Yunxiao Sun

A Soil Moisture Retrieval Method Based on Typical Polarization Decomposition Techniques for a Maize Field from Full-Polarization Radarsat-2 Data

Soil moisture (SM) estimates are important to research, but are not accurately predictable in areas with tall vegetation. Full-polarization Radarsat-2 C-band data were used to retrieve SM contents using typical polarization decomposition (Freeman–Durden, Yamaguchi and VanZly) at different growth stages of maize. Applicability analyses were conducted, including proportion, regression and surface scattering model analyses. Furthermore, the Bragg, the extended Bragg scattering model (X-Bragg) and improved surface scattering models (ISSM) were used to retrieve SM content. The results indicated that the VanZly decomposition method was the best. The proportion of surface scattering in the proportion analysis was highest (>52%), followed by that in the Yamaguchi method (>41%). The R2 (>0.6144) between surface scattering and SM was significantly higher (R2 0.6599) and lower absolute error (AE) (<5.82) and root mean square error (RMSE) (<3.73). The best algorithm was obtained at the sowing stage (R2 = 0.8843, AE = 3.13, RMSE = 1.76). In addition, the X-Bragg model provided better approximation of actual surface scattering without the measured data (better algorithm: R2 = 0.8314, AE = 4.39, RMSE = 2.81).

Spatial and Temporal Analysis of the Mitigating Effects of Industrial Relocation on the Surface Urban Heat Island over China

Urbanization is typically accompanied by the relocation and reconstruction of industrial areas due to limited space and environmental requirements, particularly in the case of a capital city. Shougang Group, one of the largest steel mill operators in China, was relocated from Beijing to Hebei Province. To study the thermal environmental changes at the Shougang industrial site before and after relocation, four Landsat images (from 2000, 2005, 2010 and 2016) were used to calculate the land surface temperature (LST). Using the urban heat island ratio index (URI), we compared the LST values for the four images of the investigated area. Following the relocation of Shougang Group, the URI values decreased from 0.55 in 2005 to 0.21 in 2016, indicating that the surface urban heat island effect in the area was greatly mitigated; we infer that this effect was related to steel production. This study shows that the use of Landsat images to assess industrial thermal pollution is feasible. Accurate and rapid extraction of thermal pollution data by remote sensing offers great potential for the management of industrial pollution sources and distribution, and for technical support in urban planning departments.

Study on a bidirectional reflectance distribution function inversion model based on HJ-1 CCD imagery

The surface bidirectional reflectance distribution function (BRDF) is an important factor in depicting the bidirectional reflectance characteristics of the land surface. In this study, BRDF is first inversed using a semiempirical, kernel-driven Algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface (Ambrals) model based on 4-day charge-coupled device (CCD) data from the HJ-1B satellite under clear sky conditions. Then, according to application needs, the inversion results of different angles are unified to the same observation angle to realize the radiometric normalization of BRDF at different viewing and incident directions. Finally, the inversed BRDF is compared with the measured BRDF in the principle plane and perpendicular plane of the Sun, respectively. The results show that: (1) The inversed BRDF based on the kernel-driven model is in good agreement with the measured BRDF. (2) The vegetation bidirectional reflectance in a backward scattering direction is higher than that in a forward scattering direction in the principle plane of the Sun. There is also a “hot spot” in the backward scattering direction. Additionally, the forward bidirectional reflectance is symmetrical relative to the backward one in the perpendicular plane of the Sun. (3) The geometric optical effect is more apparent in the visible bands of HJ-1B CCD, while the volume scattering effect is more significant in the near-infrared band. The Ambrals model and the procedures used in this study are effective and adapt to the characteristics of HJ-1B/CCD images. Therefore, our findings could advance the applications of the HJ-1 satellite and the development of quantitative remote sensing.

Canopy Structure Attributes Extraction from LiDAR Data Based on Tree Morphology and Crown Height Proportion

Urban green space is important for the well-being of urban residents. Seeking for three spatial dimension stereopsis is a very important issue in investigating urban green space. A potential applicability in the domain of urban tree space measurement and modelling has been explored based on LiDAR data in our study. This paper aims to present a framework—through a more automatic way—to extract canopy structure attributes. In this study, treetops were filtered by local maxima filtering algorithm from canopy height model. An improved spoke wheel algorithm was used to delineate the crown boundaries. And, an estimation issue of crown volume was simplified into three measurable parameters by estimating the crown structures. For accuracy assessment, data of 363 sampled trees located in the subset of Székesfehérvár city were selected randomly. The overall detection rate of treetop had proven to be 95.87% and crown boundaries were recognized effectively with a delineation quality of 88.59%, which were acceptable. About 80.26% of investigated crown volume estimates were obtained with shape distortion ranging from 3.1 to 7.8% according to the error analysis. The results indicated that the method can be used to extract canopy structure in urban areas.

Assessing the impacts of grain sizes on landscape pattern of urban green space

As an important part of the city, urban green space (UGS) plays an essential role in enhancing human well-being by virtue of multiple environmental, social and economic benefits. Study on landscape pattern of UGS is a focal point and hotspot in landscape ecology. The latest studies demonstrated that landscape metrics provides an effective method in quantifying UGS pattern. However, the study of the scale effect of landscape metrics should be strengthened. The objective of scale related research in UGS is to determine the appropriate scale in the measurement and evaluation of UGS and to find the underlying mechanisms by use of the selected scales. This study aims to identify the scale characteristics and scale domain of UGS pattern, and provide basic information for pattern analysis and scaling in UGS research. In this paper, taking the central urban area of Székesfehérvár in Hungary as an example, we firstly extracted UGS from WordView-2 multi-spectral image (2m), then obtained a series of grain sizes by upscaling, and finally calculated and analyzed the characteristics of different landscape metrics with varying grain sizes. In this study, both the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Green Index (NDGI) were used to ensure the accuracy of the green space extraction in high spatial resolution image. On the basis of green space extraction, the green space patterns at different grain sizes were obtain by the assembly of grid cells. A total of 20 grain sizes were selected in this paper, ranging from 2 m to 40 m with a step size of 2 m. Landscape metrics both under class and landscape levels, including Patch Density (PD), Percentage of Landscape (PLAND), Mean Perimeter-Area Fractal Dimension (FRAC_MN), Division Index (DIVISION), Cohesion Index (COHESION), and Shannon’s Evenness Index (SHEI) were calculated. The results demonstrated that with the increase of grain size, the landscape metrics under class level and landscape level were significantly affected by the grain size, and there was obvious critical grain size. On the whole, 16 m is the critical grain size of the green space pattern, and the suitable grain size for landscape metrics calculation of UGS ranges from 2 m to 16 m. The responding curves were varied by landscape metrics. Some metrics had clear changing trend and obvious turning grain size, while the others also had obvious turning grain size, but without clear changing trend. According to scale inflexions and responding curves discussed in the paper, scale domains of landscape metrics were confirmed. Generally, from 2 m to 16 m was the scale domain of UGS pattern, which means that related ecological model of UGS can be scaled across this scale extent by ordinary transformation. The study of impacts of changing scale on UGS can provide a reference for understanding the ecological benefits of UGS and optimizing the green space pattern.

Using mathematical morphology on LiDAR data to extract information from urban vegetation

Accurate delineation of individual tree crowns in human settlement is of vital importance to decision-making in environmental management. Increasing availability of LiDAR data and applications of mathematical morphology imply a paradigm shift in tree crown delineation. This paper introduces a new approach based on “mathematical morphology on grey-level images” that enables such delineation. We consider a LiDAR data set as a grey-level image in which the indexes are the (x,y) locations on the grid and in which each value is the corresponding height of the point acquired by using the LiDAR sensor (i.e. top of the tree at the (x,y) location). We have applied this approach to a large data set in the frame of a partnership with a Hungarian University, and the results we obtain are closely related to what can be seen on a 3D visualization of the LiDAR data set.

Walking with green scenery: Exploring street-level greenery in terms of visual perception

A potential application of LiDAR data in street-level greenery assessment has been explored in this study. Taking full advantage of the fine-scale tree structure modeling of LiDAR data, we propose a new green view index calculation method to assess street greenery visualization. Results indicate that LiDAR data is capable of differentiating the amount of greenness being perceived from different sites and the calculation method gives an objective measure of street-level greenery.