Guangbin Lei

An Enhanced Spatial and Temporal Data Fusion Model for Fusing Landsat and MODIS Surface Reflectance to Generate High Temporal Landsat-Like Data

Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band (the contrasting average absolute difference [AAD]: 0.0167 vs. 0.0265). The enhanced algorithm will support subsequent research on monitoring land surface dynamic changes at finer scales.

Estimating the Maximal Light Use Efficiency for Different Vegetation through the CASA Model Combined with Time-Series Remote Sensing Data and Ground Measurements

Maximal light use efficiency (LUE) is an important ecological index of a vegetation essential attribute, and a key parameter of the LUE-based model for estimating large-scale vegetation productivity by remote sensing technology. However, although currently used in different models there still exists extensive controversy. This paper takes the Zoige Plateau in China as a case area to develop a new approach for estimating the maximal LUEs for different vegetation. Based on an existing land cover map and MODIS NDVI product, the linear unmixing method with a moving window was adopted to estimate the time-series NDVI for different end members in a MODIS NDVI pixel; then Particle Swarm Optimizer (PSO) was applied to search for the optimization of LUE retrievals through the CASA (Carnegie-Ames-Stanford Approach) model combined with time-series NDVI and ground measurements. The derived maximal LUEs present significant differences among various vegetation types. These are 0.669 gC.MJ(-1), 0.450 gC.MJ(-1) and 0.126 gC.MJ(-1) for the xerophilous grasslands with high, moderate and low vegetation fraction respectively, 0.192 gC.MJ(-1) for the hygrophilous grasslands, and 0.125 gC.MJ(-1) for the helobious grasslands. The field validation shows that the estimated net primary productivity (NPP) by the derived maximal LUE is closely related to the ground references, with R-2 of 0.8698 and root-mean-square error (RMSE) of 59.37 gC.m(-2).a(-1). This indicates that the default set in the CASA model is not suitable for NPP estimation for the regional mountain area. The derived maximal LUEs can significantly improve the capability of NPP mapping, and open up the perspective for long-term monitoring of vegetation ecological health and ecosystem productivity by combining the LUE-based model with remote sensing observations.

Auto-registration and orthorecification algorithm for the time series HJ-1A/B CCD images

How to deal with geometric distortion is an open problem when using the massive amount of satellite images at a national or global scale, especially for multi-temporal image analysis. In this paper, an algorithm is proposed to automatically rectify the geometric distortion of time-series CCD multispectral data of small constellation for environmental and disaster mitigation (HJ-1A/B) which was launched by China in 2008. In this algorithm, the area-based matching method was used to automatically search tie points firstly, and then the polynomial function was introduced to correct the systematic errors caused by the satellite motion along the roll, pitch and yaw direction. The improved orthorectification method was finally used to correct pixel displacement caused by off-nadir viewing of topography, which are random errors in the images and cannot be corrected by the polynomial equation. Nine scenes of level 2 HJ CCD images from one path/row were taken as the warp images to test the algorithm. The test result showed that the overall accuracy of the proposed algorithm was within 2 pixels (the average residuals were 37.8 m, and standard deviations were 19.8 m). The accuracies of 45.96% validation points (VPs) were within 1 pixel and 90.33% VPs were within 2 pixels. The discussion showed that three main factors including the distortion patterns of HJ CCD images, percent of cloud cover and the varying altitude of the satellite orbit may affect the search of tie points and the accuracy of results. Although the influence of varying altitude of the satellite orbits is less than the other factors, it is noted that detailed satellite altitude information should be given in the future to get a more precise result. The proposed algorithm should be an efficient tool for the geo-correction of HJ CCD multi-spectral images.

An Improved Physics-Based Model for Topographic Correction of Landsat TM Images

Optical remotely sensed images in mountainous areas are subject to radiometric distortions induced by topographic effects, which need to be corrected before quantitative applications. Based on Li model and Sandmeier model, this paper proposed an improved physics-based model for the topographic correction of Landsat Thematic Mapper (TM) images. The model employed Normalized Difference Vegetation Index (NDVI) thresholds to approximately divide land targets into eleven groups, due to NDVIs lower sensitivity to topography and its significant role in indicating land cover type. Within each group of terrestrial targets, corresponding MODIS BRDF (Bidirectional Reflectance Distribution Function) products were used to account for land surfaces BRDF effect, and topographic effects are corrected without Lambertian assumption. The methodology was tested with two TM scenes of severely rugged mountain areas acquired under different sun elevation angles. Results demonstrated that reflectance of sun-averted slopes was evidently enhanced, and the overall quality of images was improved with topographic effect being effectively suppressed. Correlation coefficients between Near Infra-Red band reflectance and illumination condition reduced almost to zero, and coefficients of variance also showed some reduction. By comparison with the other two physics-based models (Sandmeier model and Li model), the proposed model showed favorable results on two tested Landsat scenes. With the almost half-century accumulation of Landsat data and the successive launch and operation of Landsat 8, the improved model in this paper can be potentially helpful for the topographic correction of Landsat and Landsat-like data.

Land Cover Mapping in Southwestern China Using the HC-MMK Approach

Land cover mapping in mountainous areas is a notoriously challenging task due to the rugged terrain and high spatial heterogeneity of land surfaces as well as the frequent cloud contamination of satellite imagery. Taking Southwestern China (a typical mountainous region) as an example, this paper established a new HC-MMK approach (Hierarchical Classification based on Multi-source and Multi-temporal data and geo-Knowledge), which was especially designed for land cover mapping in mountainous areas. This approach was taken in order to generate a 30 m-resolution land cover product in Southwestern China in 2010 (hereinafter referred to as CLC-SW2010). The multi-temporal native HJ (HuanJing, small satellite constellation for disaster and environmental monitoring) CCD (Charge-Coupled Device) images, Landsat TM (Thematic Mapper) images and topographical data (including elevation, aspect, slope, etc.) were taken as the main input data sources. Hierarchical classification tree construction and a five-step knowledge-based interactive quality control were the major components of this proposed approach. The CLC-SW2010 product contained six primary categories and 38 secondary categories, which covered about 2.33 million km(2) (accounting for about a quarter of the land area of China). The accuracies of primary and secondary categories for CLC-SW2010 reached 95.09% and 87.14%, respectively, which were assessed independently by a third-party group. This product has so far been used to estimate the terrestrial carbon stocks and assess the quality of the ecological environments. The proposed HC-MMK approach could be used not only in mountainous areas, but also for plains, hills and other regions. Meanwhile, this study could also be used as a reference for other land cover mapping projects over large areas or even the entire globe.

China land cover monitoring in mountainous regions by remote sensing technology — Taking the Southwestern China as a case

Land cover products are the important background for scientific researches. There are several land cover data sets at regional or global scales. However, in the Southwestern China where are the typical mountainous regions, it is usually more difficult to map land cover products because of the high proportion of complex terrain area, conspicuous landscape heterogeneity and difficult image acquisition and preprocessing. Supported by Land Cover Monitoring Project (CLCP) funded by Chinese Academy of Sciences, land cover product of Southwestern China in 2010 was mapped through an object-oriented method combined with the decision tree rules, and the land cover products in 2000 and 2005 were obtained by the change detection. The validation shows that the overall accuracies of the primary and secondary classes reach 95.09% and 90.34% respectively. Taking Sichuan province and Tibet autonomous region as case, the CLCP product analysis indicates that a total of 14,580 km2 and 8,174 km2 area, accounting for 3.04% and 0.68% of the region respectively, had changed in the last 20 years. The main driving forces including climate change, human activities and natural disasters are also discussed.

Multi-temporal cloud and snow detection algorithm for the HJ-1A/B CCD imagery of China

How to accurately detect cloud and snow in the remote sensing imagery is an open problem for the remote sensing application. For only visible and near infrared band in HJ-1A/B CCD images, the cloud detection algorithm using the shortwave infrared and thermal infrared band is restricted by the band-lacking problem. Based on the multi-temporal information of the HJ-1A/B CCD images, a new algorithm is proposed in this paper. Using available images in one month, a cloud-free reference image was firstly composed. Then, the cloud and snow pixel are separated through the difference of the blue band between the reference and each date. Subsequently, the regional covariance matrix is computed further to eliminate the non-cloud pixels. The test result shows that, the overall accuracy is about 85.96% to 93%. It indicates that the proposed method can integrate the temporal and texture information to improve detection accuracy for the cloud and snow.

Validation of MODIS global LAI products in forested terrain

The leaf area index is one of key parameters for ecosystem monitoring, global carbon circulation and climate change. At present leaf area index is routinely available from Earth Observation (EO) instruments such as MODIS. However MODIS-derived estimates of LAI require validation before they are utilised by the ecosystem modelling community. The paper presents a validation of the MODIS LAI collection 5 product over forested terrain in Xishuangbanna, southwest China, based on field measurements which are upscaled using high resolution HJ image. Results suggest that the MODIS LAI product has an accuracy with R2 of 0.35 and RMSE of 0.61 m2/m2, and overestimates values in the high LAI broadleaf forest (where LAI>3.5 m2/m2). Further validation efforts must be carried out over the study area for an assessment of the MODIS LAI in order to improve its accuracy.

Spatio-temporal variation and driving forces in alpine grassland phenology in the Zoigê plateau from 2001–2013

Based on the HANTS and dynamic threshold method, the spatio-temporal changes of the alpine grassland phenology in the Zoigê plateau was analyzed by using MODIS EVI data from 2001 to 2013. The results were found as follows: (1) The spatial distribution of the average vegetation phenology from 2001 to 2013 is closely related to the water and heat conditions. Accompanying the deterioration in heat and water conditions from low altitude to high altitude and south to north, SOG(start of growing season) was delayed gradually, EOG(end of growing season) advanced slowly, and LOG(long of growing season) shortened gradually. Water played an important role in the regional differentiation of phenology (2) From 2001 to 2013, SOG came earlier by 0.6d/a, EOG was late by 0.2d/a, and LOG lengthened by 0.8d/a. The inter-annual phenology changes of the vegetation exhibited significant differences at different elevations and water condition. (3) Heat and moisture is the main ecological factor influencing the growth of plant. Temperature responses of phenology significantly became stronger with increasing cumulative preseason precipitation.

Land Cover Change and Its Driving Forces in Nepal Since 1990

Nepal is a typically mountainous landlocked country. In recent years, land cover in Nepal has changed significantly due to climate changes, population growth and economic development. This study used four periods of land cover change data (1990–2000, 2000–2005, 2005–2010 and 2010–2015) produced by the object-oriented change detection method, to reveal the spatial patterns of land cover change and its driving forces in Nepal since 1990. The result showed that the total change area from 1990 to 2015 in Nepal is 1665.58 km2, accounting for 1.13% of the land. Among these changes, forests, wetlands and permanent ice/snow presented a trend of decrease, whereas croplands, artificial surfaces and bare lands had been continuously increasing. The prominent land cover changes included mutual transformation between wetlands and croplands, wetland class inner shifts and the conversion of forests to croplands. These changes varied among different development regions and during different periods. In general, the intensities of land cover change in Eastern and Central Development Regions were higher than in other development regions. The annual change area was 59.26 km2/year during 1990–2000, reached to 98.44 km2/year during 2000–2005 and turned to slowly decline after 2005. The analysis and discussion indicated that major driving forces of land cover change in Nepal include climate changes, natural hazards, population growth, urbanization, economic development and government policy implementation. A comprehensive understanding of the relationships between land cover change and various driving forces in Nepal was given in this study, which would be valuable for policymaking, land planning, resource development and protection. Simultaneously, it could be also benefit to China’s “Belt and Road Initiative.”