Guizhou Wang

Towards an operational method for land surface temperature retrieval from Landsat 8 data

ABSTRACT Land surface temperature (LST) is a key parameter in the physics of land surfaces through the processes of energy and water exchange with the atmosphere. For Landsat data with only one thermal infrared channel (Landsat 4 to Landsat 7), LST cannot actually be retrieved, and external data sources, such as meteorological observations or Moderate Resolution Imaging Spectroradiometer (MODIS) data, are needed to obtain the water vapour content parameter (an important input parameter for the LST retrieval algorithm); this results in limitations on deriving LST. However, the band designations of the Landsat 8 sensors enable the derivation of LST from the Landsat 8 data. This article demonstrates an LST retrieval methodology that makes use of only Landsat 8 image data. In this methodology, the split-window covariance-variance ratio (SWCVR) technique is introduced to derive water vapour content from Landsat 8. A comparison between the retrieved LST and the in situ LST measurements shows good accuracy, with a root mean squared error (RMSE) of 0.83 K. The fact that the proposed LST estimation method utilizing solely Landsat 8 image data does not rely on any external data is a significant advantage for the development of an operational Landsat 8 LST product generating system.

A method of spatial mapping and reclassification for high-spatial-resolution remote sensing image classification.

This paper presents a new classification method for high-spatial-resolution remote sensing images based on a strategic mechanism of spatial mapping and reclassification. The proposed method includes four steps. First, the multispectral image is classified by a traditional pixel-based classification method (support vector machine). Second, the panchromatic image is subdivided by watershed segmentation. Third, the pixel-based multispectral image classification result is mapped to the panchromatic segmentation result based on a spatial mapping mechanism and the area dominant principle. During the mapping process, an area proportion threshold is set, and the regional property is defined as unclassified if the maximum area proportion does not surpass the threshold. Finally, unclassified regions are reclassified based on spectral information using the minimum distance to mean algorithm. Experimental results show that the classification method for high-spatial-resolution remote sensing images based on the spatial mapping mechanism and reclassification strategy can make use of both panchromatic and multispectral information, integrate the pixel- and object-based classification methods, and improve classification accuracy.

An enhanced single‐channel algorithm for retrieving land surface temperature from Landsat series data

Land surface temperature (LST) is a critical parameter in the physics of earth surface processes and is required for many applications related to ecology and environment. Landsat series satellites have provided more than 30 years of thermal information at medium spatial resolution. This paper proposes an enhanced single-channel algorithm (SCen) for retrieving LST from Landsat series data (Landsat 4 to Landsat 8). The SCen algorithm includes three atmospheric functions (AFs), and the latitude and acquisition month of Landsat image were added to the AF models to improve LST retrieval. Performance of the SCen algorithm was assessed with both simulated and in situ data, and accuracy of three single-channel algorithms (including the mono-window algorithm developed by Qin et al. [Z-h Qin et al., 2001a], SCQin, and the generalized single-channel algorithm developed by Jimenez-Munoz and Sobrino [Jimenez-Munoz and Sobrino, 2003], SCJ&S) were compared. The accuracy assessments with simulated data had root mean square deviations (RMSDs) for the SCen, SCJ&S and SCQin algorithms of 1.363 K, 1.858 K and 2.509 K, respectively. Validation with in situ data showed RMSDs for the SCen and SCJ&S algorithms of 1.04 K and 1.49 K, respectively. It was concluded that the SCen algorithm is very operational, has good precision, and can be used to develop an LST product for Landsat series data.

Characterizing Light Pollution Trends across Protected Areas in China Using Nighttime Light Remote Sensing Data

Protected areas (PAs) with natural, ecological, and cultural value play important roles related to biological processes, biodiversity, and ecosystem services. Over the past four decades, the spatial range and intensity of light pollution in China has experienced an unprecedented increase. Few studies have been documented on the light pollution across PAs in China, especially in regions that provide a greater amount of important biodiversity conservation. Here, nighttime light satellite images from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) were selected to characterize light pollution trends across PAs using nighttime light indexes and hot spot analysis, and then the light pollution changes in PAs were classified. Furthermore, the causes of light pollution changes in PAs were determined using high-resolution satellite images and statistical data. The results showed the following: (1) Approximately 57.30% of PAs had an increasing trend from 1992 to 2012, and these PAs were mainly located in the eastern region, the central region, and a small part of the western region of China. Hot spot analysis showed that the patterns of change for the total night light and night light mean had spatial agglomeration characteristics; (2) The PAs affected by light pollution changes were divided into eight classes, of which PAs with stable trends accounted for 41%, and PAs with high increasing trends accounted for 10%. PAs that had high increasing trends with low density accounted for the smallest amount, i.e., only 1%; (3) The factors influencing light pollution changes in PAs included the distance to urban areas, mineral exploitation, and tourism development and the migration of residents. Finally, based on the status of light pollution encroachment into PAs, strategies to control light pollution and enhance the sustainable development of PAs are recommended.

NDVI-based split-window algorithm for precipitable water vapour retrieval from Landsat-8 TIRS data over land area

An algorithm for the retrieval of precipitable water vapour (PWV) from Landsat-8 thermal infrared sensor (TIRS) data over land area has been developed in this paper. This method is based on the split-window covariance-variance ratio (SWCVR) theory and introduces normalized difference vegetation index (NDVI) to improve PWV retrieval from the relatively high-resolution thermal infrared data. Validation of the method is performed with meteorological data and Moderate Resolution Imaging Spectroradiometer (MODIS) total column PWV product (MOD05), and comparisons between NDVI-based SWCVR method and previous SWCVR method are employed. The root mean square error (RMSE) between PWV retrieved and that provided by meteorological data is 0.39 and 0.57 g cm−2 respectively for the proposed and previous method. The RMSE is 0.55 and 0.69 g cm−2 respectively for the proposed and previous method as validated with MOD05. It is concluded that the proposed method for retrieval of PWV from Landsat-8 TIRS data attained a better accuracy than the previous SWCVR method. PWV obtained from the proposed method is of great value as an input for land surface temperature retrieval and atmospheric correction for the Landsat-8 data.

A coupled atmospheric and topographic correction algorithm for remotely sensed satellite imagery over mountainous terrain

Radiometric correction is an important issue in the quantitative remote-sensing community. By integrating dark object subtraction (DOS)-based atmospheric correction with physics-based topographic correction, a coupled land surface reflectance retrieval algorithm (coupled atmospheric and topographic correction algorithm, named the CAT algorithm) for rugged mountainous regions is proposed. Terra MODIS-derived atmospheric characterization data (including aerosol optical depth, integrated precipitable water, surface pressure, and ozone concentration) are employed as inputs for the proposed algorithm. A physics-based path radiance estimation model is proposed and embedded in the CAT algorithm, and band-specific per-pixel path radiance values are calculated. After the CAT algorithm was performed, the correlation between reflectance and terrain was dramatically reduced, with correlation coefficients nearly equal zero, especially for the near infrared and short-wave infrared bands, meanwhile the image information content increased over 20%. To provide a comparison with previous studies, two commonly used methods in the literature (DOS + Cosine and DOS + C) were employed. The results of the comparison show that the proposed algorithm performed better in both atmospheric and topographic corrections without empirical regression.

Sequential pattern mining of land cover dynamics based on time-series remote sensing images

Remote sensing images constitute a new type of multimedia data well suited to land cover change detection tasks, as they can repetitively provide information about the land surface and its changes over large and inaccessible areas. With plans for more missions and higher resolution earth observation systems, the challenge is increasingly going to be the efficient usability of the millions of collected images, especially the decades of remote sensing image time series, to describe land cover and/or scene evolution and dynamics. In contrast to traditional land cover change measures using pair-wise comparisons that emphasize the compositional or configurational changes between dates, this research focuses on the analysis of the temporal sequence of land cover dynamics, which refers to the succession of land cover types for a given area over more than two observational periods. The expected novel significance of this study is the generalization of the application of the sequential pattern mining method for capturing the spatial variability of landscape patterns and their trajectories of change to reveal information regarding process regularities with satellite imagery. Experimental results showed that this approach not only quantifies land cover changes in terms of the percentage area affected and maps the spatial distribution of these land cover changes but also reveals possibly interesting or useful information regarding the trajectories of change. This method is a valuable complement to existing bi-temporal change detection methods.

Validation of the generalized single-channel algorithm using Landsat 8 imagery and SURFRAD ground measurements

ABSTRACT Land surface temperature (LST) is a key parameter in land surface physics and depends on energy and water exchange processes with the atmosphere. The generalized single-channel (GSC) algorithm is an operational approach for retrieving LST from satellite sensors with only one thermal infrared channel. Until now, validations of the GSC algorithm using in situ data were limited. In this study, Landsat 8 imagery and SURFace RADiation budget network (SURFRAD) ground observations were employed to test the accuracy of the GSC algorithm. The validation results show that the GSC algorithm can obtain high LST retrieval accuracy when the total atmospheric water vapour content is between 0.5 and 3 g cm−2, with coefficient of determination (R2) between the retrieved LST and the ground LST greater than 0.97 and mean absolute error (MAE) and root mean square error (RMSE) values of 1.57 K and 1.96 K, respectively.

An Integrated Method to Generate a Cloud-Free Image Automatically Based on Landsat5 Data

How to remove cloud and shadow completely through several (more than 2) images is considered in this paper. Based on landsat5 data, when enter several images, it can automatically find one image with the least cloud and shadow as the base image. Then mosaic with other images which are taken in different time for the same area, an image without any cloud and shadow is produced.

Potentiality of Using Luojia 1-01 Nighttime Light Imagery to Investigate Artificial Light Pollution

The successful launch of Luojia 1-01 complements the existing nighttime light data with a high spatial resolution of 130 m. This paper is the first study to assess the potential of using Luojia 1-01 nighttime light imagery for investigating artificial light pollution. Eight Luojia 1-01 images were selected to conduct geometric correction. Then, the ability of Luojia 1-01 to detect artificial light pollution was assessed from three aspects, including the comparison between Luojia 1-01 and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the source of artificial light pollution and the patterns of urban light pollution. Moreover, the advantages and limitations of Luojia 1-01 were discussed. The results showed the following: (1) Luojia 1-01 can detect a higher dynamic range and capture the finer spatial details of artificial nighttime light. (2) The averages of the artificial light brightness were different between various land use types. The brightness of the artificial light pollution of airports, streets, and commercial services is high, while dark areas include farmland and rivers. (3) The light pollution patterns of four cities decreased away from the urban core and the total light pollution is highly related to the economic development. Our findings confirm that Luojia 1-01 can be effectively used to investigate artificial light pollution. Some limitations of Luojia 1-01, including its spectral range, radiometric calibration and the effects of clouds and moonlight, should be researched in future studies.