Mapping land-use and land-cover changes through the integration of satellite and airborne remote sensing data.

An integrated, remotely sensed approach to assess land-use and land-cover change (LULCC) dynamics plays an important role in environmental monitoring, management, and policy development. In this study, we utilized the advantage of land-cover seasonality, canopy height, and spectral characteristics to develop a phenology-based classification model (PCM) for mapping the annual LULCC in our study areas. Monthly analysis of normalized difference vegetation index (NDVI) and near-infrared (NIR) values derived from SPOT images enabled the detection of temporal characteristics of each land type, serving as crucial indices for land type classification. The integration of normalized difference built-up index (NDBI) derived from Landsat images and airborne LiDAR canopy height into the PCM resulted in an overall performance of 0.85, slightly surpassing that of random forest analysis or principal component analysis. The development of PCM can reduce the time and effort required for manual classification and capture annual LULCC changes among five major land types: forests, built-up land, inland water, agriculture land, and grassland/shrubs. The gross change LULCC analysis for the Taoyuan Tableland demonstrated fluctuations in land types over the study period (2013 to 2022). A negative correlation (r = - 0.79) in area changes between grassland/shrubs and agricultural land and a positive correlation (r = 0.47) between irrigation ponds and agricultural land were found. Event-based LULCC analysis for Taipei City demonstrated a balance between urbanization and urban greening, with the number of urbanization events becoming comparable to urban greening events when the spatial extent of LULCC events exceeds 1000 m2. Besides, small-scale urban greening events are frequently discovered and distributed throughout the metropolitan area of Taipei City, emphasizing the localized nature of urban greening events.

The application of a high-density street-level air temperature observation network (HiSAN): The relationship between air temperature, urban development, and geographic features.

The urban heat island effect in cities has become an important problem in relation to not only urban climate but also public health and urban planning. Tainan, which located in Southern Taiwan, is a compact city with intense development. Therefore, this study investigated the urban thermal condition by employing a high-density street-level air temperature observation network (HiSAN). A total of 100 measurement points were set in various urban development areas. The geographic factors in Tainan can be used for indicating the relationship between thermal conditions and urban built environments to comprehensively compare the approaches, such as conducting traverse measurement and utilizing only a single datum or a small amount of weather station data. Buffer zone analysis was used in this study for zones of different sizes, and it was determined that a 300-m scale is optimal to illustrate the effects of land features on microclimate. The results revealed that the thermal condition in Tainan is influenced by urban development factors, such as the floor area and land cover area, and by geographic factors, such as the distance to the sea. A better cooling effect can be obtained from a vegetation area during the night time and from a water body during the daytime. Moreover, different cooling effects are observed based on the distance to the sea. Through these results, a model for predicting the thermal condition for different periods can be established using a multiple regression model. Urban planners and architects can proffer design and planning suggestions for different areas based on the findings of this study to reduce thermal stress in urban areas.

Identifying the relationships between water quality and land cover changes in the Tseng-Wen reservoir watershed of Taiwan.

The effects on water quality of land use and land cover changes, which are associated with human activities and natural factors, are poorly identified. Fine resolution satellite imagery provides opportunities for land cover monitoring and assessment. The multiple satellite images after typhoon events collected from 2001 to 2010 covering land areas and land cover conditions are evaluated by the Normalized Difference Vegetation Index (NDVI). The relationship between land cover and observed water quality, such as suspended solids (SS) and nitrate-nitrogens (NO(3)-N), are explored in the study area. Results show that the long-term variations in water quality are explained by NDVI data in the reservoir buffer zones. Suspended solid and nitrate concentrations are related to average NDVI values on multiple spatial scales. Annual NO(3)-N concentrations are positively correlated with an average NDVI with a 1 km reservoir buffer area, and the SS after typhoon events associated with landslides are negatively correlated with the average NDVI in the entire watershed. This study provides an approach for assessing the influences of land cover on variations in water quality.

A Monte Carlo study of the seagrass-induced depth bias in bathymetric lidar.

A bathymetric lidar survey is the most cost efficient method of producing bathymetric maps in near shore areas where the ocean bottom is both highly variable and of greatest importance for shipping and recreation. So far, not much attention has been paid to the influence of bottom materials on the lidar signals. This study addresses this issue using a Monte Carlo modeling technique. The Monte Carlo simulation includes a plane parallel water body and a flat bottom with or without seagrass. The seagrass canopy structure is adopted from Zimmerman (2003). Both the surface of the seagrass leaves and the bottom are assumed to be Lambertian. Convolution with the lidar pulse function followed by the median operator is used to reduce the variance of the resultant lidar waveform. Two seagrass orientation arrangements are modeled: seagrass in still water with random leaf orientation and seagrass with a uniform orientation as would be expected when under the influence of a water current. For each case, two maximum canopy heights, 0.5 m and 1 m, three shoot densities, 100, 500, and 1000, and three bending angles, 5, 25, and 45 degrees, are considered. The seagrass is found to induce a depth bias that is proportional to an effective leaf area index (eLAI) and the contrast in reflectance between the seagrass and the bottom material.

Potential of ILRIS3D Intensity Data for Planar Surfaces Segmentation.

Intensity value based point cloud segmentation has received less attention because the intensity value of the terrestrial laser scanner is usually altered by receiving optics/hardware or the internal propriety software, which is unavailable to the end user. We offer a solution by assuming the terrestrial laser scanners are stable and the behavior of the intensity value can be characterized. Then, it is possible to use the intensity value for segmentation by observing its behavior, i.e., intensity value variation, pattern and presence of location of intensity values, etc. In this study, experiment results for characterizing the intensity data of planar surfaces collected by ILRIS3D, a terrestrial laser scanner, are reported. Two intensity formats, grey and raw, are employed by ILRIS3D. It is found from the experiment results that the grey intensity has less variation; hence it is preferable for point cloud segmentation. A warm-up time of approximate 1.5 hours is suggested for more stable intensity data. A segmentation method based on the visual cues of the intensity images sequence, which contains consecutive intensity images, is proposed in order to segment the 3D laser points of ILRIS3D. This method is unique to ILRIS3D data and does not require radiometric calibration.

Potential for application of an acoustic camera in particle tracking velocimetry.

We explored the potential and limitations for applying an acoustic camera as the imaging instrument of particle tracking velocimetry. The strength of the acoustic camera is its usability in low-visibility environments where conventional optical cameras are ineffective, while its applicability is limited by lower temporal and spatial resolutions. We conducted a series of experiments in which acoustic and optical cameras were used to simultaneously image the rotational motion of tracer particles, allowing for a comparison of the acoustic- and optical-based velocities. The results reveal that the greater fluctuations associated with the acoustic-based velocities are primarily attributed to the lower temporal resolution. The positive and negative biases induced by the lower spatial resolution are balanced, with the positive ones greater in magnitude but the negative ones greater in quantity. These biases reduce with the increase in the mean particle velocity and approach minimum as the mean velocity exceeds the threshold value that can be sensed by the acoustic camera.