a b s t r a c tAs a proxy measure of the human ecological footprint, impervious surface area (ISA) has recently become a key concept in the field of urban remote sensing, with a focus on estimation of the ISA at a city-scale by using Landsat-style satellite images. However, ISA estimation is also in demand in disciplines such as the environmental assessment and policy making at a national scale. This paper proposes a new method for estimating the ISA fraction in Japan based on a temporal mixture analysis (TMA) technique. The required inputs for the proposed method are rearranged MODIS NDVI time-series datasets at the temporal stable zone (i.e., the first to the sixth largest NDVI values in a year). Three ISA distribution maps obtained from Landsat-5 TM data were used as reference maps to evaluate the performance of the proposed method. The results showed that the proposed TMA-based method achieved a large reduction in the effects of endmember variability compared with the previous methods (e.g., SMA and NSMA), and thus the new method has promising accuracy for estimating ISA in Japan. The overall root mean square error (RMSE) of the proposed method was 8.7%, with a coefficient of determination of 0.86, and there was no obvious underestimation or overestimation for the whole ISA range. Ó

a b s t r a c tThe impervious surface area (ISA) has emerged not only as an indicator of the degree of urbanization, but also as a major indicator of environmental quality for drainage basin management. However, since almost all of the methods for estimating ISA have been developed for urban environments, it is questionable whether these methods can be successfully applied to drainage basins, such as those found in Japan, which usually have more complicated vegetation components (e.g. paddy field, plowed field and dense forest). This paper presents a pre-screened and normalized multiple endmember spectral mixture analysis (PNMESMA) method, which includes a new endmember selection strategy and an integration of the normalized spectral mixture analysis (NSMA) and multiple endmember spectral mixture analysis (MESMA), for estimating the ISA fraction in Lake Kasumigaura Basin, Japan. This new proposed method is superior to the previous methods in that the estimation error of the proposed method is much smaller than the previous SMA-or NSMA-based methods for drainage basin environments. The overall root mean square error was reduced to 5.2%, and no obvious underestimation or overestimation occurred for high or low ISA areas. Through the assessment of environmental quality in Lake Kasumigaura Basin using the ISA fraction, the results showed that the basin has been in the impacted category since 1987, and that in the two decades since, the environmental quality has continued to decline. If this decline continues, then Lake Kasumigaura Basin will fall into the degraded category by 2017.

Since the combinations of water constituents are different between Case-1 and Case-2 waters, bio-optical models, retrieval algorithms for water constituent concentrations and other applications in watercolor remote sensing are also very dissimilar between these waters. Use of the algorithms specifically developed for Case-1 waters returns inaccurate results in Case-2 waters, and vice versa. To select an appropriate algorithm for a given water pixel, it is important to first determine whether it is a Case-1 or Case-2 water and to clarify its temporal variations. This paper presents a simple method based on the inherent optical properties (IOPs) of water bodies for discriminating global Case-1 and Case-2 waters based on satellite data. Compared with the previous methods, the newly proposed method only requires two remote-sensing reflectances at 412 nm and 443 nm for relative comparisons, and thus it not only can easily be implemented using satellite data but also is robust even for satellite data with imperfect atmospheric correction, unpredictable noise pixels in the images, and so on. The new method was then applied to seasonal SeaWiFS 9-km data to map the global distribution of Case-1 and Case-2 waters for each season in 2003. The results showed that more than 80% of global waters belong to the Case-1 category throughout the year, and the Case-2 waters are mainly concentrated in the Northern Hemisphere along the coasts. Both the area and distribution of Case-1 and Case-2 waters changed seasonally. By using a sub-dataset from NOMAD, it was found that when the ratio of [a ph (443)+a w (443)]/a(443) was larger (smaller) than 50%, about 70% (75%) of the samples were identified as Case-1 (Case-2) waters by the new method. Moreover, the semi-analytical algorithm GSM01 was more accurate for distinguishing Case-1 than Case-2 waters, which implies that use of the proposed method to select the appropriate remote-sensing 3 algorithm would be important.

Due to increasing global urbanization and climate change, the quantification of "human footprints" has become an urgent goal in the fields of biodiversity conservation and regional environment management. A human footprint is defined as the impact of a particular human activity on the Earth's surface, which can be represented mainly by impervious surfaces (related to industry and urbanization) and cropland (related to agriculture). Here we present a method called sorted temporal mixture analysis with post-classification (STMAP) for mapping impervious surfaces and cropland simultaneously at the subpixel level to fill the demand for precise human footprint information on a national scale. The STMAP method applies a fourendmember sorted temporal mixture analysis to provide the initial fractions of evergreen forests, deciduous forests, cropland, and impervious surfaces as a first step. Endmembers are selected from the sorted temporal profiles of the MODIS-normalized difference vegetation index (NDVI), as guided by a principal component analysis. The yearly maximum land surface temperatures and averaged stable nighttime light are then statistically analyzed to provide the thresholds for post-classification to further separate cropland from deciduous forest and bare land from impervious surface. As the four outputs of STMAP, the fractions of forest, cropland, impervious surfaces and bare land are derived. We used the reference maps of impervious surfaces and cropland obtained from the Landsat/TM and ALOS precise landuse/land-cover map at the subpixel level to evaluate the performance of the proposed method, respectively. Historical satellite images with high spatial resolution were used to further evaluate the cropland results derived with the STMAP method. The results showed that the STMAP method has promising accuracy for estimating impervious surfaces and cropland in Japan. The root mean square errors obtained with the STMAP method were 6.3% for the estimation of impervious surfaces and 9.8% for the estimation of cropland. Our findings can extend the applications of remote sensing technologies in ecological research and environment management on a large scale.

Light-to-Camera Communications (LCC) have emerged as a new wireless communication technology with great potential to benefit a broad range of applications. However, the existing LCC systems either require cameras directly facing to the lights or can only communicate over a single link, resulting in low throughputs and being fragile to ambient illuminant interference. We present HYCACO, a novel LCC system, which enables multiple light emitting diodes (LEDs) with an unaltered camera to communicate via the non-line-of-sight (NLoS) links. Different from other NLoS LCC systems, the proposed scheme is resilient to the complex indoor luminous environment. HYCACO can decode the messages by exploring the mixed reflected optical signals transmitted from multiple LEDs. By further exploiting the rolling shutter mechanism, we present the optimal optical frequencies and camera exposure duration selection strategy to achieve the best performance. We built a hardware prototype to demonstrate the efficiency of the proposed scheme under different application scenarios. The experimental results show that the system throughput reaches 4.5 kbps on iPhone 6s with three transmitters. With the robustness, improved system throughput and ease of use, HYCACO has great potentials to be used in a wide range of applications such as advertising, tagging objects, and device certifications.