Caixia Liu

Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data

We have produced the first 30 m resolution global land-cover maps using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data. We have classified over 6600 scenes of Landsat TM data after 2006, and over 2300 scenes of Landsat TM and ETM+ data before 2006, all selected from the green season. These images cover most of the worlds land surface except Antarctica and Greenland. Most of these images came from the United States Geological Survey in level L1T (orthorectified). Four classifiers that were freely available were employed, including the conventional maximum likelihood classifier (MLC), J4.8 decision tree classifier, Random Forest (RF) classifier and support vector machine (SVM) classifier. A total of 91,433 training samples were collected by traversing each scene and finding the most representative and homogeneous samples. A total of 38,664 test samples were collected at preset, fixed locations based on a globally systematic unaligned sampling strategy. Two software tools, Global Analyst and Global Mapper developed by extending the functionality of Google Earth, were used in developing the training and test sample databases by referencing the Moderate Resolution Imaging Spectroradiometer enhanced vegetation index (MODIS EVI) time series for 2010 and high resolution images from Google Earth. A unique land-cover classification system was developed that can be crosswalked to the existing United Nations Food and Agriculture Organization (FAO) land-cover classification system as well as the International Geosphere-Biosphere Programme (IGBP) system. Using the four classification algorithms, we obtained the initial set of global land-cover maps. The SVM produced the highest overall classification accuracy (OCA) of 64.9% assessed with our test samples, with RF (59.8%), J4.8 (57.9%), and MLC (53.9%) ranked from the second to the fourth. We also estimated the OCAs using a subset of our test samples (8629) each of which represented a homogeneous area greater than 500 m × 500 m. Using this subset, we found the OCA for the SVM to be 71.5%. As a consistent source for estimating the coverage of global land-cover types in the world, estimation from the test samples shows that only 6.90% of the world is planted for agricultural production. The total area of cropland is 11.51% if unplanted croplands are included. The forests, grasslands, and shrublands cover 28.35%, 13.37%, and 11.49% of the world, respectively. The impervious surface covers only 0.66% of the world. Inland waterbodies, barren lands, and snow and ice cover 3.56%, 16.51%, and 12.81% of the world, respectively.

Towards a common validation sample set for global land-cover mapping

Validating land-cover maps at the global scale is a significant challenge. We built a global validation data-set based on interpreting Landsat Thematic Mapper (TM) and Enhanced TM Plus (ETM+) images for a total of 38,664 sample units pre-determined with an equal-area stratified sampling scheme. This was supplemented by MODIS enhanced vegetation index (EVI) time series data and other high-resolution imagery on Google Earth. Initially designed for validating 30 m-resolution global land-cover maps in the Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) project, the data-set has been carefully improved through several rounds of interpretation and verification by different image interpreters, and checked by one quality controller. Independent test interpretation indicated that the quality control correctness level reached 90% at level 1 classes using selected interpretation keys from various parts of the USA. Fifty-nine per cent of the samples have been verified with high-resolution images on Google Earth. Uncertainty in interpretation was measured by the interpreter’s perceived confidence. Only less than 7% of the sample was perceived as low confidence at level 1 by interpreters. Nearly 42% of the sample units located within a homogeneous area could be applied to validating global land-cover maps whose resolution is 500 m or finer. Forty-six per cent of the sample whose EVI values are high or with little seasonal variation throughout the year can be applied to validate land-cover products produced from data acquired in different phenological stages, while approximately 76% of the remaining sample whose EVI values have obvious seasonal variation was interpreted from images acquired within the growing season. While the improvement is under way, some of the homogeneous sample units in the data-set have already been used in assessing other classification results or as training data for land-cover mapping with coarser-resolution data.

Meta-discoveries from a synthesis of satellite-based land-cover mapping research

Since the launch of the first land-observation satellite (Landsat-1) in 1972, land-cover mapping has accumulated a wide range of knowledge in the peer-reviewed literature. However, this knowledge has never been comprehensively analysed for new discoveries. Here, we developed the first spatialized database of scientific literature in English about land-cover mapping. Using this database, we tried to identify the spatial temporal patterns and spatial hotspots of land-cover mapping research around the world. Among other findings, we observed (1) a significant mismatch between hotspot areas of land-cover mapping and areas that are either hard to map or rich in biodiversity; (2) mapping frequency is positively related to economic conditions; (3) there is no obvious temporal trend showing improvement in mapping accuracy; (4) images with more spectral bands or a combination of data types resulted in increased mapping accuracies; (5) accuracy differences due to algorithm differences are not as large as those due to various types of data used; and (6) the complexity of a classification system decreases its mapping accuracy. We recommend that one way to improve our understanding of the challenges, advances, and applications of previous land-cover mapping is for journals to require area-based information at the time of manuscript submission. In addition, building a standard protocol for systematic assessment of land-cover mapping efforts at the global scale through international collaboration is badly needed.

Quantifying Multi-Decadal Change of Planted Forest Cover Using Airborne LiDAR and Landsat Imagery

Continuous monitoring of forest cover condition is key to understanding the carbon dynamics of forest ecosystems. This paper addresses how to integrate single-year airborne LiDAR and time-series Landsat imagery to derive forest cover change information. LiDAR data were used to extract forest cover at the sub-pixel level of Landsat for a single year, and the Landtrendr algorithm was applied to Landsat spectral data to explore the temporal information of forest cover change. Four different approaches were employed to model the relationship between forest cover and Landsat spectral data. The result shows incorporating the historic information using the temporal trajectory fitting process could infuse the model with better prediction power. Random forest modeling performs the best for quantitative forest cover estimation. Temporal trajectory fitting with random forest model shows the best agreement with validation data (R2 = 0.82 and RMSE = 5.19%). We applied our approach to Youyu county in Shanxi province of China, as part of the Three North Shelter Forest Program, to map multi-decadal forest cover dynamics. With the availability of global time-series Landsat imagery and affordable airborne LiDAR data, the approach we developed has the potential to derive large-scale forest cover dynamics.

A tentative study of water quality retrieval in low-level-polluted Case II waters using analytical model

The problem of Case II water pollution has been paid more and more attention these years. Although traditional means could provide more accurate measured values, they are greatly limited by its temporal and spatial defects. Therefore, remote sensing technology is introduced in and it has become a hot spot in the study of water environment monitoring. In this study, Guanting Reservoir, which lies between Zhangjiakou (HeBeijing) and Beijing, is selected to be the research object. With the data obtained in situ and in the laboratory, the water quality retrial model is established to calculate the water quality parameters of Guanting Reservoir. Further, combining with the satellite data (CHRIS/Proba), the qualitative distribution of water quality parameters at the whole reservoir region scale is obtained, which could support the quantitative study in the next step with data and theory basis.

Water-volume variations of Lake Hulun estimated from serial Jason altimeters and Landsat TM/ETM+ images from 2002 to 2017

ABSTRACT Lake Hulun is the fifth largest lake in China. The dramatic water-volume variations since the 21th century has aroused concerns from local and transboundary water resource authorities. To track its dynamics during the past sixteen years with no aid of in-situ data, serial Jason altimeters and optical remote sensing images were integrated to reconstruct the time series of the water-volume variations. We developed a workflow consisting of four procedures: 1) Deriving the water-level time series using Jason-1, Jason-2 and Jason-3 after applying a feature-based waveform classification method and a targeted waveform retracking strategy; 2) Extracting the water-area by using Landsat images; 3) Establishing the relationship between water-level and water-area; and 4) Reconstructing the water-volume variation time series by integrating the water-level time series and the relationship derived from 3). We found that Lake Hulun has gone through three stages of changes: 1) Period one (2002–2009), water-level and volume dropped significantly at a rate of −0.40 m yr−1 and −0.73 km3 yr−1, respectively. 2) Period two (2010–2012), the water-level and volume were relatively stable. 3) Period three (2013–2015), the water-level and volume began to rise rapidly at the rates of 1.09 m yr−1 and 1.99 km3 yr−1, respectively. In addition, two significant change years (2007 and 2013) were detected, which corresponded well with extreme climatic conditions. Preliminary analyses indicate that the water-volume variations are closely related to precipitation and temperature anomalies in wet seasons.

The importance of data type, laser spot density and modelling method for vegetation height mapping in continental China

ABSTRACT Vegetation height not only has great significance in the field of ecology but also offers a useful contribution to detailed land cover classification. The first vegetation height map was acquired in this study using the ice, cloud, and land elevation satellite /geosciences laser altimeter system (ICESat/GLAS) and other multisource remote sensing data, such as moderate-resolution imaging spectroradiometer (MODIS) tree cover products, leaf area index (LAI) products, Nadir bidirectional reflectance distribution function (BRDF)-adjusted reflectance (NBAR), climatic variables, and topographic indices. We mainly discuss the importance of data type, density of laser spot and modelling method in the generation of this vegetation height map in continental China. It was found that (1) a higher density of laser spot could improve the reliability of modelling in mountainous areas covered by a wide range of forest and shrub land; (2) in terms of the importance of input variables, in the random forest regression modelling, the most important ones are elevation, slope, mean air temperature, temperature variance, precipitation, precipitation variance, and NBAR; (3) when modelling using 50 ecozones covering the whole of continental China, the model showed a good performance with an accuracy of root mean square error (RMSE), correlation coefficient (r), index of agreement (d), and mean absolute error (MAE) at 5.7, 0.7, 0.8, and 3.8 m, respectively. A visual comparison suggests that the spatial pattern of vegetation height is consistent with that of land cover in China. It is very necessary in evaluating the importance of data type, laser spot density, and modelling method in vegetation height mapping in continental China.

A simplified image fusion technique with sensor spectral response

Introducing the sensor spectral response into the wavelet-based image fusion methods may produce images closer to which obtained by the ideal sensor. But the wavelet-based image fusion methods are complex because of the wavelet decomposition. This paper presents a simplified image fusion method using sensor spectral response which derives from a wavelet-based one, but need not to do the wavelet decomposition at the last calculation. The experimental results demonstrate that it provides good performance both in processing speed and image fusion quality.