Assessing the cold island effect of urban parks in metropolitan cores: a case study of Hangzhou, China.

As the oasis area in the city, urban park plays an extremely prominent role in the regulation and improvement of the urban ecological environment, especially the local thermal environment, and has become one of the significant ways to reduce the urban heat island (UHI) effect. Our study comprehensively considers the maximum cooling distance and spatial continuity of urban parks, takes 30 parks in Hangzhou, and analyzed their influencing factors to comprehensively explore the park cooling effect. The results showed that the land cover shifted drastically during 2000-2020, and the built-up land area increased greatly, which aggravated the UHI effect. The high UHI value of Hangzhou was concentrated in the city center and presented a spreading trend from north to south. Different types of urban parks presented different cold island effects, with comprehensive parks and ecological parks having the largest cooling area, and community parks exhibit better accumulative cooling effect. In addition, the park's own characteristics (perimeter, area, shape index) and inner and surrounding landscapes were significantly correlated with the park's cooling effect (park cooling area and park cooling efficiency). Our study comprehensively considered the cooling effect of parks from the maximum and accumulative perspectives and provides theoretical and practical guidance for the construction and planning of urban parks, thereby enhancing the well-being of urban residents.

Biogenic Silica and Organic Carbon Records in Zhoushan Coastal Sea over the Past One Hundred Years and Their Environmental Indications.

The influence of terrestrial and marine input has dramatically changed eutrophication in coastal seas over the past 100 years. In this study, Zhoushan coastal sea (ZCS) is taken as a study area. We studied ZCS as it is a sink of the temporal and spatial variation of primary productivity, dominant species of algae, and the variation of provenance in this area over the past 100 years. We performed analysis using three sediment cores and the carbon and silicon deposition records. The analysis results demonstrate that: (1) The primary productivity in the northern area of the ZCS close to the Yangtze Estuary was the highest comparatively, but it declined slightly before 2010. The primary productivity in the southern area had an increasing trend over the past 100 years. The value of total organic carbon (TOC) in the northern area was relatively high, with an average value of 0.532% over the past 100 years, with a decreasing trend in recent years. On the contrary, TOC in the southern area was relatively low, but it was increased dramatically after 1995. (2) Diatom might play an important role in the variations. The biogenic silica (BSi) and TOC in the northern area showed a synchronous declining trend, while the BSi/TOC ratio did not change significantly. This indicates the algae population structure in this area was relatively stable over the past 100 years. The BSi/TOC ratio decreased continuously in the southern area, indicating that the dominance of diatoms was decreasing continuously. (3) The variation of diatom dominance in this area might have a great relationship with the change of nutrients' provenance. A mean value of stable carbon isotope (δ13C) in the north of Zhoushan was -23.46‱, indicating that the terrestrial-source input was the highest. (4) The change of provenance in the study area was quite different. This illustrates that the terrestrial input impacted the largest area of ZCS while marine input became dominant in the offshore area.

Landscape Grain Effect in Yancheng Coastal Wetland and Its Response to Landscape Changes.

The landscape grain effect reflects the spatial heterogeneity of a landscape and it is used as a research core of landscape ecology. The landscape grain effect can be used to not only explore spatiotemporal variation characteristics of a landscape pattern, but also to disclose variation laws of ecological structures and functions of landscapes. In this study, the sensitivity of landscape pattern indexes to grain sizes 50-1000 m was studied based on landscape data in Yancheng Coastal Wetland acquired in 1991, 2000, 2008, and 2017. Response of the grain effect to landscape changes was analyzed and an optimal grain size for analysis in the study area was determined. Results indicated that: (1) among 27 indexes (12 in a class level and 15 in a landscape level), eight indexes were highly sensitive to grains, ten indexes presented moderate sensitivity, eight indexes presented low sensitivity, and one was unresponsive. It was shown that the area-margin index and the shape index were more sensitive to the different grain sizes. The aggregation index had some differences in the grain size change, and the diversity index had a low response degree to the grain size. (2) Landscape indexes showed six different responses to different grains, including slow reduced response, fast reduced and then slow reduced response, monotonically increased response, fluctuating reduced response, up-down responses, and stable response, which indicated that the landscape index was closely related to the spatial grain. (3) From 1991 to 2017, variation curves of the landscape grain size of different landscape types could be divided into four types: fluctuation rising type, fluctuation type, monotonous decreasing type, and monotonous rising type. Different grain size curves had different interpretations of landscape changes, but in general, Yancheng Coastal Wetland's landscape tended to be fragmented and complicated, internal connectivity was weakened, and dominant landscape area was reduced. Natural wetlands were more sensitive to grain size effects than artificial wetlands. (4) The landscape index at the 50 m grain size had a strong response to different grain size changes, and the loss of landscape information was the smallest. Therefore, it was determined that the optimal landscape grain size in the study area was 50 m.

Estimation of chlorophyll a content in inland turbidity waters using WorldView-2 imagery: a case study of the Guanting Reservoir, Beijing, China.

Complex optical properties, such as non-pigment suspension and colored dissolved organic matter (CDOM), make it difficult to achieve accurate estimations of remotely sensed chlorophyll a (Chla) content of inland turbidity. Recent attempts have been made to estimate Chla based on red and near-infrared regions where non-pigment suspension and CDOM have little effect on water reflectance. The objective of this study is to validate the applicability of WV-2 imagery with existing effective estimation methods from MERIS when estimating Chla content in inland turbidity waters. The correlation analysis of measured Chla content and WV-2 imagery bands shows that the Chla sensitive bands of WV-2 are red edge, NIR 1, and NIR 2. The coastal band is designed for seawater Chla detection. However, the high correlation with turbidity data and low correlation with Chla made coastal band unsuitable for estimating Chla in inland waters. The high-resolution water body images were extracted by combining the spectral products (NDWI) with the spatial morphological products (sobel edge detection). The estimation results show that the accuracy of the single band and NDCI is not as good as the two-band method, three-band method, stepwise regression algorithm (SRA) and support vector machines (SVM). The SVM estimation accuracy was the highest with an R2, RMSE, and URMSE of 0.8387, 0.4714, and 19.11%, respectively. This study demonstrates that the two-band and three-band methods are effective for estimating Chla in inland water for WV-2 imagery. As a high-precision estimation method, SVM has great potential for inland turbidity water Chla estimation.

Changes of Ecosystem Service Value in a Coastal Zone of Zhejiang Province, China, during Rapid Urbanization.

Gains and losses in ecosystem service values (ESV) in coastal zones in Zhejiang Province during rapid urbanization were analyzed in terms of land-use changes. Decision-making on coastal development based on ESV estimation is significant for the sustainable utilization of coastal resource. In this study, coastal land-use changes in Zhejiang Province during rapid urbanization were discussed based on remote-sensing derived land-use maps created in the years 1990, 2000 and 2010. The ESV changes in coastal zones in Zhejiang Province from 1990 to 2010 were estimated by using the established ESV estimation model. The analysis results demonstrate the following: (1) with the continuous acceleration of urbanization, land-use types in coastal zones in Zhejiang Province changed significantly from 1990 to 2010, demonstrated by considerable growth of urban construction land and reduction of forest land and farmland; (2) in the study period, the total ESV in coastal zones in Zhejiang Province continuously decreased in value from RMB 35.278 billion to 29.964 billion, a reduction of 15.06%; (3) in terms of the spatial distribution of ESV, the ESVs in coastal zones in Zhejiang Province were generally converted from a higher ESV to a lower ESV; (4) estimates of ESV for the three years 1990, 2000 and 2010 appear to be relatively stable; and (5) land-use intensity in coastal zones in Zhejiang Province continuously increased during the 20 years. The spatial distribution of land-use intensity was consistent with that of the ESV change rate. Disordered land-use changes from forestland and farmland to urban construction land was a major cause of ESV loss.

Estimation of nitrogen vertical distribution by bi-directional canopy reflectance in winter wheat.

Timely measurement of vertical foliage nitrogen distribution is critical for increasing crop yield and reducing environmental impact. In this study, a novel method with partial least square regression (PLSR) and vegetation indices was developed to determine optimal models for extracting vertical foliage nitrogen distribution of winter wheat by using bi-directional reflectance distribution function (BRDF) data. The BRDF data were collected from ground-based hyperspectral reflectance measurements recorded at the Xiaotangshan Precision Agriculture Experimental Base in 2003, 2004 and 2007. The view zenith angles (1) at nadir, 40° and 50°; (2) at nadir, 30° and 40°; and (3) at nadir, 20° and 30° were selected as optical view angles to estimate foliage nitrogen density (FND) at an upper, middle and bottom layer, respectively. For each layer, three optimal PLSR analysis models with FND as a dependent variable and two vegetation indices (nitrogen reflectance index (NRI), normalized pigment chlorophyll index (NPCI) or a combination of NRI and NPCI) at corresponding angles as explanatory variables were established. The experimental results from an independent model verification demonstrated that the PLSR analysis models with the combination of NRI and NPCI as the explanatory variables were the most accurate in estimating FND for each layer. The coefficients of determination (R2) of this model between upper layer-, middle layer- and bottom layer-derived and laboratory-measured foliage nitrogen density were 0.7335, 0.7336, 0.6746, respectively.

Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery.

Powdery mildew is one of the most serious diseases that have a significant impact on the production of winter wheat. As an effective alternative to traditional sampling methods, remote sensing can be a useful tool in disease detection. This study attempted to use multi-temporal moderate resolution satellite-based data of surface reflectances in blue (B), green (G), red (R) and near infrared (NIR) bands from HJ-CCD (CCD sensor on Huanjing satellite) to monitor disease at a regional scale. In a suburban area in Beijing, China, an extensive field campaign for disease intensity survey was conducted at key growth stages of winter wheat in 2010. Meanwhile, corresponding time series of HJ-CCD images were acquired over the study area. In this study, a number of single-stage and multi-stage spectral features, which were sensitive to powdery mildew, were selected by using an independent t-test. With the selected spectral features, four advanced methods: mahalanobis distance, maximum likelihood classifier, partial least square regression and mixture tuned matched filtering were tested and evaluated for their performances in disease mapping. The experimental results showed that all four algorithms could generate disease maps with a generally correct distribution pattern of powdery mildew at the grain filling stage (Zadoks 72). However, by comparing these disease maps with ground survey data (validation samples), all of the four algorithms also produced a variable degree of error in estimating the disease occurrence and severity. Further, we found that the integration of MTMF and PLSR algorithms could result in a significant accuracy improvement of identifying and determining the disease intensity (overall accuracy of 72% increased to 78% and kappa coefficient of 0.49 increased to 0.59). The experimental results also demonstrated that the multi-temporal satellite images have a great potential in crop diseases mapping at a regional scale.

Natural and anthropogenic influences on a red-crowned crane habitat in the Yellow River Delta Natural Reserve, 1992-2008.

This study aims to assess the relative importance of natural and anthropogenic variables on the change of the red-crowned crane habitat in the Yellow River Nature Reserve, East China using multitempopral remote sensing and geographic information system. Satellite images were used to detect the change in potential crane habitat, from which suitable crane habitat was determined by excluding fragmented habitat. In this study, a principal component analysis (PCA) with seven variables (channel flow, rainfall, temperature, sediment discharge, number of oil wells, total length of roads, and area of settlements) and linear regression analyses of potential and suitable habitat against the retained principal components were applied to explore the influences of natural and anthropogenic factors on the change of the red-crowned crane habitat. The experimental results indicate that suitable habitat decreased by 5,935 ha despite an increase of 1,409 ha in potential habitat from 1992 to 2008. The area of crane habitat changed caused by natural drivers such as progressive succession, retrogressive succession, and physical fragmentation is almost the same as that caused by anthropogenic forces such as land use change and behavioral fragmentation. The PCA and regression analyses revealed that natural factors (e.g., channel flow, rainfall, temperature, and sediment discharge) play an important role in the crane potential habitat change and human disturbances (e.g., oil wells, roads, and settlements) jointly explain 51.8 % of the variations in suitable habitat area, higher than 48.2 % contributed by natural factors. Thus, it is vital to reduce anthropogenic influences within the reserve in order to reverse the decline in the suitable crane habitat.

Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor, Florida, USA.

In the event of a natural or anthropogenic disturbance, environmental resource managers require a reliable tool to quickly assess the spatial extent of potential damage to the seagrass resource. The temporal availability of the Landsat 5 Thematic Mapper (TM) imagery provided a suitable option to detect and assess damage of the submerged aquatic vegetation (SAV). This study examined Landsat TM imagery classification techniques to create two-class (SAV presence/absence) and three-class (SAV estimated coverage) SAV maps of the seagrass resource. The Mahalanobis Distance method achieved the highest overall accuracy (86%) and validation accuracy (68%) for delineating the seagrass resource (two-class SAV map). The Maximum Likelihood method achieved the highest overall accuracy (74%) and validation accuracy (70%) for delineating the seagrass resource three-class SAV map. The Landsat 5 TM imagery classification provided a seagrass resource map product with similar accuracy to the aerial photointerpretation maps (validation accuracy 71%). The results support the application of remote sensing methods to analyze the spatial extent of the seagrass resource.

Mapping urban forest tree species using IKONOS imagery: preliminary results.

A stepwise masking system with high-resolution IKONOS imagery was developed to identify and map urban forest tree species/groups in the City of Tampa, Florida, USA. The eight species/groups consist of sand live oak (Quercus geminata), laurel oak (Quercus laurifolia), live oak (Quercus virginiana), magnolia (Magnolia grandiflora), pine (species group), palm (species group), camphor (Cinnamomum camphora), and red maple (Acer rubrum). The system was implemented with soil-adjusted vegetation index (SAVI) threshold, textural information after running a low-pass filter, and brightness threshold of NIR band to separate tree canopies from non-vegetated areas from other vegetation types (e.g., grass/lawn) and to separate the tree canopies into sunlit and shadow areas. A maximum likelihood classifier was used to identify and map forest type and species. After IKONOS imagery was preprocessed, a total of nine spectral features were generated, including four spectral bands, three hue-intensity-saturation indices, one SAVI, and one texture image. The identified and mapped results were examined with independent ground survey data. The experimental results indicate that when classifying all the eight tree species/ groups with the high-resolution IKONOS image data, the identifying accuracy was very low and could not satisfy a practical application level, and when merging the eight species/groups into four major species/groups, the average accuracy is still low (average accuracy = 73%, overall accuracy = 86%, and κ = 0.76 with sunlit test samples). Such a low accuracy of identifying and mapping the urban tree species/groups is attributable to low spatial resolution IKONOS image data relative to tree crown size, to complex and variable background spectrum impact on crown spectra, and to shadow/shaded impact. The preliminary results imply that to improve the tree species identification accuracy and achieve a practical application level in urban area, multi-temporal (multi-seasonal) or hyperspectral data image data should be considered for use in the future.

Invasive species change detection using artificial neural networks and CASI hyperspectral imagery.

For monitoring and controlling the extent and intensity of an invasive species, a direct multi-date image classification method was applied in invasive species (salt cedar) change detection in the study area of Lovelock, Nevada. With multidate Compact Airborne Spectrographic Imager (CASI) hyperspectral data sets, two types of hyperspectral CASI input data and two classifiers have been examined and compared for mapping and monitoring the salt cedar change. The two types of input data are all two-date original CASI bands and 12 principal component images (PCs) derived from the two-date CASI images. The two classifiers are an artificial neural network (ANN) and linear discriminant analysis (LDA). The experimental results indicate that (1) the direct multitemporal image classification method applied in land cover change detection is feasible either with original CASI bands or PCs, but a better accuracy was obtained from the CASI PCA transformed data; (2) with the same inputs of 12 PCs, the ANN outperforms the LDA due to the ANN's non-linear property and ability of handling data without a prerequisite of a certain distribution of the analysis data.

Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval.

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods.

Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index.

In this study, a comparative analysis of capabilities of three sensors for mapping forest crown closure (CC) and leaf area index (LAI) was conducted. The three sensors are Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) onboard EO-1 satellite and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A total of 38 mixed coniferous forest CC and 38 LAI measurements were collected at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) extracting spectral vegetation indices (VIs), spectral texture information and maximum noise fractions (MNFs), (2) establishing multivariate prediction models, (3) predicting and mapping pixel-based CC and LAI values, and (4) validating the mapped CC and LAI results with field validated photo-interpreted CC and LAI values. The experimental results indicate that the Hyperion data are the most effective for mapping forest CC and LAI (CC mapped accuracy (MA) = 76.0%, LAI MA = 74.7%), followed by ALI data (CC MA = 74.5%, LAI MA = 70.7%), with ETM+ data results being least effective (CC MA = 71.1%, LAI MA = 63.4%). This analysis demonstrates that the Hyperion sensor outperforms the other two sensors: ALI and ETM+. This is because of its high spectral resolution with rich subtle spectral information, of its short-wave infrared data for constructing optimal VIs that are slightly affected by the atmosphere, and of its more available MNFs than the other two sensors to be selected for establishing prediction models. Compared to ETM+ data, ALI data are better for mapping forest CC and LAI due to ALI data with more bands and higher signal-to-noise ratios than those of ETM+ data.