Spatial Patterns and Driving Forces of Greenhouse Land Change in Shouguang City, China

Yu, Biao; Song, Wei; Lang, Yanqing

doi:10.3390/su9030359

Cited by 33 publications

(27 citation statements)

References 56 publications

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“…In light of the comprehensive effects of various factors, including the natural environment, social economy, and technology, more than 40 countries worldwide (about 40% of the global population) currently face critical water shortages, severely restricting their sustainable social and ecological development [1,5,6]. Imbalances in water supply and demand lead to shortages; specifically, global climate change, population growth, rapid urbanization, and agricultural expansion have all caused the water demands of different sectors to increase rapidly [7][8][9][10][11]. Global warming leads to increased evapotranspiration, which aggravates water cycling processes and causes increased rainfall [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Crop Irrigation Water Requirements in the Heihe River Basin, China

Liu

Song

Deng

2017

Water

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Abstract:Agricultural expansion, population growth, rapid urbanization, and climate change have all significantly impacted global water supply and demand and have led to a number of negative consequences including ecological degradation and decreases in biodiversity, especially in arid and semi-arid areas. The agricultural sector consumes the most water globally; crop irrigation alone uses up more than 80% of available agricultural water. Thus, to maintain sustainable development of the global economy and ecosystems, it is crucial to effectively manage crop irrigation water. We focus on the arid and semi-arid Heihe River Basin (HRB), China, as a case study in this paper, extracting spatiotemporal information on the distribution of crop planting using multi-temporal Thematic Mapper and Enhanced Thematic Mapper Plus (TM/ETM+) remote sensing (RS) images. We estimate the spatiotemporal crop irrigation water requirements (IWR c ) using the Food and Agriculture Organization of the United Nations (FAO) Penman-Monteith method and reveal variations in IWR c . We also analyze the impact of changes in crop planting structure on IWR c and discuss strategies for the rational allocation of irrigation water as well as policies to alleviate imbalance between water supply and demand. The results of this study show that effective rainfall (ER) decreases upstream-to-downstream within the HRB, while crop evapotranspiration under standard conditions (ET c ) increases, leading to increasing spatial variation in IWR c from zero up to 150 mm and between 300 and 450 mm. Data show that between 2007 and 2012, annual mean ER decreased from 139.49 to 106.29 mm, while annual mean ET c increased from 483.87 to 500.38 mm, and annual mean IWR c increased from 339.95 to 370.11 mm. Data show that monthly mean IWR c initially increased before decreasing in concert with crop growth. The largest values for this index were recorded during the month of June; results show that IWR c for May and June decreased by 8.14 and 11.67 mm, respectively, while values for July increased by 5.75 mm between 2007 and 2012. These variations have helped to ease the temporal imbalance between water supply and demand. Mean IWR c values for oilseed rape, corn, barley, and other crops all increased over the study period, from 208. 43, 349.35, 229.26, and 352.85 mm, respectively, in 2007, to 241.81, 393.10, 251.17, and 378.86 mm, respectively, in 2012. At the same time, the mean IWR c of wheat decreased from 281.53 mm in 2007 to 266.69 mm in 2012. Mainly because of changes in planting structure, the total IWR c for the HRB in 2012 reached 2692.58 × 10 6 m 3 , an increase of 332.16 × 10 6 m 3 (14.07%) compared to 2007. Data show that 23.11% (76.77 × 10 6 m 3 ) of this increase is due to crop transfers, while the remaining 76.89% (255.39 × 10 6 m 3 ) is the result of the rapid expansion of cultivated land. Thus, to maintain both the sustainable development and ecological security of the HRB, it is crucial to efficiently manage and utilize agricultural water in light of spat...

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Patterns of Crop Irrigation Water Requirements in the Heihe River Basin, China

Liu

Song

Deng

2017

Water

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“…It proves that the phenological stage has great impacts on plastic greenhouse mapping accuracies. Thus, remote sensing images at a particular phase (mid or later April) of the year are typically required [40]. Our mapping method, however, presents good performances at two completely different phenological stages (summer and winter).…”

Section: Discussionmentioning

confidence: 99%

“…2 The study area has a warm temperate continental monsoon climate with long and dry winters. The exhibited plastic greenhouses belong to both arch and single plastic greenhouses, which are the main types in China for planting various vegetables and typically feature a size of 400-1200 m² [40]. In other areas, such as in Spain even larger cover areas are characteristic (around 10 000 m 2 ) [37].…”

Section: A Study Areamentioning

confidence: 99%

“…Remote sensing images from mid or late April are considered suitable for the plastic greenhouse mapping in China [40]. In this period, the crops under the plastic greenhouse are mainly fruit-vegetable (e.g., tomato, cucumber, and luffa) with large plants and leaf areas with typical reflective properties of vegetation; plastic membrane on the roof of greenhouses increases the spectral reflectance significantly; as a consequence, the spectral signature of plastic greenhouses differs from other land surface types.…”

Section: B Gf-2 Data and Preprocessingmentioning

confidence: 99%

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Mapping Plastic Greenhouses Using Spectral Metrics Derived From GaoFen-2 Satellite Data

Shi

Huang

Zhong

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Plastic greenhouses are an important hallmark of agricultural progress. To meet the growing demand for vegetable and food, the amount of plastic greenhouses has increased significantly over the past few decades. Remote sensing is considered as a promising data source for taking inventory and monitoring plastic greenhouses for managing modern agriculture. However, a systematic catalog of number and spatial distribution of plastic greenhouses is mostly inexistent. This is primarily due to the complex land surface characteristics and seasonal changes, which make automated classification based on EO data challenging. Current approaches generally suffer from the susceptibility of approaches toward thresholds and changes in the phenological stage. Besides, they often require an extensive training of models, however, often the necessary amount of training data is inexistent. To address these issues, we suggest an adaptable and universal plastic greenhouse mapping method based on very high spatial resolution optical satellite data (GaoFen-2 image) with a threestep procedure. A plastic greenhouse gathering area (100 km 2) is selected for the development of the initial method. We receive a very competitive mapping accuracy 97.34% and the likelihood of plastic greenhouses being mapped correctly reaches to 95.20%. Subsequently, we transfer it to a much larger area (2025 km 2) featuring a different phenological stage and different surrounding patterns. The stable mapping accuracy proves the validity of our approach.

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“…Globally, the use of greenhouses (GHs) in food production has increased owing to their high productivity and economic benefits [10]. The land area occupied by plastic GHs exceeded 500,000 ha; where south-east of Asia have the most significant concentrations followed by the Mediterranean basin [11].…”

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confidence: 99%

Spatio-Temporal Distribution of Greenhouses and Affecting Driving Factors in Al-Batinah South, Oman, Using GIS Mapping

2019

Am.-Eursion j. sustain. Agric.

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Geographic Information System (GIS) tools have been utilized in greenhouse cropping systems for detection, identifying, and mapping. This study aimed to study the spatiotemporal distribution of greenhouses (GHs) and the driving factors affecting this distribution in Al-Batinah South, Oman using GIS Mapping. First, a field survey was conducted between July and December 2017 to all farms with greenhouses (GH farms) in the study area. Second, the required digital and non-digital data were collected from different sources. Third, for 2017, the recorded geographic coordinates of GHs and GH farms were mapped, while the aerial photographs for 2008 using ArcMap 10.1 were used to study the spatial and temporal distribution of GH farms and GHs. Fourth, four main driving factors were selected: distance to various market-outlets, distance to roads, distance to coastline and effects of groundwater quality to observe their significance on the spatial distribution of GH farms in 2017 using simple regression analysis. Fifth, the most driving factors explaining the variance of GH farms spatial distribution in 2017 were extracted using Principle Component Analysis (PCA). The results revealed that, for 2017, the distribution of greenhouse farms varied insignificantly (p>0.05) among the six cities in Al-Batinah South, where 71% and 53% of greenhouses and greenhouse farms were concentrated only in Barka coastal city. For the period 2008-2017, the density of greenhouse farms and greenhouses were shifting towards the inland cities such as Nakhal, Al-Rustaq, and Awabi. Within the same period, there was an average reduction of several greenhouses and greenhouse farms by 16.2 and 2.2, respectively. The total land area occupied by greenhouses shrank by an average of 0.56 ha/year in the same period. Results displayed that only one of the four main driving factors had a statistically significant influence (p<0.05) on the spatial distribution of greenhouse farms in 2017, which was the distance to roads. The most affecting driving factors on this distribution was the distance to local markets and the distance to Al-Mawaleh Central market by accounting for 44.2% and 23.8% of the total variance, respectively. In conclusion, Over the period 2008-2017, there was an overall reduction in the average number of GH farms and GHs due to land use changes, high construction costs (capital cost), high production costs, fuel cost, pests and diseases, water scarcity (in some places), competition due to foreign produce and from high tech domestic, low subsidy by Ministry of Agriculture and Fisheries and/or price fluctuations of fresh produce. The influence of distance to the highway and distance to the nearest paved roads were found significant driving factors (p<0.05). Based on PCA analysis, distance to local markets, and distance to Al-Mawaleh Central market were the two driving factors describing the total variance of GH farms spatial distribution. It is recommended, for future expansion of greenhouse farms, to consider inland areas with low groundwater sa...

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Spatial Patterns and Driving Forces of Greenhouse Land Change in Shouguang City, China

Cited by 33 publications

References 56 publications

Spatiotemporal Patterns of Crop Irrigation Water Requirements in the Heihe River Basin, China

Spatiotemporal Patterns of Crop Irrigation Water Requirements in the Heihe River Basin, China

Mapping Plastic Greenhouses Using Spectral Metrics Derived From GaoFen-2 Satellite Data

Spatio-Temporal Distribution of Greenhouses and Affecting Driving Factors in Al-Batinah South, Oman, Using GIS Mapping

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