Qin Ma

SRTM DEM Correction in Vegetated Mountain Areas through the Integration of Spaceborne LiDAR, Airborne LiDAR, and Optical Imagery

The Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is one of the most complete and frequently used global-scale DEM products in various applications. However, previous studies have shown that the SRTM DEM is systematically higher than the actual land surface in vegetated mountain areas. The objective of this study is to propose a procedure to calibrate the SRTM DEM over large vegetated mountain areas. Firstly, we developed methods to estimate canopy cover from aerial imagery and tree height from multi-source datasets (i.e., field observations, airborne light detection and ranging (LiDAR) data, Geoscience Laser Altimeter System (GLAS) data, Landsat TM imagery, climate surfaces, and topographic data). Then, the airborne LiDAR derived DEM, covering ~5% of the study area, was used to evaluate the accuracy of the SRTM DEM. Finally, a regression model of the SRTM DEM error depending on tree height, canopy cover, and terrain slope was developed to calibrate the SRTM DEM. Our results show that the proposed procedure can significantly improve the accuracy of the SRTM DEM over vegetated mountain areas. The mean difference between the SRTM DEM and the LiDAR DEM decreased from 12.15 m to −0.82 m, and the standard deviation dropped by 2 m.

Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery

ABSTRACT Forests of the Sierra Nevada (SN) mountain range are valuable natural heritages for the region and the country, and tree height is an important forest structure parameter for understanding the SN forest ecosystem. There is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial resolution. In this study, we presented a method to map wall-to-wall forest tree height (defined as Lorey’s height) across the SN at 70-m resolution by fusing multi-source datasets, including over 1600 in situ tree height measurements and over 1600 km2 airborne light detection and ranging (LiDAR) data. Accurate tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements, and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System (GLAS) footprints. Finally, the random forest algorithm was used to model the SN tree height from these GLAS tree heights, optical imagery, topographic data, and climate data. The results show that our fine-resolution SN tree height product has a good correspondence with field measurements. The coefficient of determination between them is 0.60, and the root-mean-squared error is 5.45 m.

Analysis of urbanization dynamics in mainland China using pixel-based night-time light trajectories from 1992 to 2013

ABSTRACT Understanding urbanization dynamics, or how intensity of urbanization changes over time, is an important basis for urban planning and management, which has been investigated using various data-driven approaches. Considering the advantages and constraints of different data sources, we use pixel-based, time-series night-time light (NTL) trajectories to characterize urbanization dynamics in mainland China where massive urban development has been occurring in recent decades. After pre-processing the data, we extracted time-series NTL trajectories for each 1 km × 1 km pixel between 1992 and 2013 and used the unsupervised k-means classification to identify the major typologies of these trajectories as urbanization dynamics based on their main statistical parameters. The classification identified five urbanization dynamics, namely, stable urban activity, high-level steady growth, acceleration, low-level steady growth, and fluctuation. Their distributions and spatial patterns were further summarized and compared among different Chinese administrative divisions. We specifically analysed the acceleration trajectories that showed rapid transitions from rural to urban, as we considered these trajectories as potential indicators for aggressive urbanization. We found several clusters at prefecture city and county levels with high proportion of the acceleration, and referred to the underlying socioeconomic characteristics and developmental history to understand how these clusters could had been formed. Through this study, we revealed the dominant tendencies of urbanization in China over space and time, and developed an analysis framework that could be extended to other regions.

Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data: a case study in the Sierra Nevada Mountains, California

ABSTRACT Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth modeling. Airborne Laser Scanning (ALS) can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy terrains. This study proposed an ALS-based framework to quantify tree growth and competition. Bi-temporal ALS data were used to quantify tree growth in height (ΔH), crown area (ΔA), crown volume (ΔV), and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada forests. We analyzed the correlations between tree growth attributes and controlling factors (i.e. tree sizes, competition, forest structure, and topographic parameters) at multiple levels. At the individual tree level, ΔH had no consistent correlations with controlling factors, ΔA and ΔV were positively related to original tree sizes (R > 0.3) and negatively related to competition indices (R < −0.3). At the forest-stand level, ΔH and ΔA were highly correlated to topographic wetness index (|R| > 0.7), ΔV was positively related to original tree sizes (|R| > 0.8). Multivariate regression models were simulated at individual tree level for ΔH, ΔA, and ΔV with the R2 ranged from 0.1 to 0.43. The ALS-based tree height estimation and growth analysis results were consistent with field measurements.

Emerging Stress and Relative Resiliency of Giant Sequoia Groves Experiencing Multiyear Dry Periods in a Warming Climate

The relative greenness and wetness of Giant Sequoia (Sequoiadendron giganteum, SEGI) groves and the surrounding Sierra Nevada, California forests were investigated using patterns in vegetation indices from Landsat imagery for the period 1985-2015. Vegetation greenness (normalized difference vegetation index) and thus forest biomass in groves increased by about 6% over that 30-year period, suggesting a 10% increase in evapotranspiration. No significant change in the surrounding non-grove forest was observed. In this period, local temperature measurements showed an increase of about 2.2 °C. The wetness of groves (normalized difference wetness index) showed no overall long-term trend, but responded to changes in annual water-year precipitation and temperature. The long-term trends of grove greenness and wetness were elevation dependent, with the lower rain-snow transition elevation zone (1700 – 2100 m) marking a change from an increasing trend at lower elevations to a decreasing trend at higher elevations. The 2011-2015 drought brought an unprecedented drop in grove wetness, over five times the 1985-2010 standard deviation; and wetness in SEGI groves dropped 50% more than in non-grove areas. Overall, the wetness and greenness of SEGI groves showed a larger response to the warming climate and drought than non-grove areas. The influence of droughts on the wetness of SEGI groves reflected effects of both the multi-decadal increase in forest biomass and the effects of warmer drought-year temperatures on the evaporative demand of current grove vegetation, plus sufficient regolith water storage of rain and snowmelt to sustain that vegetation through seasonal and multi-year dry periods.

AN IMPROVED SAR RADIOMETRIC TERRAIN CORRECTION METHOD AND ITS APPLICATION IN POLARIMETRIC SAR TERRAIN EFFECT REDUCTION

A new SAR radiometric terrain correction method was proposed to reduce the terrain efiects in sloped regions. Based on this method, a procedure for polarimetric SAR terrain efiect reduction was proposed, including geometric correction, shadow detection, radiometric terrain correction, and polarization orientation angle shift compensation. Experiments using RADARSAT-2 polarimetric SAR data of the Three Gorges Area, China demonstrated the efiectiveness of the proposed radiometric terrain correction method. Both visual and quantitative analyses showed that after the proposed radiometric terrain correction method was applied, the contrast between difierent slopes that caused by local incidence angle difierences, foreshortening, and layover was signiflcantly reduced. The difierence of backscattering intensity on slopes facing the radar sensor and facing away from the sensor was reduced from 12.5dB before radiometric correction to 1.3dB. The overall accuracy of land use/land cover classiflcation was improved by 11.2 percent using the terrain corrected polarimetric SAR data.