Transit-Oriented Development (TOD) is an urban planning approach that encourages a modal shift from private to public transportation. This shift can generate additional benefits from a sustainability perspective. This study aims to assess the efficiency of TOD by applying the data envelopment analysis (DEA) method. The ridership of public transportation is considered as the direct output characteristic of TOD efficiency, and nine indicators of ridership are selected as inputs on the basis of the core concepts of TOD. These concepts include density, diversity, and design (3Ds). The Tokyu Den-en Toshi Line in Japan is presented as a typical case of TOD because this line includes TOD and non-TOD stations. Assessing and comparing the results of all railway stations reveal that almost all indicator values of non-TOD stations are higher than those of TOD stations. The results suggest that TOD planning and programs are inefficient in terms of ridership generation. This implication, however, may be attributed to the inadequacy of the selected indicators for TOD assessment. The results obtained after adding operation year as input indicators and removing transfer station show that TOD stations perform efficiently and in accordance with expectations. This response indicates that the inclusion of influential factors is necessary for equitable TOD assessment. Therefore, other influential factors must be considered when evaluating the efficiency of TOD-based stations with different inherent attributes. In addition, the design input with the largest impact on all inefficient units was identified, suggesting that management of bus service and railway system should be well enhanced.

Remote sensing is a valuable way to retrieve spatially continuous information on vegetation phenological changes, which are widely used as an indicator of climate change. We propose a simple method called weighted cross-correlogram spectral matching—phenology (CCSM-P), which combines CCSM and a weighted correlation system, for detecting vegetation phenological changes by using multiyear vegetation index (VI) time series. In experiments with simulated enhanced VI (EVI) for various scenarios, CCSM-P exhibited high accuracy and robustness to noise and the potential to capture long-term phenological change trends. For a temperate grassland in northern China, CCSM-P retrieved more reasonable vegetation spring phenology from Moderate Resolution Imaging Spectroradiometer (MODIS) EVI images than the MODIS phenology product (MCD12Q2). When validated against field phenological observations in five of the AmeriFlux Network sites in the U.S. (four deciduous broadleaf forest sites and a closed shrublands site), and a cropland site in China, CCSM-P exhibited mean absolute differences (MADs) ranging from 2 to 10 days (median: 4.2 days), whereas MAD of non-CCSM methods showed larger variations, ranging from 5 to 58 days (median: 21.3 days). This is because CCSM-P integrates field phenological observations. Compared with non-CCSM methods, which are widely used to identify phenological events, CCSM-P is more accurate and less dependent on prior knowledge (thresholds or predefined functions), which indicates its effectiveness and applicability for detecting year-to-year variations and long-term change trends in phenology, and should facilitate more reliable assessments of phenological changes in climate change studies.

Land surface albedo (LSA), one of the Visible Infrared Imaging Radiometer Suite (VIIRS) environmental data records (EDRs), is a fundamental component for linking the land surface and the climate system by regulating shortwave energy exchange between the land and the atmosphere. Currently, the improved bright pixel sub-algorithm (BPSA) is a unique algorithm employed by VIIRS to routinely generate LSA EDR from VIIRS top-of-atmosphere (TOA) observations. As a product validation procedure, LSA EDR reached validated (V1 stage) maturity in December 2014. This study summarizes recent progress in algorithm refinement, and presents comprehensive validation and evaluation results of VIIRS LSA by using extensive field measurements, Moderate Resolution Imaging Spectroradiometer (MODIS) albedo product, and Landsat-retrieved albedo maps. Results indicate that: (1) by testing the updated desert-specific look-up-table (LUT) that uses a stricter standard to select the training data specific for desert aerosol type in our local environment, it is found that the VIIRS LSA retrieval accuracy is improved over a desert surface and the absolute root mean square error (RMSE) is reduced from 0.036 to 0.023, suggesting the potential of the updated desert LUT to the improve the VIIRS LSA product accuracy; (2) LSA retrieval on snow-covered surfaces is more accurate if the newly developed snow-specific LUT (RMSE = 0.082) replaces the generic LUT (RMSE = 0.093) that is employed in the current operational LSA EDR production; (3) VIIRS LSA is also comparable to high-resolution Landsat albedo retrieval (RMSE < 0.04), although Landsat albedo has a slightly higher accuracy, probably owing to higher spatial resolution with less impacts of mixed pixel; (4) VIIRS LSA retrievals agree well with the MODIS albedo product over various land surface types, with overall RMSE of lower than 0.05 and the overall bias as low as 0.025, demonstrating the comparable data quality between VIIRS and the MODIS LSA product.

Fractional vegetation cover (FVC) is one of the fundamental parameters for characterizing terrestrial ecosystems, with wide uses in various environmental and climate-related modeling applications. The remote sensing technique provides a unique opportunity for estimating FVC over large geographical areas by employing spectral mixture analysis (SMA). The effectiveness of SMA depends largely on the accurate extraction of representative and pure endmembers. However, in arid and semiarid environments that have sparse vegetation distributions, most current SMA models may produce large biases due to difficulties in obtaining pure vegetation spectra from the satellite images. This letter developed a new approach to estimate FVC from satellite observations by integrating an endmember spectrum purification procedure into a nonlinear SMA model. The proposed method is capable of extracting pure endmember spectra even though pure vegetation endmember is not present in target images in arid and semiarid environments, which improves the accuracy of FVC retrievals. Validation experiments conducted in the Xilingol grassland, Inner Mongolia, China, demonstrate that the proposed method produces more accurate FVC estimates (RMSE <; 0.13, AD <; 0.06) than do current algorithms. The better performance of the proposed method can be attributed to the purified vegetation spectra that more closely resemble the real pure vegetation spectra.