Classification and detection of dominant factors in geospatial patterns of traditional settlements in China

Journal of Geographical Sciences - The geospatial distribution pattern in traditional Chinese settlements (TCSs) reflects the traditional harmony between humans and nature, which has been learned...     

Hydrologic processes at the urban residential scale

In the face of increasing urbanization, there is growing interest in application of microscale hydrologic solutions to minimize storm runoff and conserve water at the source. In this study, a physically based numerical model was developed to understand hydrologic processes better at the urban residential scale and the interaction of these processes among different best management practices (BMPs). This model simulates hydrologic processes using an hourly interval for over a full year or for specific storm events. The model was applied to treatment and control single‐family residential parcels in Los Angeles, California. Data collected from the control and treatment sites over 2 years were used to calibrate and validate the model. Annual storm runoff to the street was eliminated by 97% with installation of rain gutters, a driveway interceptor, and lawn retention basin. Evaluated individually, the driveway interceptor was the most effective BMP for storm runoff reduction (65%), followed by the rain gutter installation (28%), and lawn converted to retention basin (12%). An 11 m³ cistern did not substantially reduce runoff, but provided 9% of annual landscape irrigation demand. Simulated landscape irrigation water use was reduced 53% by increasing irrigation system efficiency, and adjusting application rates monthly based on plant water demand. The model showed that infiltration and surface runoff processes were particularly sensitive to the soil's physical properties and its effective depth. Replacing the existing loam soil with clay soil increased annual runoff discharge to the street by 63% when climate and landscape features remained unchanged. Copyright © 2006 John Wiley & Sons, Ltd.     

Impact of data model and point density on aboveground forest biomass estimation from airborne LiDAR

Background

Accurate estimation of aboveground forest biomass (AGB) and its dynamics is of paramount importance in understanding the role of forest in the carbon cycle and the effective implementation of climate change mitigation policies. LiDAR is currently the most accurate technology for AGB estimation. LiDAR metrics can be derived from the 3D point cloud (echo-based) or from the canopy height model (CHM). Different sensors and survey configurations can affect the metrics derived from the LiDAR data. We evaluate the ability of the metrics derived from the echo-based and CHM data models to estimate AGB in three different biomes, as well as the impact of point density on the metrics derived from them.

Results

Our results show that differences among metrics derived at different point densities were significantly different from zero, with a larger impact on CHM-based than echo-based metrics, particularly when the point density was reduced to 1 point m^?2. Both data models-echo-based and CHM-performed similarly well in estimating AGB at the three study sites. For the temperate forest in the Sierra Nevada Mountains, California, USA, R² ranged from 0.79 to 0.8 and RMSE (relRMSE) from 69.69 (35.59%) to 70.71 (36.12%) Mg ha^?1 for the echo-based model and from 0.76 to 0.78 and 73.84 (37.72%) to 128.20 (65.49%) Mg ha^?1 for the CHM-based model. For the moist tropical forest on Barro Colorado Island, Panama, the models gave R² ranging between 0.70 and 0.71 and RMSE between 30.08 (12.36%) and 30.32 (12.46) Mg ha^?1 [between 0.69–0.70 and 30.42 (12.50%) and 61.30 (25.19%) Mg ha^?1] for the echo-based [CHM-based] models. Finally, for the Atlantic forest in the Sierra do Mar, Brazil, R² was between 0.58–0.69 and RMSE between 37.73 (8.67%) and 39.77 (9.14%) Mg ha^?1 for the echo-based model, whereas for the CHM R² was between 0.37–0.45 and RMSE between 45.43 (10.44%) and 67.23 (15.45%) Mg ha^?1.

Conclusions

Metrics derived from the CHM show a higher dependence on point density than metrics derived from the echo-based data model. Despite the median of the differences between metrics derived at different point densities differing significantly from zero, the mean change was close to zero and smaller than the standard deviation except for very low point densities (1 point m^?2). The application of calibrated models to estimate AGB on metrics derived from thinned datasets resulted in less than 5% error when metrics were derived from the echo-based model. For CHM-based metrics, the same level of error was obtained for point densities higher than 5 points m^?2. The fact that reducing point density does not introduce significant errors in AGB estimates is important for biomass monitoring and for an effective implementation of climate change mitigation policies such as REDD + due to its implications for the costs of data acquisition. Both data models showed similar capability to estimate AGB when point density was greater than or equal to 5 point m^?2.

     

Winter rainfall interception by two mature open‐grown trees in Davis,California

A rainfall interception measuring system was developed and tested for open‐grown trees. The system includes direct measurements of gross precipitation, throughfall and stemflow, as well as continuous collection of micrometeorological data. The data were sampled every second and collected at 30‐s time steps using pressure transducers monitoring water depth in collection containers coupled to Campbell CR10 dataloggers. The system was tested on a 9‐year‐old broadleaf deciduous tree (pear, Pyrus calleryana ‘Bradford’) and an 8‐year‐old broadleaf evergreen tree (cork oak, Quercus suber) representing trees having divergent canopy distributions of foliage and stems. Partitioning of gross precipitation into throughfall, stemflow and canopy interception is presented for these two mature open‐grown trees during the 1996–1998 rainy seasons. Interception losses accounted for about 15% of gross precipitation for the pear tree and 27% for the oak tree. The fraction of gross precipitation reaching the ground included 8% by stemflow and 77% by throughfall for the pear tree, as compared with 15% and 58%, respectively, for the oak tree. The analysis of temporal patterns in interception indicates that it was greatest at the beginning of each rainfall event. Rainfall frequency is more significant than rainfall rate and duration in determining interception losses. Both stemflow and throughfall varied with rainfall intensity and wind speed. Increasing precipitation rates and wind speed increased stemflow but reduced throughfall. Analysis of rainfall interception processes at different time‐scales indicates that canopy interception varied from 100% at the beginning of the rain event to about 3% at the maximum rain intensity for the oak tree. These values reflected the canopy surface water storage changes during the rain event. The winter domain precipitation at our study site in the Central Valley of California limited our opportunities to collect interception data during non‐winter seasons. This precipitation pattern makes the results more specific to the Mediterranean climate region. Copyright © 2000 John Wiley & Sons, Ltd.     

The development of a geographic information system (GIS) database for Jiuzhaigou national nature reserve and its application

The objective of this paper is to develop a GIS (Geographic Information System) database for Jiuzhaigou National Nature Reserve (Jiuzhaigou, hereafter) in China and demonstrate its application as a research tool. A cost-effective procedure was developed to compile a variety of geographical and biological data of the study area in terms of popular GIS format such as shape files. These files were further calibrated and validated using field surveys data. The developed GIS database was used to quantify the distributions of the wildlife (amphibians, mammals, and birds) using the distances of the wildlife to the centerline of the bus-tour routes. The Pearson correlation coefficient was used to quantify the correlation in space between pairs of different wildlife using the number of habitats for given space contexts. An ArcObject-based macro was developed to perform the analysis. The results showed the majority of the habitats of wildlife are located in the proximity of the tour-bus routes with an average distance ranging from 564 to 894 m depending on types of wildlife. This indicates a possibility of the disturbance to the wildlife by human activities. The correlation coefficient of the wildlife ranged from 0.36 to 0.64 depending on pairs of wildlife, indicating some correlations in space. However, due to the limited sample size, the statistical significances need to be further investigated. This paper has successfully demonstrated the use of the GIS-based database as a research tool for environmental study.     

Mapping of freshwater lake wetlands using object-relations and rule-based inference

Inland freshwater lake wetlands play an important role in regional ecological balance.Hongze Lake is the fourth biggest freshwater lake in China.In the past three decades,there has been significant loss of freshwater wetlands within the lake and at the mouths of neighboring rivers,due to disturbance,primarily from human activities.The main purpose of this paper was to explore a practical technology for differentiating wetlands effectively from upland types in close proximity to them.In the paper,an integrated method,which combined per-pixel and per-field classification,was used for mapping wetlands of Hongze Lake and their neighboring upland types.Firstly,Landsat ETM+ imagery was segmented and classified by using spectral and textural features.Secondly,ETM+ spectral bands,textural features derived from ETM+ Pan imagery,relative relations between neighboring classes,shape features,and elevation were used in a decision tree classification.Thirdly,per-pixel classification results from the decision tree classifier were improved by using classification results from object-oriented classification as a context.The results show that the technology has not only overcome the salt-and-pepper effect commonly observed in the past studies,but also has improved the accuracy of identification by nearly 5%.     

Retrieval of Fresh Leaf Fuel Moisture Content Using Genetic Algorithm Partial Least Squares (GA-PLS) Modeling

Fuel moisture content (FMC) is an important parameter in forest fire modeling. We investigated the performance of genetic algorithms with partial least squares (GA-PLS) modeling to retrieve live FMC and its components, equivalent water thickness (EWT) and dry matter content (DM), from fresh leaf reflectance in the leaf optical properties experiment dataset. The results show that GA-PLS achieved a good estimation of FMC directly (R²=0.878-0.893) or indirectly (R ²=0.815-0.862) through the joint retrieval of EWT and DM; future work is required to assess the effectiveness of GA-PLS when applied to datasets that consist of low FMC values     

Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing

Large-scale farming of agricultural crops requires on-time detection of diseases for pest management. Hyperspectral remote sensing data taken from low-altitude flights usually have high spectral and spatial resolutions, which can be very useful in detecting stress in green vegetation. In this study, we used late blight in tomatoes to illustrate the capability of applying hyperspectral remote sensing to monitor crop disease in the field scale and to develop the methodologies for the purpose. A series of field experiments was conducted to collect the canopy spectral reflectance of tomato plants in a diseased tomato field in Salinas Valley of California. The disease severity varied from stage 1 (the light symptom), to stage 4 (the sever damage). The economic damage of the crop caused by the disease is around the disease stage 3. An airborne visible infrared imaging spectrometer (AVIRIS) image with 224 bands within the wavelength range of 0.4–2.5 μm was acquired during the growing season when the field data were collected. The spectral reflectance of the field samples indicated that the near infrared (NIR) region, especially 0.7–1.3 μm, was much more valuable than the visible range to detect crop disease. The difference of spectral reflectance in visible range between health plants and the infected ones at stage 3 was only 1.19%, while the difference in the NIR region was high, 10%. We developed an approach including the minimum noise fraction (MNF) transformation, multi-dimensional visualization, pure pixels endmember selection and spectral angle mapping (SAM) to process the hyperspectral image for identification of diseased tomato plants. The results of MNF transformation indicated that the first 28 eigenimages contain useful information for classification of the pixels and the rest were mainly noise-dominated due to their low eigenvalues that had few signals. Therefore, the 28 signal eigenimages were used to generate a multi-dimensional visualization space for endmember spectra selection and SAM. Classification with the SAM technique of plants’ spectra showed that the late blight diseased tomatoes at stage 3 or above could be separated from the healthy plants while the less infected plants (at stage 1 or 2) were difficult to separate from the healthy plants. The results of the image analysis were consistent with the field spectra. The mapped disease distribution at stage 3 or above from the image showed an accurate conformation of late blight occurrence in the field. This result not only confirmed the capability of hyperspectral remote sensing in detecting crop disease for precision disease management in the real world, but also demonstrated that the spectra-based classification approach is an applicable method to crop disease identification.     

The Effect of Submerged Aquatic Vegetation Expansion on a Declining Turbidity Trend in the Sacramento-San Joaquin River Delta

Submerged aquatic vegetation (SAV) has well-documented effects on water clarity. SAV beds can slow water movement and reduce bed shear stress, promoting sedimentation and reducing suspension. However, estuaries have multiple controls on turbidity that make it difficult to determine the effect of SAV on water clarity. In this study, we investigated the effect of primarily invasive SAV expansion on a concomitant decline in turbidity in the Sacramento-San Joaquin River Delta. The objective of this study was to separate the effects of decreasing sediment supply from the watershed from increasing SAV cover to determine the effect of SAV on the declining turbidity trend. SAV cover was determined by airborne hyperspectral remote sensing and turbidity data from long-term monitoring records. The turbidity trends were corrected for the declining sediment supply using suspended-sediment concentration data from a station immediately upstream of the Delta. We found a significant negative trend in turbidity from 1975 to 2008, and when we removed the sediment supply signal from the trend it was still significant and negative, indicating that a factor other than sediment supply was responsible for part of the turbidity decline. Turbidity monitoring stations with high rates of SAV expansion had steeper and more significant turbidity trends than those with low SAV cover. Our findings suggest that SAV is an important (but not sole) factor in the turbidity decline, and we estimate that 21–70 % of the total declining turbidity trend is due to SAV expansion.