Qinchuan Xin

FROM-GC: 30 m global cropland extent derived through multisource data integration

We report on a global cropland extent product at 30-m spatial resolution developed with two 30-m global land cover maps (i.e. FROM-GLC, Finer Resolution Observation and Monitoring, Global Land Cover; FROM-GLC-agg) and a 250-m cropland probability map. A common land cover validation sample database was used to determine optimal thresholds of cropland probability in different parts of the world to generate a cropland/noncropland mask according to the classification accuracies for cropland samples. A decision tree was then applied to combine two 250-m cropland masks: one existing mask from the literature and the other produced in this study, with the 30-m global land cover map FROM-GLC-agg. For the smallest difference with country-level cropland area in Food and Agriculture Organization Corporate Statistical (FAOSTAT) database, a final global cropland extent map was composited from the FROM-GLC, FROM-GLC-agg, and two masked cropland layers. From this map FROM-GC (Global Cropland), we estimated the global cropland areas to be 1533.83 million hectares (Mha) in 2010, which is 6.95 Mha (0.45%) less than the area reported by the Food and Agriculture Organization (FAO) of the United Nations for the year 2010. A country-by-country comparison between the map and the FAOSTAT data showed a linear relationship (FROM-GC = 1.05*FAOSTAT −1.2 (Mha) with R2= 0.97). Africa, South America, Southeastern Asia, and Oceania are the regions with large discrepancies with the FAO survey.

Mapping Crop Cycles in China Using MODIS-EVI Time Series

Abstract: As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with

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.

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the Midwestern US with low errors for both corn (R2 = 0.77; RMSE = 0.89 MT/ha) and soybeans (R2 = 0.66; RMSE = 0.38 MT/ha). Because the proposed algorithm does not require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.

Do Arctic breeding geese track or overtake a green wave during spring migration

Geese breeding in the Arctic have to do so in a short time-window while having sufficient body reserves. Hence, arrival time and body condition upon arrival largely influence breeding success. The green wave hypothesis posits that geese track a successively delayed spring flush of plant development on the way to their breeding sites. The green wave has been interpreted as representing either the onset of spring or the peak in nutrient biomass. However, geese tend to adopt a partial capital breeding strategy and might overtake the green wave to accomplish a timely arrival on the breeding site. To test the green wave hypothesis, we link the satellite-derived onset of spring and peak in nutrient biomass with the stopover schedule of individual Barnacle Geese. We find that geese track neither the onset of spring nor the peak in nutrient biomass. Rather, they arrive at the southernmost stopover site around the peak in nutrient biomass, and gradually overtake the green wave to match their arrival at the breeding site with the local onset of spring, thereby ensuring gosling benefit from the peak in nutrient biomass. Our approach for estimating plant development stages is critical in testing the migration strategies of migratory herbivores.

Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions

Agricultural droughts can create serious threats to food security. Tools for dynamic prediction of drought impacts on yields over large geographical regions can provide valuable information for drought management. Based on the DeNitrification-DeComposition (DNDC) model, the current research proposes a Drought Risk Analysis System (DRAS) that allows for the scenario-based analysis of drought-induced yield losses. We assess impacts on corn yields using two case studies, the 2012 U.S.A. drought and the 2000 and 2009 droughts in Liaoning Province, China. The results show that the system is able to perform daily simulations of corn growth and to dynamically evaluate the large-scale grain production in both regions. It is also capable of mapping the up-to-date yield losses on a daily basis, the additional losses under different drought development scenarios, and the yield-based drought return periods at multiple scales of geographic regions. In addition, detailed information about the water-stress process, biomass development, and the uncertainty of drought impacts on crop growth at a specific site can be displayed in the system. Remote sensing data were used to map the areas of drought-affected crops for comparison with the modeling results. Beyond the conventional drought information from meteorological and hydrological data, this system can provide comprehensive and predictive yield information for various end-users, including farmers, decision makers, insurance agencies, and food consumers. A Drought Risk Analysis System for daily evaluating and predicting large-scale drought-induced yield losses during the 2012 U.S. drought.Detailed information available for the crop water-stresses processes, biomass development, and uncertainties of drought impacts.Dynamic and scale-dependent drought return periods according to yield losses demonstrated in the case study of Liaoning, China.Remotely sensed data provided essential information for mapping and verifying the drought-affected areas.

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.

Propagating Updates of Residential Areas in Multi-Representation Databases Using Constrained Delaunay Triangulations

Updating topographic maps in multi-representation databases is crucial to a number of applications. An efficient way to update topographic maps is to propagate the updates from large-scale maps to small-scale maps. Because objects are often portrayed differently in maps of different scales, it is a complicated process to produce multi-scale topographic maps that meet specific cartographical criteria. In this study, we propose a new approach to update small-scale maps based on updated large-scale maps. We first group spatially-related objects in multi-scale maps and decompose the large-scale objects into triangles based on constrained Delaunay triangulation. We then operate the triangles and construct small-scale objects by accounting for cartographical generalization rules. In addition, we apply the Tabu Search algorithm to search for the optimal sequences when constructing small-scale objects. A case study was conducted by applying the developed method to update residential areas at varied scales. We found the proposed method could effectively update small-scale maps while maintaining the shapes and positions of large-scale objects. Our developed method allows for parallel processing of update propagation because it operates grouped objects together, thus possesses computational advantages over the sequential updating method in areas with high building densities. Although the method proposed in this study requires further tests, it shows promise with respect to automatic updates of polygon data in the multi-representation databases.

High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data

Multiple cropping, a common practice of intensive agriculture that grows crops multiple times in the agricultural land in one growing season, is an effective way to fulfill the food demand given limited cropland areas. Deriving cropping cycles from satellite data provides the spatial distribution of cropping intensities that allows for monitoring of the multiple cropping activities over large areas. Although efforts have been made to map cropping cycles at 500 m or coarser resolution, producing cropping cycle maps at high resolution remain challenging because data from single satellite sensor do not provide sufficient spatiotemporal observations. In this paper, we generate dense time series of satellite data at 30 m resolution by fusion of Landsat and MODIS data, and derive the cropping cycles from the fused time series data. The method achieves overall accuracies of 92.5% and 89.2%, respectively, for two typical regions of multiple cropping in China using samples identified based on satellite time series data, and an overall accuracy of 81.2% for four subregions using all samples identified based on multi-temporal high resolution images. The mapped crop cycles show to be reasonable geographically and agree with the national census data. The fusion approach provides a feasible way to map cropping cycles at 30 m resolution and enables improved depiction of the spatial distribution of multiple cropping.

Monitoring annual urbanization activities in Guangzhou using Landsat images 1987–2015

ABSTRACT Rapid land-use/land-cover (LULC) changes such as urbanization have tremendous impacts on regional climate and environment. Satellite images acquired by fast-developing remote-sensing techniques provide frequent observations of the land surface, thereby allowing for continuous mapping of urbanization activities. In this study, we investigated the annual urbanization activities over the past three decades in Guangzhou, one of the largest metropolises in China. To enhance the efficiency of training sample extraction in long-term land-cover mapping, we developed a three-step method: 1) three spectral indices were derived to extract the candidates of training samples based on decision trees; 2) a spatial filter was used to extract homogenous samples for each land-cover type; and 3) temporal consistency checking was performed for the samples of urban areas. We applied the developed method to time-series Landsat images and produced annual land-cover maps of Guangzhou from 1987 to 2015. We evaluated the produced land-cover maps and found an average overall accuracy of 89.80% for all studied years. Our results show that dramatic urbanization has occurred in the region of the Guangzhou city, where built-up areas have mostly expanded to the northwest, east, and south of the central regions of Guangzhou. The average growth rate of urban areas in Guangzhou from 1987 to 2015 was at 38.72 km2 per year, which was generally consistent with the government survey data. Future studies are required to understand how rapid urbanization in Guangzhou influences social economy and environmental sustainability.