intensity—from 0.43 t/thousand yuan to 0.20 t/thousand yuan between 1995 and 2013, which constitutes an average annual decline of 4.34 percent. Despite still very high CO2 intensities in western China, a convergence of CO2 intensities across the country has occurred over the last few decades. Using panel regression modeling, we analyze differences in the decline of CO2 emission intensities due to regional differences in socioeconomic variables such as economic growth, population, economic structure, population density, and characteristics of urbanization. Factors that have hampered the decline of CO2 intensities are the ongoing industrialization that demands the increase in the production of heavy industry, in infrastructure investment, and in housing stock. Key Words: CO2 emission intensity, nighttime light imagery, spatiotemporal modeling, urban agglomerations. 中国的城市集聚, 生产了中国与能源相关的二氧化碳排放的百分之六十四, 因此在决定气候变迁的未来方面扮演了重要角色。但城市层级的能源消耗和二氧化碳排放的可及信息却相当稀少；因此我们运用时空模式化, 该模式化使用防卫气象卫星计画／线形扫描系统（DMSP/OLS）的夜间光影像。我们的研究发现显示, 这些集聚其实经历了二氧化碳排放密集度的明显减少——从1995年的0.43吨／千元到2013年的0.2吨／千元, 每年平均降低百分之四点三四。尽管中国西部的二氧化碳密度仍相当高, 过去数十年来仍发生了全国二氧化碳密度的聚合。我们运用面板迴归模型, 分析因诸如经济成长、人口、经济结构、人口密度与城市化特徵等社会经济变因的区域差异所导致的二氧化碳排放密度降低的差异。阻碍二氧化碳密度降低的因素是需要增加重工业生产、基础建设投资以及房地产需求的持续工业化。 关键词：二氧化碳排放密度, 夜间光影像, 时空模式化, 城市集聚。 Las aglomeraciones urbanas de China contribuyen el 64 por ciento de las emisiones chinas de CO2 relacionadas con energía, para así desempeñar un papel vital en la determinación futura del cambio climático. Hay poca información disponible acerca del consumo de energía a nivel de ciudad y de las emisiones de CO2; entonces, empleamos modelado espaciotemporal usando imágenes de luminosidad nocturna del Programa Satelital Meteorológico de la Defensa/Sistema Operacional de Escaneo en Línea (DMSP/OLS). Nuestros hallazgos muestran que de hecho tales aglomeraciones han experimentado una notable disminución en la intensidad de la emisión de CO2 ––de 0.43t/miles de yuanes a 0.20t/miles yuanes entre 1995 y 2013, lo cual constituye una declinación promedio anual de 4.34 por ciento. Pese a las intensidades de CO2 en China occidental todavía demasiado altas, una convergencia de intensidades del CO2 a través del país ha ocurrido durante las pasadas pocas décadas. Usando modelado de regresión de panel, analizamos las diferencias de la declinación de las intensidades en la emisión de CO2 debidas a diferencias regionales en variables socioeconómicas tales como crecimiento económico, población, estructura económica, densidad de población y características de la urbanización. Los factores que han dificultado la declinación en las intensidades del CO2 son la industrialización en marcha que demanda incremento en la producción de la industria pesada, en inversiones para infraestructura y en el inventario de vivienda.