Qiangyi Yu

Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping

Google Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics.

From meta-studies to modeling: Using synthesis knowledge to build broadly applicable process-based land change models

This paper explores how meta-studies can support the development of process-based land change models (LCMs) that can be applied across locations and scales. We describe a multi-step framework for model development and provide descriptions and examples of how meta-studies can be used in each step. We conclude that meta-studies best support the conceptualization and experimentation phases of the model development cycle, but cannot typically provide full model parameterizations. Moreover, meta-studies are particularly useful for developing agent-based LCMs that can be applied across a wide range of contexts, locations, and/or scales, because meta-studies provide both quantitative and qualitative data needed to derive agent behaviors more readily than from case study or aggregate data sources alone. Recent land change synthesis studies provide sufficient topical breadth and depth to support the development of broadly applicable process-based LCMs, as well as the potential to accelerate the production of generalized knowledge through model-driven synthesis.

Model-based analysis of spatio-temporal changes in land use in Northeast China

Spatially explicit modeling techniques recently emerged as an alternative to monitor land use changes. This study adopted the well-known CLUE-S (Conversion of Land Use and its Effects at Small regional extent) model to analyze the spatio-temporal land use changes in a hot-spot in Northeast China (NEC). In total, 13 driving factors were selected to statistically analyze the spatial relationships between biophysical and socioeconomic factors and individual land use types. These relationships were then used to simulate land use dynamic changes during 1980–2010 at a 1 km spatial resolution, and to capture the overall land use change patterns. The obtained results indicate that increases in cropland area in NEC were mainly distributed in the Sanjiang Plain and the Songnen Plain during 1980–2000, with a small reduction between 2000 and 2010. An opposite pattern was identified for changes in forest areas. Forest decreases were mainly distributed in the Khingan Mountains and the Changbai Mountains between 1980 and 2000, with a slight increase during 2000–2010. The urban areas have expanded to occupy surrounding croplands and grasslands, particularly after the year 2000. More attention is needed on the newly gained croplands, which have largely replaced wetlands in the Sanjiang Plain over the last decade. Land use change patterns identified here should be considered in future policy making so as to strengthen local eco-environmental security.

Extending the Pairwise Separability Index for Multicrop Identification Using Time-Series MODIS Images

The pairwise separability index (SI) has been demonstrated as an effective indicator for capturing crucial phenological differences between two plant species. However, its application to crop types, which have more obvious phenological characteristics than natural vegetation, has received less attention, and extending the pairwise SI to multiple crops for feature selection still remains a challenge. This paper presented two SI extension approaches (SIave and SImin) to select the optimal spectro-temporal features for multiple crops, and investigated their classification performance using Heilongjiang Province, China, as a study area. Feature interpretability and classification accuracy of different crops were evaluated for the two approaches. The results showed that the SIave approach generally has relatively high feature interpretability due to its better description of crucial phenological characteristics of different crops. Those crops with high separability are insensitive to the extension approach and have similar classification accuracy for the two approaches, whereas those crops with poor separability show good performance with the SImin method. Due to the higher temporal autocorrelation, the optimal features for crop classification that are selected by the SIave approach exhibit greater information redundancy across the time domain than those that are selected by the SImin approach, which largely explains the relatively low classification accuracy achieved using the SIave approach. These comparison results between SImin and SIave approaches also indicate that time-series images with high temporal resolution do not necessarily produce high classification accuracy, regardless of their ability to describe the seasonal characteristics of crops.

eFarm: A Tool for Better Observing Agricultural Land Systems

Currently, observations of an agricultural land system (ALS) largely depend on remotely-sensed images, focusing on its biophysical features. While social surveys capture the socioeconomic features, the information was inadequately integrated with the biophysical features of an ALS and the applications are limited due to the issues of cost and efficiency to carry out such detailed and comparable social surveys at a large spatial coverage. In this paper, we introduce a smartphone-based app, called eFarm: a crowdsourcing and human sensing tool to collect the geotagged ALS information at the land parcel level, based on the high resolution remotely-sensed images. We illustrate its main functionalities, including map visualization, data management, and data sensing. Results of the trial test suggest the system works well. We believe the tool is able to acquire the human–land integrated information which is broadly-covered and timely-updated, thus presenting great potential for improving sensing, mapping, and modeling of ALS studies.

Spatial evaluation of crop maps by the spatial production allocation model in China

Spatial Production Allocation Model (SPAM), developed by International Food Policy Research Institute (IFPRI), is one of broadest spatial models that applied a cross-entropy method to downscale the area and yield for each crop with a resolution of 5 arc minute globally for the year 2000 and 2005. To evaluate the accuracy of three staple crops (rice, wheat and maize) in China allocated by SPAM, we compared these crop maps with remote sensed cropland derived from national land cover datasets. This is done through a comparison scheme that accounts for spatial difference at the pixel level. Four types (no-existing, mis-allocated, over-estimated and reasonable) were formulated in this scheme that was used to evaluate the per-pixel area accuracy of each of the three crops on national and provincial scales. Overall, the map of maize has the highest area accuracy with a 64% percentage of reasonable pixels that covers 96% of the total maize area, in contrast, 57% (90%) and 44% (81%) for the wheat and rice map respectively. Further, crop area consistency in rain-fed cropland is better than that in irrigated cropland. Through the evaluations, we can provide decision makers with information on the SPAM products exist as well as the strengths and weaknesses. Meanwhile, some recommendations can be concluded on priorities for further work on the improvement of the reliability, utility and periodic repeatability of crop distribution products.

Spatio-temporal differences and factors influencing intensive cropland use in the Huang-Huai-Hai Plain

This study developed a comprehensive system to evaluate the intensity of cropland use and evolution of cropland use in the Huang-Huai-Hai Plain. Delphi-entropy methods were adopted to determine the weight of the index, and the GeoDetector model was established to explore the influencing factors. The results are summarized as follows: (1) The intensity of inputs, degree of utilization, and production increased continuously, but the intensity of continuous conditions experienced an overall decline followed by a rebound towards the end of the study period. The number of counties with high and moderately high intensity increased by 56.8% and 14.6%, respectively, from 1996 to 2011. The number of counties with moderately low and low intensity declined by 35.9 % and 11.9 %, respectively. Areas with significant increases in intensity were mainly distributed in northeast Hebei Province, northwest Shandong Province, and north Jiangsu Province. The intensity is high in northern Jiangsu and Anhui; the output effect remained above moderate intensity mainly near Beijing, Tianjin, Tangshan, and counties in the suburbs of Shijiazhuang. (2) Natural disasters, elevation, slope, and road networks were the main factors influencing the intensity of cropland use in this region, with influence values of 0.158, 0.143, 0.129, and 0.054, respectively. Areas with moderately high and high levels of intensity were distributed in low-lying areas. Uneven distribution of precipitation, seasonal drought, and flood disasters can directly affect the stability index of croplands and reduce the intensity of cropland use. Developed road networks are associated with moderately high intensity. Our results suggest recommendations such as promoting agricultural intensification and large-scale management, promoting the construction of road networks, improving early warning systems for drought and flood disasters, and promoting moderate and intensive use of arable land, and focusing on restoration and sustainable use of cropland.

Cultivated land change in the Belt and Road Initiative region

The Belt and Road Initiative (BRI)–a development strategy proposed by China – provides unprecedented opportunities for multi-dimensional communication and cooperation across Asia, Africa and Europe. In this study, we analyse the spatio-temporal changes in cultivated land in the BRI countries (64 in total) to better understand the land use status of China along with its periphery for targeting specific collaboration. We apply FAO statistics and GlobeLand30 (the world’s finest land cover data at a 30-m resolution), and develop three indicator groups (namely quantity, conversion, and utilization degree) for the analysis. The results show that cultivated land area in the BRI region increased 3.73×104 km2 between 2000 and 2010. The increased cultivated land was mainly found in Central and Eastern Europe and Southeast Asia, while the decreased cultivated land was mostly concentrated in China. Russia ranks first with an increase of 1.59×104 km2 cultivated land area, followed by Hungary (0.66×104 km2) and India (0.57×104 km2). China decreased 1.95×104 km2 cultivated land area, followed by Bangladesh (–0.22×104 km2) and Thailand (–0.22×104 km2). Cultivated land was mainly transferred to/from forest, grassland, artificial surfaces and bare land, and transfer types in different regions have different characteristics: while large amount of cultivated land in China was converted to artificial surfaces, considerable forest was converted to cultivated land in Southeast Asia. The increase of multi-cropping index dominated the region except the Central and Eastern Europe, while the increase of fragmentation index was prevailing in the region except for a few South Asian countries. Our results indicate that the negative consequence of cultivated land loss in China might be underestimated by the domestic- focused studies, as none of its close neighbours experienced such obvious cultivated land losses. Nevertheless, the increased cultivated land area in Southeast Asia and the extensive cultivated land use in Ukraine and Russia imply that the regional food production would be greatly improved if China’ “Go Out policy” would help those countries to intensify their cultivated land use.

Efficiency Analysis of Agricultural Land Use Based on DEA Method: A Case Study among APEC Economies

This study made an analysis of agricultural land use efficiency in APEC (Asia-Pacific Economic Cooperation) with an objective to find which member economy can produce at least the same amount of food production with less resources input. To do so, data envelopment analysis (DEA), a useful method to evaluate the efficiency and productivity of a number of producers which consume multiple inputs to produce multiple outputs, was used to analyze the agricultural land use efficiency in an input-output system for food production. The results show that the overall land use efficiency for food production is relatively low in APEC region, there is plenty of room for most economies to increase food yield at current input level. The input factors made different contribution to land use efficiency: Arable land area is the last factor to cause input redundancy, agricultural labors should be liberated from agricultural sector, or it will cause inefficiency to a certain extent, artificial material input such as fertilizer and machinery plays an important role in agricultural land use system.

Evaluating food security in APEC region based on grain productivity

This study made an assessment of food security in APEC (Asia-Pacific Economic Cooperation) region with more attention to highlight grain production in food security system. To do so, the mechanism method was firstly used to calculate the productivity potential of radiation, thermal, climatic, and land productivity potential regressively, and to count the grain production potential in the whole APEC region. Based on the grain productivity potential, some important indicators were used to assess the food security of each APEC member economies under the framework of Analytic Hierarchy Process. The results show that the overall grain production potential of APEC region reaches as high as 1.7 billion tons. Compared with the real grain production of 1.1 billion tons, there is possibility for all the economies to increase their production in the future. However, when considering population carrying capacity and food consumption, food security status largely differs among individual economies, and expect for Japan and Korea, the developed economies is generally more secured than the developing economies. Exploiting land productivity potential at uttermost is a possible way to improve food security, yet at same time it is necessary to control population explosion, especially for those overloaded regions.