Shuisen Chen

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.

An enhanced MODIS remote sensing model for detecting rainfall effects on sediment plume in the coastal waters of Apalachicola Bay

Mapping of total suspended solids (TSS) was conducted to investigate rainstorm-induced characteristic of sediment plume in the coastal waters of Apalachicola Bay. An improved TSS quadratic polynomial regression model (Calibration: R2=0.8586, N=32; validation: RMSE of 4.76 mg/l, N=30) for MODIS remote sensing was presented in this study. TSS mapping of MODIS before and after a rainstorm event showed distinct temporal-spatial variability of TSS concentration. Driven by ocean tidal current, a storm plume of width at about 40-50 km was formed flowing towards southwest of study area. The distinct boundary separating the highly turbid (west side) and relatively clear water (east side) was found near Sikes Cut. Further, by taking the TSS mapping under the low river discharge condition due to a local rainstorm as a reference of background TSS, two thresholds of TSS (25 and 45 mg/l respectively) were used to estimate the range of rainstorm plume and the central area of sediment load from surrounding land, and the spatial extent and evolution of the sediment plume during the local rainstorm. Besides, it was found that the storm plume concentration of TSS at east side of Sites Cut was quickly diluted under 25 mg/L, forming a storm plume towards east with width at about 7-8 km. The method developed in this study may be used to support coastal storm water research and management activities.

Papaya Tree Detection with UAV Images Using a GPU-Accelerated Scale-Space Filtering Method

The use of unmanned aerial vehicles (UAV) can allow individual tree detection for forest inventories in a cost-effective way. The scale-space filtering (SSF) algorithm is commonly used and has the capability of detecting trees of different crown sizes. In this study, we made two improvements with regard to the existing method and implementations. First, we incorporated SSF with a Lab color transformation to reduce over-detection problems associated with the original luminance image. Second, we ported four of the most time-consuming processes to the graphics processing unit (GPU) to improve computational efficiency. The proposed method was implemented using PyCUDA, which enabled access to NVIDIA’s compute unified device architecture (CUDA) through high-level scripting of the Python language. Our experiments were conducted using two images captured by the DJI Phantom 3 Professional and a most recent NVIDIA GPU GTX1080. The resulting accuracy was high, with an F-measure larger than 0.94. The speedup achieved by our parallel implementation was 44.77 and 28.54 for the first and second test image, respectively. For each 4000 × 3000 image, the total runtime was less than 1 s, which was sufficient for real-time performance and interactive application.

A total suspended sediment retrieval model for multiple estuaries and coasts by Landsat imageries

Based on 119 in-situ data from five estuaries and coasts of South China including Xunwen coast, estuary of Moyangjiang River, estuary and coast of Pearl River, estuary of Hanjiang River and estuary of Yangtze River, this paper aims to develop and establish a TSS retrieval model that applicable in different field conditions. After recalibrating and validating the form with the highest correlation coefficient between reflectance and TSS concentration and other TSS retrieval models that have been successful applied in many places, we found that the quadratic model of the ratio of logarithmic transformation of red band and near infrared band and logarithmic transformation of TSS concentration (QRLTSS) shows the highest performance. QRLTSS model based on Landsat OLI, ETM+ and TM can explained about 71% of the TSS concentration variation (4.3~577.2 mg/L) in the five regions and has a high and acceptable validation accuracy with root mean square error (RMSE) of 21.5-25mg/L and mean relative error (MRE) of 27.2-32.2%. We concluded that QRLTSS model can be used to quantify the TSS concentration of multiple estuaries and coasts of south China which would be helpful to understand the temporal and spatial variation of TSS in a large region. QRLTSS model should be applied to Landsat imagery for further validation in the future. The approach proposed in the paper also could promote the research work of establishing regional and uniform TSS retrieval model forward.

The spatial and temporal variation of total suspended solid concentration in Pearl River Estuary during 1987–2015 based on remote sensing

The movement and migration of total suspended solid (TSS) are the essential component of global material cycling and change. Based on the TSS concentrations retrieved from 112 scenes of Landsat remote sensing imageries during 1987-2015, the spatial and temporal variations of TSS concentration in high flow season and low flow seasons of six sub-regions (west shoal, west channel, middle shoal, east channel, east shoal and Pearl River Estuary Chinese White Dolphin National Nature Reserve and its adjacent waters (NNR)) of Pearl River Estuary (PRE) were analyzed and compared by statistical simulation. It was found that TSS concentrations in east and west shoals were about 23mg/L and 64mg/L higher than that of the middle shoal, respectively. There was a significant decreasing trend of TSS concentration from the northwest (223.7mg/L) to southeast (51.4mg/L) of study area, with an average reduction of 5.86mg/Lperkm, which mainly attributes to unique interaction of runoff and tide in PRE. In high flow season, there existed a significant and definite annual cycle period (5-8years) of TSS concentration change primarily responding to the periodic variation of precipitation. There were five full-fledged period changes of TSS detected in west shoal and west channel (the years of changes in 1988, 1994, 1998, 2003, 2010, 2015), while there were the last four cycle periods found in middle shoal, east channel, east shoal and NNR only. TSS concentrations in shoals and channels of PRE showed a significant decreased trend mainly due to the dam construction at the same time, with an average annual TSS concentration decrease of 5.7-10.1mg/L in high flow season from 1988 to 2015. There was no significant change trend of TSS concentration in NNR before 2003, but the TSS concentration decreased significantly after the establishment of the NNR since June 2003, with an average annual decrease of 9.7mg/L from 2004 to 2015. It was deduced that man-made protection measures had a great influence on the variation trend and intensity of TSS concentration in PRE, but had little effect on the cycle of TSS changes, indicating that the cyclical change is a very strong natural law. In low flow season, there was no significant change trend of TSS concentrations in PRE except that TSS concentrations in west channel and middle shoal showed a weak increasing trend (2.1mg/L and 2.9mg/L, respectively), which is probably because of controlled discharge for avoiding the intrusion of saltwater in PRE. Evidently, the change trend and cycle periods of TSS concentration in high- and low-flow seasons in six sub-regions of PRE had significant difference. The decreasing trend and cycle periods of TSS concentration mainly occurred in high flow season. The change trend and cycle periods of TSS concentration in low flow season was relatively small in PRE. The study shows that long series mapping of Landsat remote sensing images is an effective way to help understanding the spatial and temporal variation of TSS concentrations of estuaries and coasts, and to increase awareness of environmental change and human activity effects.

Spatiotemporal Analysis of MODIS Land Surface Temperature With In Situ Meteorological Observation and ERA-Interim Reanalysis: The Option of Model Calibration

The land surface temperature (<italic>Ts</italic>) is an important parameter in land surface and atmosphere studies. A set of synchronously observed “ground-truth” temperature as training data is required for some empirical/semiempirical statistical and neural network methods for retrieving <italic>Ts</italic> from passive microwave (PMW) remote sensing data. To provide information for the choice of the most suitable dataset in <italic>Ts </italic> retrieval of PMW remote sensing, the spatiotemporal comparison between the moderate-resolution imaging spectroradiometer <italic>Ts</italic> (MODIS <italic>Ts</italic>), the meteorologically observed <italic>Ts</italic> ( <italic>in situ Ts</italic>), the meteorologically observed near-surface air temperature (<italic>in situ Ta</italic>), and European Center for Medium-Range Weather Forecast reanalysis products, the ERA-Interim <italic>Ts</italic> (ERA <italic>Ts</italic>), in South China for each seasons daytime and nighttime is conducted in this paper. Results show that a large discrepancy between the MODIS <italic>Ts</italic> and the <italic>in situ Ts</italic> exists, whereas the discrepancies between the MODIS <italic>Ts</italic>, the <italic>in situ Ta</italic> and the ERA <italic> Ts</italic> are relatively smaller in daytime. For nighttime period, the differences between each dataset are relatively much smaller. Because the MODIS <italic>Ts</italic> is representative at the satellite pixel scale, it has a smaller spatial-scale mismatch with PMW data compared to <italic>in situ</italic> meteorological observation. The MODIS <italic>Ts</italic> is suitable for both the daytime and the nighttime PMW <italic>Ts</italic> model calibration if it is synchronously observed under almost clear-sky condition. By contrast, for the PMW <italic>Ts</italic> model calibration within the daytime period, the synchronously obtained <italic>in situ Ts</italic> is not suitable to be used as training data. If the ground temperature of daytime period derived from PMW is required, but the MODIS <italic> Ts</italic> is unavailable, the <italic>in situ Ta</italic> should be selected as the “ground truth” for the model calibration. However, it should be noticed that the inversion results are the near-surface air temperature rather than the <italic>Ts</italic>. Remarkably, reanalysis products such as the ERA <italic>Ts</italic> presents an alternative choice for both the daytime and the nighttime <italic>Ts</italic> model calibration if there are no MODIS <italic>Ts</italic> products or <italic>in situ</italic> temperature available. After the comparison, an example of PMW <italic>Ts</italic> retrieval for nighttime period was given, showing a promising performance on deriving an applicable PMW <italic>Ts</italic> inversion model based on the selection of an appropriate training dataset.

The design of intelligent expert classifier for featured crop mapping combining spectral library

In the study, a module was designed which was used to estimate yield of crop (such as lychee, banana and sugarcane etc) using spectral library of featured crops of South China. An approach combining spectral library with expert system classification methods by spatial data mining techniques is present. Being one of spatial data mining techniques, inductive learning algorithm is used to discover knowledge of spectral library for the expert system and is designed specially for monitoring featured crops of South China. So the intelligent expert classifier can make use of spectral data, attribute data and spatial data of spectral library to extract information of South Chinas featured crops. The study pre-defined some attributes of inductive learning algorithm which are in favor of featured crops mapping, for improving the efficiency of algorithm and ensuring the usability of rules. The estimation of lychee planting area of Shenzhen city in 2005 was presented as a case study. The following classification rules of lychee were acquired by running inductive learning algorithm: for example, 0.062≪ reflectance of TM2 ≪0.071, 0.47≪NDVI≪0.54, 20≪DEM≪90, special rules of lychee planting in Guangdong province, 40≪area of lychee orchard (pixel number) ≪5000 and the result of classification was presented by figure 2. Compared with traditional unsupervised classification, it improves classification accuracy greatly, and the rate of accuracy reaches 93.5% with KAPPA coefficient of 0.85. The result indicates that intelligent expert classifier is a better classification method for extracting of lychee planting area and is able to meet the need in agriculture application for quick crop area monitoring in South China.

Investigating Rainstorm Disturbance on Suspended Substance in Coastal Coral Reef Water Based on MODIS Imagery and Field Measurements

From July 11-12, 2009, the tropical storm Soudeler swept the study area with a Level 8 wind and disturbed the suspended substance in this coastal area, which may have caused some fatal impact on the health condition of coral reef in Xuwen coral reef coast located in Leizhou Peninsula of South China. In order to evaluate the impact of extreme weather on coral reef, this study applied and validated a TSS model to map the TSS variation based on red and infrared spectral bands of MODIS data through one before-storm and two after-storm images after applying the atmospheric correction of in-water linear regression analysis. By mapping and comparing the changes of TSS values before- and after- tropical storm, this study found substantial increases of TSS concentrations as a mean value of 47.8 mg/L (~3.6 times of mean TSS value before rainstorm) in the area during the passage of tropical storm compared to those under no-storm condition. Besides, the TSS returned back to even lower values five days after the passage of tropical storm as a mean value of 3.6mg/L (~one quarter of mean TSS value (13.4 mg/L) before rainstorm). The conclusion was made that the TSS concentration in estuary and coastal areas under local rainstorm tends to return to a normal level faster (approximately 2.5 days) than under a hurricane [1] or tropical storm as discovered in this study (approximately 5 days). Compared to the less frequent and non-synoptic in-situ field sampling approach, the synoptic and frequent sampling facilitated by frequent remote sensing imagery of MODIS provides an improved assessment of TSS concentration and two-dimensional distribution patterns and is recommended to be used as a valuable tool for frequently monitoring coral reef water quality in coastal water bodies of China and other areas in the world if applicable.

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.

Temperature variation and winter planted potato's NDVI change during early 2016's super cold wave in Guangdong province, South China

under the background of climate warming, obvious decrease trend in frequency and intensity of cold wave that invaded Guangdong province, South China have been observed in recent years. However in January of 2016, a named “super cold wave” which caused serious influence on human health and agriculture production invaded Guangdong. In this paper, satellite derived land surface temperature (LST) from GCOM-W1/AMSR2 brightness temperature (TB) were retrieved. Based on the satellite-derived LST, its variation characteristic was analyzed, which showed an apparent decrease trend and increase trend in Guangdong province during the whole “super cold wave” process. Moreover, based on NDVI retrieved from HJ-1 A/B satellites CCD sensors, obvious declination of potatos NDVI after the “super cold wave” can be seen: nearly 89% area of potatos NDVI decreased reflecting great loss on potatos production which was confirmed by in situ investigation. This study demonstrated that multi-sources satellite remote sensing data have the capability of monitoring temperature change in cold wave and assessing crop loss after cold injury.