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Abstract
land Article Mapping Highland Barley on the Qinghai–Tibet Combing Landsat OLI Data and Object-Oriented Classification Method 1 1 1 2 1 , 3 2 , 3 , Weidong Ma , Wei Jia , Peng Su , Xingyun Feng , Fenggui Liu and Jing’ai Wang * School of Geographical Science, Qinghai Normal University, Xining 810008, China; 201947341017@stu.qhnu.edu.cn (W.M.); jiawei1212@qhnu.edu.cn (W.J.); 201947331031@stu.qhnu.edu.cn (P.S.); liufenggui@igsnrr.ac.cn (F.L.) Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; 201811051120@mail.bnu.edu.cn Academy of Plateau Science and Sustainability, Xining 810008, China * Correspondence: jwang@bnu.edu.cn Abstract: In this paper, we use the extraction method of multi-factors fusion to extract the Highland barley cultivation area on Qinghai–Tibet Plateau. The study results indicate that: (1) the method (extracting through multi-factors fusion) is efficient during the extracting process and is highly accurate in extraction results. This extraction scheme allows for not only the spatial heterogeneity of different physical geographic units, but also the impact of multi-factors on crop cultivation; (2) according to our research, the total Highland barley cultivation area on Qinghai–Tibet Plateau is about 2.74 10 ha. Based on the statistics, we draw the first distribution map of the Highland barley cultivation area on Qinghai–Tibet Plateau, which upgrades its spatial distribution pattern from administrative unit to patch unit; (3) Highland barley in various divisions has a distinct spatial heterogeneity in elevation. On the whole, the Highland barley on the plateau is planted at an Citation: Ma, W.; Jia, W.; Su, P.; Feng, elevation of 2500–4500 m, up to 5200 m. Due to the impact of topography diversity, temperature, X.; Liu, F.; Wang, J. Mapping moisture, light, arable land and irrigation conditions, its cultivation area at the same elevation varies Highland Barley on the in different divisions. Qinghai–Tibet Combing Landsat OLI Data and Object-Oriented Keywords: Highland barley; Qinghai–Tibet Plateau; cultivation area extraction; object-oriented Classification Method. Land 2021, 10, extraction method; spatial distribution of Highland barley 1022. https://doi.org/10.3390/ land10101022 Academic Editor: Le Yu 1. Introduction Received: 30 July 2021 Since the 21st century, rapid climate change has caused a severer impact of agricultural Accepted: 27 September 2021 disasters, making the assessment of crop losses a research hotspot in natural disaster risk Published: 29 September 2021 assessment [1]. Natural disaster exposure research of crops is the prerequisite for crop loss assessment. The data of crop’s spatial distribution have become an urgent requirement Publisher’s Note: MDPI stays neutral for crop disaster loss assessment. As one of the oldest crops, barley is widely planted all with regard to jurisdictional claims in over the world [2]. Highland barley, a variety of genus barley, shows strong geoherbalism published maps and institutional affil- on Qinghai–Tibet Plateau [3] and is recognized as a predominant crop, because it adapts iations. best to the relatively harsh natural environment of the plateau. Various ethnic groups such as the Tibetan, Qiang and Naxi who live on the plateau regard it as an essential food crop [4,5]. Due to the influence of climate change, the upper limit of elevation for Highland barley cultivation is gradually rising, and the unstable climate in the high-elevation areas Copyright: © 2021 by the authors. leads to unpredictable cultivation [6]. Due to the limited cultivated land resources in Licensee MDPI, Basel, Switzerland. the Qinghai–Tibet Plateau, new cultivated land is bound to be reclaimed along with the This article is an open access article elevation of Highland barley cultivation. For the newly reclaimed Highland barley land, distributed under the terms and we also need to choose reasonable management strategies according to different land conditions of the Creative Commons attributes. Therefore, it is urgent and necessary to understand the spatial distribution of Attribution (CC BY) license (https:// Highland barley cultivation. creativecommons.org/licenses/by/ 4.0/). Land 2021, 10, 1022. https://doi.org/10.3390/land10101022 https://www.mdpi.com/journal/land Land 2021, 10, 1022 2 of 15 In recent years, researches on Highland barley mainly focus on three aspects: its physiological characteristics [7], nutritional value [8] and medicinal value [9]. They provide references for an in-depth study on the characteristics of Highland barley. However, the current data of its spatial distribution pattern is measured by county-level administrative unit, and it is impossible to carry out a high-accuracy quantitative assessment of natural disaster exposure of Highland barley [10]. We need to study it at a higher scale level so as to obtain a more accurate spatial distribution. Remote sensing technology can provide stable and accurate simultaneous observation results of wide-range earth’s surface. It has become an effective means for agricultural cultivation monitoring and can efficiently detect crop cultivation areas. Many satellite programs (such as Landsat, Modis, Aster, SPOT, Sentinel-1 and Sentinel-2) are nowadays providing free datasets, thus promoting satellite imagery exploitation for many agricultural applications [11]. Low-resolution and medium-resolution image data such as MODIS, Landsat TM/OLI, Sentinel-2 and GF-1 WFV have been widely used in studies of extracting rice, wheat, rapeseed and other crops [12–15]. Among them, the combination of MODIS data and vegetation index is a main approach to monitor large-scale crop [16,17]. However, with low spatial resolution and a large number of mixed pixels, MODIS data-based extrac- tion result has a lower accuracy than the higher resolution image data [18]. As for small and medium scale crop areas, the common remote sensing image data consist of Landsat TM/OLI, Sentinel-2 and GF-1 WFV. The result of the development of unmanned aerial vehicle (UAV) technology and high-resolution MS images, the managers and specialists in agriculture can use new tools and have more information to optimize management decisions and formulate precision farming solutions [19]. In the extraction of crops, find- ing an appropriate way to balance the cost–benefit relationship is of great significance. Many research results show that, medium-resolution-based extracting result has a higher accuracy than the low-resolution image data. The common methods used to extract crop distribution are supervised classifica- tion [20], artificial neural network [21], and random forest classification [22]. These above methods use spectral information of pixels, yet they overlook the texture information within the category and the interaction among the neighborhoods of the pixel [23,24]. The object-oriented classification method integrates the spectral and geometric characteristics of images, takes the segmented object as the target and analyzes the essential characteristics of the image object and the topological relationship between neighboring objects, which can effectively improve the spatial analysis ability of computer analyzing [25]. In order to obtain the high-accuracy spatial distribution of Highland barley, this paper uses Landsat 8 OLI images to extract its cultivation area through the following steps: (1) refer to China’s comprehensive agricultural zoning for division; (2) limit the extent of agricultural areas with the digital elevation model (DEM) and slope data; (3) use optimum index factor (OIF) to optimize the best band for Highland barley extraction; (4) use object-oriented classification method to extract the Highland barley cultivation area on Qinghai–Tibet Plateau. After obtaining the current spatial distribution information of Highland barley, we will also analyze the spatial distribution information of highland barley 30 years ago in the future work to analyze its dynamic changes. Since GF-1 WFV and Sentinel-2 began service in 2013 and 2016, respectively, the time series of image acquisition is relatively short. Landsat TM/OLI, which began to provide remote sensing images in 1984, has a long enough time series to meet the research needs, so we adopt Landsat 8 OLI images as the remote sensing image data [26]. We hope the final data will contribute to the quantitative analysis of Highland barley exposure and its future disaster loss assessment. 2. Study Area The Qinghai–Tibet Plateau, known as the third pole of the earth, has an average eleva- tion of more than 4000 m, making it the highest plateau in the world. Due to the alpine and complex natural geographical features, it has become the “initiator” and “regulatory Land 2021, 10, x FOR PEER REVIEW 3 of 16 Land 2021, 10, 1022 3 of 15 zone” of global climate change [27]. Located in western and southwestern China, the plat- zone” of global climate change [27]. Located in western and southwestern China, the 6 2 eau covers an approximate total area of 2.53 × 10 km and contains vast territory and 6 2 plateau covers an approximate total area of 2.53 10 km and contains vast territory diverse landforms. It stretches over the whole area of Tibet Autonomous Region and and diverse landforms. It stretches over the whole area of Tibet Autonomous Region Qinghai Province, parts of Sichuan, Yunnan, Gansu Province and Xinjiang Uygur Auton- and Qinghai Province, parts of Sichuan, Yunnan, Gansu Province and Xinjiang Uygur omous Region (Figure 1). Most part of the plateau belongs to arid and semi-arid areas: the Autonomous Region (Figure 1). Most part of the plateau belongs to arid and semi-arid northern zone is dominated by arid areas, and the central and southern zones are domi- areas: the northern zone is dominated by arid areas, and the central and southern zones nated by semi-humid and humid areas. In this region, the annual average temperature are dominated by semi-humid and humid areas. In this region, the annual average tem- range is from −5.75 °C to 2.57 °C, the annual sunshine duration is from 2500 to 3400 h, and perature range is from 5.75 C to 2.57 C, the annual sunshine duration is from 2500 to the annual total solar radiation is at 5000 to 8500 MJ/m . It is one of the most important 3400 h, and the annual total solar radiation is at 5000 to 8500 MJ/m . It is one of the most alpine agricultural and pastoral areas in the world [28,29]. Agricultural land is mainly important alpine agricultural and pastoral areas in the world [28,29]. Agricultural land is distributed in valley areas below 4600 m above sea level, and most is concentrated in He- mainly distributed in valley areas below 4600 m above sea level, and most is concentrated huang Valley (in Qinghai Province) and YLN region (the region along the Yarlung Zangbo in Hehuang Valley (in Qinghai Province) and YLN region (the region along the Yarlung River, Lhasa River and Nianchu River in Tibet Autonomous Region). Main crops include Zangbo River, Lhasa River and Nianchu River in Tibet Autonomous Region). Main crops wheat, Highland barley and rapeseed [30]. To study the impact of topographical factors include wheat, Highland barley and rapeseed [30]. To study the impact of topographical and to specifically extract Highland barley, this paper refers to the Chinese agricultural factors and to specifically extract Highland barley, this paper refers to the Chinese agri- division culturalsch division eme pro scheme posedpr by oposed Zhou by LisZhou an [31] Lisan and [d 31 ivide ] and s th divides e platea the u plateau into sevinto en major seven agricultural divisions, which is mainly based on natural conditions, agricultural produc- major agricultural divisions, which is mainly based on natural conditions, agricultural pro- tion duction status status and soc and ial social econom economy y condconditions. itions. The seven The seven major major agricultura agricultural l divisio divisions ns are as ar e follows, as follows, I: InI: ner Inner Mongo Mongolia, lia, Gans Gansu u and and NiNingxia ngxia far farming–pastoral ming–pastoral division division (IM (IMGN/FPD), GN/FPD), IIII: : Cent Central ral GGansu ansu and and EEastern astern Qin Qinghai ghai farm farming-pastoral ing-pastoral ddivision ivision (C (CGEQ/HFD), GEQ/HFD), III III: : Qi Qing- ng- hai and Gansu farming-pastoral division (QG/FPD), IV: South Xinjiang farming-pastoral hai and Gansu farming-pastoral division (QG/FPD), IV: South Xinjiang farming-pastoral division division (SX/ (SX/FPD), FPD), V: V Qin : Qinghai-T ghai-Tibet ibet alpine alpine pas pasturing turing division division (QT/ (QT/AP APD), VI: D), S VI: outh South Tibet Ti- bet farming-pastoral division (ST/FPD), VII: Sichuan–Tibet forest and farming–pastoral farming-pastoral division (ST/FPD), VII: Sichuan–Tibet forest and farming–pastoral divi- division (ST/FFD). sion (ST/FFD). Figure 1. Agricultural divisions on the Qinghai–Tibet Plateau. Figure 1. Agricultural divisions on the Qinghai–Tibet Plateau. 3. Materials and Methods 3. Materials and Methods 3.1. Data Collection 3.1. Data Collection We adopt Landsat 8 OLI images as the remote sensing image data (Table 1). The data We adopt Landsat 8 OLI images as the remote sensing image data (Table 1). The data are based on summer images and the imaging time is mainly from June 2019 to September are based on summer images and the imaging time is mainly from June 2019 to September 2019, select images with less than 10% cloud cover. Due to the extensive study area, in 2019, select images with less than 10% cloud cover. Due to the extensive study area, in some parts on the southern plateau, large amount of water vapor causes cloud cover, which some parts on the southern plateau, large amount of water vapor causes cloud cover, blocks out the earth surface. The 2019 summer images in these areas could not meet the which blocks out the earth surface. The 2019 summer images in these areas could not meet requirements, and they were replaced by the summer images from the nearest year. We the requirements, and they were replaced by the summer images from the nearest year. have a total number of 96 scenes in this paper. We have a total number of 96 scenes in this paper. Land 2021, 10, 1022 4 of 15 Table 1. Landsat 8 OLI sensor parameter. Band Wavelength Range Spatial Resolution General Purpose 1-COASTAL/AEROSOL 0.43–0.45 m 30 m Coastal environmental monitoring 2-Blue 0.45–0.51 m 30 m Visible light three-band 3-Green 0.53–0.59 m 30 m True color is used for feature recognition 4-Red 0.64–0.67 m 30 m 5-NIR 0.85–0.88 m 30 m Vegetation information extraction Vegetation drought monitoring 6-SWIR1 1.57–1.65 m 30 m Fire monitoring Mineral information extraction 7-SWIR2 2.11–2.29 m 30 m Feature recognition 8-PAN 0.50–0.68 m 15 m Data fusion Cirrus detection 9-Cirrus 1.36–1.38 m 30 m Data quality evaluation The boundary data of Qinghai–Tibet Plateau used in this paper are the vector data released by Zhang Yili from the Global Change Scientific Research Data Publishing Sys- tem [32]. This paper uses the Qinghai–Tibet Plateau DEM data with a spatial resolution of 30 m (Table 2). Table 2. Statistics of Highland barley planting areas in various agricultural divisions. Data Name Temporal Resolution Spatial Resolution Data Source Institute of Geographic Sciences and Natural Resources Research, CAS Landsat 8 OLI images 2019 30 m 30 m http://ids.ceode.ac.cn (accessed on 30 September 2020) [33] United States Geological Survey Digital elevation model 2010 30 m 30 m https://topotools.cr.usgs.gov (accessed on 15 March 2020) China Meteorological Data Network Multi-year average precipitation 1961–2019 - http://data.cma.cn (accessed on 1 January 2020) Ministry of Natural Resources of the People’s Republic of China Map of China 2019 - http://bzdt.ch.mnr.gov.cn (accessed on 25 August 2021) Global Change Research Data Qinghai–Tibet Plateau range and Publishing & Repository 2014 - boundary line http://www.geodoi.ac.cn (accessed on 1 January 2020) 3.2. Remote Sensing Image Preprocessing The remote sensing image data are preprocessed by ENVI 5.3 and ArcGIS 10.3. The steps are as follows: (1) Use the three parameters of spectral radiance value of each band, solar elevation angle and shooting time for Radiometric Calibration. Set the Calibration Type to Radiance; set the Data Type to Float; set the Scale Factor to 0.1; (2) Use FLAASH model for atmospheric correction. Atmospheric models are selected based on water vapor data or surface air temperature, fill in the Ground Elevation, Flight Date and Flight Time, select Aerosol Retrieval as 2-Band (K-T); set Water Retrieval to NO; (3) take the Gram- Schmidt method to convert panchromatic B8 (15 m) fusion with the multispectral band (30 m), and increase the spatial resolution of the multispectral band to 15 m. The result of the fusion is shown in Figure 2. Land 2021, 10, 1022 5 of 15 Land 2021, 10, x FOR PEER REVIEW 5 of 16 0 0 Figure 2. Comparison before and after OLI image fusion. (a–d) show the image before fusion, and (a –d ) are after fusion. Figure 2. Comparison before and after OLI image fusion. (a–d) show the image before fusion, and (a′–d′) are after fusion. After fusion, we cut the vector boundary of the study area to generate the work base 3.3. Sample-Based and Object-Oriented Information Extraction Methods image map, and obtain the slope data of the plateau from the DEM data. The object-oriented sample information-based extraction method takes the object generated by segmenting the entire image as the research target, and the size of the object 3.3. Sample-Based and Object-Oriented Information Extraction Methods is determined by the image segmentation scale and spatial structure. When extracting in- The object-oriented sample information-based extraction method takes the object formation, not only the spectral feature difference of the image is considered, but also generated by segmenting the entire image as the research target, and the size of the object spatial features such as the texture and shape of the features in the image and the relation- is determined by the image segmentation scale and spatial structure. When extracting ship structure information among various objects are considered [34]. The object-oriented information, not only the spectral feature difference of the image is considered, but also sample information-based extraction method encompasses two main steps: (1) the “seg- spatial features such as the texture and shape of the features in the image and the relation- mentation”, which is the delineation of homogeneous objects from the input imagery, fol- ship structure information among various objects are considered [34]. The object-oriented lowing the principle of clustering neighboring image pixels into “objects”, so as to max- sample information-based extraction method encompasses two main steps: (1) the “seg- imize the intra-object spectral homogeneity and inter-object spectral heterogeneity; (2) the mentation”, which is the delineation of homogeneous objects from the input imagery, “classification”, which labels and assigns each polygon to the target cover class [35]. following the principle of clustering neighboring image pixels into “objects”, so as to OLI image shows an enhancement in spectral heterogeneity after band fusion. And maximize the intra-object spectral homogeneity and inter-object spectral heterogeneity; its pixels contain more information, such as structural features, texture information, and (2) the “classification”, which labels and assigns each polygon to the target cover class [35]. the interconnection with neighboring features. Using the multi-scale segmentation algo- OLI image shows an enhancement in spectral heterogeneity after band fusion. And its rithm could fully take advantage of these abundant information. Multi-scale segmentation pixels contain more information, such as structural features, texture information, and the is an object-oriented image segmentation algorithm. It can segment an image into “homo- interconnection with neighboring features. Using the multi-scale segmentation algorithm geneous” objects, and the object size is determined by the segmentation scale. Any object could fully take advantage of these abundant information. Multi-scale segmentation is an after segmentation has the same or very similar features, such as spectrum and shape, and object-oriented image segmentation algorithm. It can segment an image into “homoge- each object interacts with each other, but not overlaps. We use it to segment the remote neous” objects, and the object size is determined by the segmentation scale. Any object sensi after n segmentation g images at th has e op the timal same sc or ale very , and similar then mer featur ge es, imag such es accor as spectr ding um to and geom shape, etric and and spectral differences of the pixel objects. each object interacts with each other, but not overlaps. We use it to segment the remote sensing The images setting at of the segment optimal atiscale, on para and met then ers mer is th ge e images most impo accor rtant ding pa tort geometric of the who and le multi spectral -scal dif e fer image ences seof gmen the tation pixel objects. process. The steps are as follows: (1) an appropriate scale The setting of segmentation parameters is the most important part of the whole threshold is set to terminate pixel merging; (2) according to the spectral information, tex- multi-scale image segmentation process. The steps are as follows: (1) an appropriate scale ture characteristics and characteristics of the ground object type to be extracted, appropri- threshold is set to terminate pixel merging; (2) according to the spectral information, texture ate weights are given to the spectrum and shape factors. The shape factor includes com- characteristics and characteristics of the ground object type to be extracted, appropriate pactness and smoothness, which are determined by the structural characteristics of the weights are given to the spectrum and shape factors. The shape factor includes compactness ground objects to be extracted from the image; (3) the image segmentation starts with any and smoothness, which are determined by the structural characteristics of the ground pixel in the image as the center. The segmentation process is completed when it exceeds objects to be extracted from the image; (3) the image segmentation starts with any pixel in the given threshold. We repeat experiments until the results match the terrain information the image as the center. The segmentation process is completed when it exceeds the given in the images. In each agricultural division, we set the segmentation threshold at 20–30, threshold. We repeat experiments until the results match the terrain information in the and the merging threshold at 80–90. The results are shown in Figure 3. All these parame- images. In each agricultural division, we set the segmentation threshold at 20–30, and the ters were empirically found to ensure the best results for delineation of desired segmen- merging threshold at 80–90. The results are shown in Figure 3. All these parameters were tation, using the trial and error approach. empirically found to ensure the best results for delineation of desired segmentation, using the trial and error approach. Land 2021, 10, 1022 6 of 15 Land 2021, 10, x FOR PEER REVIEW 6 of 16 Figure 3. Segmentation results under different segmentation scales. (a–d) show the image before segmentation, and 0 (a′0– Figure 3. Segmentation results under different segmentation scales. (a–d) show the image before segmentation, and (a –d ) d′) are after segmentation. are after segmentation. 3.4. Band Optimization 3.4. Band Optimization OLI image consists of seven multi-spectral bands, which contain detailed feature in- OLI image consists of seven multi-spectral bands, which contain detailed feature infor- formation. This paper uses the optimal waveband method to obtain the optimal band for mation. This paper uses the optimal waveband method to obtain the optimal band for High- Highland barley extraction in each agricultural division, in order to reduce the data re- land barley extraction in each agricultural division, in order to reduce the data redundancy. dundancy. We select the Optimal band through the optimal index factor (OIF) (Equation We select the Optimal band through the optimal index factor (OIF) (Equation (1)) [36], (1)) [36], which is based on the interrelationship of the information quantity of image which is based on the interrelationship of the information quantity of image bands. It integrates bands. It integrates the standar th d e deviation standard of devi a single ation of band a single and the band corr and elation the between correlation bands. between The higher bands. value The hiof gher OIF va pr lu esents e of OI richer F present combined s richer information combined inf quantity ormatio and n quantity lower redundancy and lower, thus providing a better solution. We select the bands with richer information quantity, redundancy, thus providing a better solution. We select the bands with richer information lower quantity corr , low elater ion, corre distinct lation spectral , distinct dif spec ferences tral di and fferhigher ences and differ hig entiation her differ index entiaas tiothe n index best solution. as the best so Thelformula ution. Th of e fcalculating ormula of calcu OIF is latin shown g OIF asis s follows: hown as follows: 𝑛 𝑛 n n ∑ ∑ 𝑂 = 𝑆 / |𝑅 | (1) 𝑖 =1 𝑖 𝑗 =1 OIF = S / R (1) å i å i j i=1 j=1 where n is the number of image bands, Si is the standard deviation of the reflectance value where n is the number of image bands, S is the standard deviation of the reflectance value of i-th band, and |Rij| is the absolute value of the correlation coefficient of two bands. of i-th band, and |R | is the absolute value of the correlation coefficient of two bands. ij 3.5. Highland Barley Cultivation Extent Restriction 3.5. Highland Barley Cultivation Extent Restriction The terrain of Qinghai–Tibet Plateau slopes from northwest to southeast, represent- The terrain of Qinghai–Tibet Plateau slopes from northwest to southeast, representing ing a strong regional differentiation. Among the seven divisions, there are five Highland- a strong regional differentiation. Among the seven divisions, there are five Highland-barley barley divisions (consisting of ST/FPD, ST/FFD, QG/FPD, QT/APD and CGEQ/HFD), and divisions (consisting of ST/FPD, ST/FFD, QG/FPD, QT/APD and CGEQ/HFD), and two two non-Highland-barley planting divisions (MGN/FPD and SX/FPD). non-Highland-barley planting divisions (MGN/FPD and SX/FPD). We judge the agricultural land through elevation and slope factors based on the to- We judge the agricultural land through elevation and slope factors based on the pography of each division. A Chinese reforestation policy requires that the cultivated land topography of each division. A Chinese reforestation policy requires that the cultivated steeper than 30° should be converted into woodland, because it is not suitable for planting land steeper than 30 should be converted into woodland, because it is not suitable for crops [37]. Therefore, the upper limit of the slopes for growing Highland barley is 30°. The planting crops [37]. Therefore, the upper limit of the slopes for growing Highland barley highest cultivation elevation is different in each division, shown as follows: 3600 m in is 30 . The highest cultivation elevation is different in each division, shown as follows: CGEQ/HFD; 4500 m in QG/FPD; 5000 m in QT/APD; 5500 m in ST/FPD; 5200 m in ST/FFD. 3600 m in CGEQ/HFD; 4500 m in QG/FPD; 5000 m in QT/APD; 5500 m in ST/FPD; Then we turn to the influence of precipitation. The multi-year average precipitation on the 5200 m in ST/FFD. Then we turn to the influence of precipitation. The multi-year average plateau is about 470 mm. There has a significant distribution variability of rainfall on the precipitation on the plateau is about 470 mm. There has a significant distribution variability plateau: it is the most abundant in the southeastern plateau, and least abundant in Qaidam of rainfall on the plateau: it is the most abundant in the southeastern plateau, and least Basin and Qiangtang Plateau (in the north and west of the Qinghai–Tibet Plateau), where abundant in Qaidam Basin and Qiangtang Plateau (in the north and west of the Qinghai– the rainfall cannot meet the needs for Highland barley cultivation. In this study, we re- Tibet Plateau), where the rainfall cannot meet the needs for Highland barley cultivation. move areas with an annual precipitation less than 250 mm, unless they provide irrigation In this study, we remove areas with an annual precipitation less than 250 mm, unless conditions that can satisfy the growing needs of Highland barley. Agricultural areas with they provide irrigation conditions that can satisfy the growing needs of Highland barley. Agricultural satisfactory ir ar rigation eas with con satisfactory ditions conirrigation sist of the conditions oasis agricul consist tural areas of the in oasis Golmu agricultural d, Dulan, ar Uleas an and cert in Golmud, ain places Dulan,in Ulan Qaid and am B certain asin [38] places . in Qaidam Basin [38]. 𝑖𝑗 𝐼𝐹 Land 2021, 10, 1022 7 of 15 Land 2021, 10, x FOR PEER REVIEW 7 of 16 3.6. 3.6. Accuracy Accuracy V Verif erification ication This This paper paper uses uses th the e Co Confusion nfusion M Matrix atrix to to verify verify ththe e acc accuracy uracy of of the the extextraction raction resu re- lts sults [39]. We select QG/FPD as the typical area for field sampling in June 2020. We [39]. We select QG/FPD as the typical area for field sampling in June 2020. We selected 109 selected 109 highland barley sample points and 109 other crop sample points. The major highland barley sample points and 109 other crop sample points. The major sampling sampling process is to obtain the latitude and longitude of each sample point through process is to obtain the latitude and longitude of each sample point through high-preci- high-precision handheld GPS units, measure its slope by Clinometer, take photographs of sion handheld GPS units, measure its slope by Clinometer, take photographs of the land- the landscape nearby for the subsequent verification. The distribution of sample points is scape nearby for the subsequent verification. The distribution of sample points is shown shown in Figure 4. in Figure 4. Figure Figure 4. 4.The The locati location on of of the the sample sample points pointsin in QG/F QG/FPD. PD. 3.7. The Extraction Process of Highland Barley 3.7. The Extraction Process of Highland Barley As shown in Figure 5, this paper adopts the method of zoning, classification and As shown in Figure 5, this paper adopts the method of zoning, classification and multi-element fusion to efficiently perform the high-accuracy extraction. multi-element fusion to efficiently perform the high-accuracy extraction. Land 2021, 10, x FOR PEER REVIEW 8 of 16 Land 2021, 10, 1022 8 of 15 Comprehensive Landsat 8 OLI DEM and precipitation agricultural division Determination of the Data preprocessing Extraction planted range Preset value of the Radiometric correction Agricultural division of elevation, Slope and and Image fusion the Qinghai-Tibet Plateau precipitation Selection of extraction bands and establishment of database Determine the optimal Verify the separability of Establish Highland barley band land types sample data Object-oriented classification method Determine the optimal band Determine the optimal band Accuracy evaluation Zonal Statistics of Highland barley Figure 5. The extraction process of Highland barley in the Qinghai–Tibet Plateau. Figure 5. The extraction process of Highland barley in the Qinghai–Tibet Plateau. 4. Results 4. Results 4.1. Highland Barley Extraction Based on Sample Information 4.1. Highland Barley Extraction Based on Sample Information Under the optimal segmentation scale, the cultivation area of Highland barley has a Under the optimal segmentation scale, the cultivation area of Highland barley has a distinct boundary. Based on the spectral information, geometric and texture characteristics distinct boundary. Based on the spectral information, geometric and texture characteris- of Highland barley cultivation areas and non-Highland barley cultivation areas in each tics of Highland barley cultivation areas and non-Highland barley cultivation areas in division, we construct the Highland barley sample databases for each division. These each division, we construct the Highland barley sample databases for each division. These databases collect spatial and attribute information of the Highland barley, which we use to databases collect spatial and attribute information of the Highland barley, which we use establish a sample dataset of extracted characteristic parameters. Combining the dataset to establish a sample dataset of extracted characteristic parameters. Combining the dataset with the previous field survey results, we use the support vector machine method to classify with the previous field survey results, we use the support vector machine method to clas- the agricultural divisions, then obtain the sample quantity taken from Highland barley sify the agricultural divisions, then obtain the sample quantity taken from Highland bar- in different divisions. In divisions with large patches of Highland barley (QG/FPD), the ley in different divisions. In divisions with large patches of Highland barley (QG/FPD), HB sample quantity at the county level is between 300 and 500, and the non-HB sample the HB sample quantity at the county level is between 300 and 500, and the non-HB sample quantity is between 600 and 800; in divisions with small and continuously distributed quantity is between 600 and 800; in divisions with small and continuously distributed patches of Highland barley (ST/FPD and CGEQ/HFD), the HB sample quantity is between patches of Highland barley (ST/FPD and CGEQ/HFD), the HB sample quantity is between 500 and 700, and the non-Highland barley sample quantity is about 1000; in divisions with 500 and 700, and the non-Highland barley sample quantity is about 1000; in divisions with small and evenly distributed patches of Highland barley (ST/FFD), the HB sample quantity small and evenly distributed patches of Highland barley (ST/FFD), the HB sample quan- is between 600 and 1000, and the non-HB sample quantity is about 1000; in divisions with tity is between 600 and 1000, and the non-HB sample quantity is about 1000; in divisions irregularly distributed patches (QT/APD), the county-level sample quantity of HB and with irregularly distributed patches (QT/APD), the county-level sample quantity of HB non-HB is adjusted according to the case (Appendix A). and non-HB is adjusted according to the case (Appendix A). 4.2. Optimizing the Bands of Highland Barley Extraction in Each Agriculture Area 4.2. Optimizing the Bands of Highland Barley Extraction in Each Agriculture Area According to the calculation, the optimal extraction bands for CGEQ/HFD and According to the calculation, the optimal extraction bands for CGEQ/HFD and ST/FPD are B4, B5 and B7; for QG/FPD and QT/APD are B1, B4 and B5; for ST/FFD are ST/FPD are B4, B5 and B7; for QG/FPD and QT/APD are B1, B4 and B5; for ST/FFD are B1, B1, B5 and B6. Next, we verify whether rapeseed might affect the extraction of Highland B5 and B6. Next, we verify whether rapeseed might affect the extraction of Highland bar- barley after band optimization treatment. To this end, we randomly select 100 typical ley after band optimization treatment. To this end, we randomly select 100 typical samples samples each of Highland barley and rapeseed from each agricultural division, and draw each of Highland barley and rapeseed from each agricultural division, and draw the op- the optimal band spectral characteristic curve (Figure 6). The results show that, in each timal band spectral characteristic curve (Figure 6). The results show that, in each agricul- agricultural division, Highland barley has a distinct difference with other major crops in tural division, Highland barley has a distinct difference with other major crops in reflec- reflectance spectra after the optimal wavebands, revealing a fine discrimination index. tance spectra after the optimal wavebands, revealing a fine discrimination index. Land 2021, 10, x FOR PEER REVIEW 9 of 16 Land 2021, 10, x FOR PEER REVIEW 9 of 16 Land 2021, 10, 1022 9 of 15 Figure 6. Surface reflectance curves of Highland barley and rapeseed in various agricultural divisions. Figure 6. Surface reflectance curves of Highland barley and rapeseed in various agricultural divisions. Figure 6. Surface reflectance curves of Highland barley and rapeseed in various agricultural divisions. 4.3. The Extraction Results of Highland Barley 4.3. The Extraction Results of Highland Barley The extraction results indicate that the total cultivation area of Highland barley on 4.3. The Extraction Results of Highland Barley the plateau is 2.74 × 10 ha, and it mainly grows in the eastern and southern plateau (Figure The extraction results indicate that the total cultivation area of Highland barley on The extraction resul5ts indicate that the total cultivation area of Highland barley on 7). We sort the 5 agricultural divisions from the largest to the smallest in terms of Highland the plateau is 2.74 10 ha, and it mainly grows in the eastern and southern plateau the plateau is 2.74 × 10 ha, and it mainly grows in the eastern and southern plateau (Figure (Figure 7). We barl sort ey the cultivat 5 agricultural ion area: QG divisions /FPD, ST/FPD from the , ST/FFD, largest to QT the /APD, smallest and CG in terms EQ/HFD, of accounting 7). We sort the 5 agricultural divisions from the largest to the smallest in terms of Highland Highland barley for 31. cultivation 09%, 28.91%, area: 23 QG/FPD, .23%, 11.86% ST/FPD, and ST/FFD, 4.91%, re QT/APD, spectively, and of th CGEQ/HFD, e total area of each divi- barley cultivation area: QG/FPD, ST/FPD, ST/FFD, QT/APD, and CGEQ/HFD, accounting sion. accounting for 31.09%, 28.91%, 23.23%, 11.86% and 4.91%, respectively, of the total area of for 31.09%, 28.91%, 23.23%, 11.86% and 4.91%, respectively, of the total area of each divi- each division. sion. Figure 7. The extraction results of Highland barley on the Qinghai–Tibet Plateau. Figure 7. The extraction results of Highland barley on the Qinghai–Tibet Plateau. Figure 7. The extraction results of Highland barley on the Qinghai–Tibet Plateau. Land 2021, 10, 1022 10 of 15 4.4. Results of Accuracy Verification The confusion matrix was used to evaluate the accuracy of Highland barley extraction results. Table 3 shows that the accuracy of extraction results is relatively good, with Kappa coefficient up to 0.83 and Producer ’s Accuracy and User ’s Accuracy both over 90%. Table 3. Evaluation of the accuracy of Highland barley extraction results. Producer’s User’s Producer’s User’s Overall Kappa Type Accuracy Accuracy Accuracy Accuracy Accuracy Coefficient (Pixels) (Pixels) (%) (%) (%) Highland barley 99/109 99/107 90.83 92.52 91.74 0.83 Other crops 101/109 101/111 92.66 90.99 5. Discussion 5.1. Classification and Statistics of Highland Barley Cultivation Area We count the Highland barley cultivation area in five agricultural divisions at different elevation ranges, as shown in Table 4. We divide it into eight ranges at 500 m intervals. In general, 5200 m is the current upper limit of elevation for growing Highland barley, and the elevation range of 2500–4500 m concentrates the most patches, which accounts for 94.43% of the total Highland barley planting area on the plateau. Within the elevation range lower than 2500 m, its cultivation land accounts for 4.84%. By contrast, the elevation range above 4500 m only accounts for 0.73%. Table 4. Statistics of Highland barley planting areas in various agricultural divisions. Agricultural Divisions CGEQ/HFD ST/FPD ST/FFD QT/APD IMGN/FPD Proportion of Highland barley 31.09% 28.91% 23.23% 11.86% 4.91% planting areas <2000 m 0.29% 0.00% 1.72% 0.08% 2.20% Proportion of 2000 to 2500 m 2.24% 0.00% 6.82% 0.73% 37.33% Highland 2500 to 3000 m 32.05% 0.14% 14.42% 3.86% 52.84% barley planting 3000 to 3500 m 65.40% 0.46% 20.29% 22.19% 7.62% areas in 3500 to 4000 m 0.02% 46.51% 32.99% 45.74% 0.00% different 4000 to 4500 m 0.00% 50.68% 23.45% 27.13% 0.00% elevation 4500 to 5000 m 0.00% 2.16% 0.32% 0.27% 0.00% ranges 5000 m 0.00% 0.05% 0.00% 0.00% 0.00% Proportion of <5 87.97% 76.28% 18.24% 40.14% 66.06% Highland 5 to 10 6.69% 14.53% 19.70% 18.94% 21.95% barley planting 10 to 15 2.38% 5.21% 16.19% 13.44% 8.10% area in different 15 to 20 1.08% 2.08% 13.35% 9.66% 1.99% slope ranges 20 1.87% 1.91% 32.53% 17.82% 1.90% We divide the slope gradient of each division into 5 ranges, including <5 , 5 –10 , 10 –15 , 15 –20 , 20 , and count the cultivation area at each slope range in 5 agricultural divisions. Overall, in QG/FPD, ST/FPD and CGEQ/HFD, most Highland barley patches are at the slope of <5 , accounting for 87.97%, 76.28% and 66.06% of each division’s total agricultural area, respectively. In these three divisions, only a small proportion of HB patches is at a slope of 20 , accounting for 1.87%, 1.91% and 1.90%, respectively. In QT/APD, most HB patches are at a slope of <5 , accounting for 40.13%; that at a slope of 20 accounts for 17.82%, and the least are at the slope range of 15 –20 , accounting for 9.66%. In ST/FFD, the HB patches at the slope range of <5 , 5 –10 , 10 –15 , 15 –20 accounts for 18.24%, 19.70%, 16.19% and 13.35%, respectively, showing an even distribution at the slope below 20 ; the slope range of 20 has the largest amount of cultivation areas, accounting for 32.52%. These distribution characteristics might be related to the fragmentation degree of the land patch in this division. Land 2021, 10, x FOR PEER REVIEW 11 of 16 Land 2021, 10, 1022 11 of 15 5.2. The Relationship between Highland Barley and Elevation 5.2. The Relationship between Highland Barley and Elevation Due to strong spatial heterogeneity, each of the divisions has clearly different distri- Due to strong spatial heterogeneity, each of the divisions has clearly different distribu- bution patterns of Highland barley, as shown in Figure 8. QG/FPD has the largest High- tion patterns of Highland barley, as shown in Figure 8. QG/FPD has the largest Highland land barley cultivation area, and 65.40% of them occurs at the elevation of 3000–3500 m. barley cultivation area, and 65.40% of them occurs at the elevation of 3000–3500 m. In In QG/FPD, most of the Highland barley is continuously distributed in river valleys, QG/FPD, most of the Highland barley is continuously distributed in river valleys, moun- mountain basins and flat slopes along the plateau surface (Figure 8e). In Qaidam Basin, tain basins and flat slopes along the plateau surface (Figure 8e). In Qaidam Basin, Highland Highland barley cultivation areas are found in the oasis agricultural zone (Figure 8c). In barley cultivation areas are found in the oasis agricultural zone (Figure 8c). In ST/FPD, ST/FPD, Highland barley is mostly planted in Yarlung Zangbo River Valley (Figure 8g) Highland barley is mostly planted in Yarlung Zangbo River Valley (Figure 8g) where there where there has fine hydrothermal condition and continuous and flat land. In this divi- has fine hydrothermal condition and continuous and flat land. In this division, most HB sion, most HB patches are at the range of 3500–4500 m, up to 5000 m or higher, making it patches are at the range of 3500–4500 m, up to 5000 m or higher, making it the only area the only area that can have Highland barley grown above 5000 m a.s.l. on the plateau, that can have Highland barley grown above 5000 m a.s.l. on the plateau, though most of though most of these patches are small and scattered along the valley (Figure 8f). In these patches are small and scattered along the valley (Figure 8f). In ST/FFD and QT/APD, ST/FFD and QT/APD, the HB patches are at the range of 3000–4500 m a.s.l., most in frag- the HB patches are at the range of 3000–4500 m a.s.l., most in fragmented patches along the men river ted valleys patch(Figur es along e 8a,d,h); the river CG va EQ/HFD lleys (Figis ure the 8a,d,h area);at CG the EQ/ lowest HFD elevation is the area and at th has e low- the est smallest elevation barley and cultivation has the smal area les on t ba the rle plateau y cultiva . In tion this ar division, ea on the Highland plateau. barley In this is division, mainly Hig distributed hland ba at rley the is range mainly of di 2000–3000 stributed m at a.s.l., the ra and nge mostly of 2000in –3000 fragmented m a.s.l., patches and mostly of the in fragmen valley atted p highatches o elevation f th (Figur e valley e 8b). at high elevation (Figure 8b). Figure 8. Distribution patterns of Highland barley under different elevation and different hydrological elements. Figure 8. Distribution patterns of Highland barley under different elevation and different hydrological elements. The above results indicate that Highland barley cultivation areas show various dis- The above results indicate that Highland barley cultivation areas show various dis- tribution characteristics at different elevation ranges, because each division varies in the tribution characteristics at different elevation ranges, because each division varies in the terrain, temperature, moisture, light, distribution range of arable land, and irrigation con- terrain, temperature, moisture, light, distribution range of arable land, and irrigation ditions. For example, the elevation of 3500 m is the upper limit in CGEQ/HFD, while in conditions. For example, the elevation of 3500 m is the upper limit in CGEQ/HFD, while ST/FPD, the area at the elevation above 3500 m concentrates most of the HB cultivation in ST/FPD, the area at the elevation above 3500 m concentrates most of the HB cultiva- areas (Figure 9). tion areas (Figure 9). Land 2021, 10, x FOR PEER REVIEW 12 of 16 Land 2021, 10, 1022 12 of 15 Figure 9. The elevation interval of Highland barley cultivation and each agricultural division. Figure 9. The elevation interval of Highland barley cultivation and each agricultural division. 5.3. Ways to Improve the Accuracy of Remote Sensing Extraction of Highland Barley Area 5.3. Ways to Improve the Accuracy of Remote Sensing Extraction of Highland Barley Area The accuracy of Highland barley extraction results is limited by three factors: the The accuracy of Highland barley extraction results is limited by three factors: the fragmentation degree of each division, the asynchrony of the growing period of Highland fragmentation degree of each division, the asynchrony of the growing period of Highland barley in each division, and the similarity between other crops and Highland barley in barley in each division, and the similarity between other crops and Highland barley in spectral characteristics. We could improve the accuracy of Highland barley extraction in spectral characteristics. We could improve the accuracy of Highland barley extraction in further studies in following aspects. First, improve the spatial resolution of remote sensing further studies in following aspects. First, improve the spatial resolution of remote sensing images [40]. At the present stage, using the spatial resolution of 15 m is difficult to identify images [40]. At the present stage, using the spatial resolution of 15 m is difficult to identify Highland barley in certain regions due to various fragmentation degrees. Second, optimize Highland barley in certain regions due to various fragmentation degrees. Second, opti- the temporal resolution of Highland barley extraction. The broad span causes various mize the temporal resolution of Highland barley extraction. The broad span causes vari- growth period of Highland barley in each division. It would be better to count the growing ous growth period of Highland barley in each division. It would be better to count the period of the Highland barley in each division, and select the barley-extraction optimal growing period when period itsof spectral the High characteristics land barley in are each distinct divisfr ion, om and other selec crops. t the Thir barle d, y optimize -extraction the op extraction timal perr io esults d when its sp by restricting ectral suitable characteris conditions tics are dist for Highland inct from ot barley her crop [41]. s. In Ththis ird, opti- paper, we restrict the Highland barley distribution from the aspects of elevation and slope, and mize the extraction results by restricting suitable conditions for Highland barley [41]. In th further is paper studies , we restrict can discuss the Hig accumulated hland barley temperatur distribution e, sunshine from the duration, aspects of etc. elevation and slope, and further studies can discuss accumulated temperature, sunshine duration, etc. 6. Conclusions 6. Con T clus his p ions ape r uses OIF images, selects the best bands extracted from Highland barley, and takes the object-oriented classification method to extract the Highland barley cultivation area This paper uses OIF images, selects the best bands extracted from Highland barley, on Qinghai–Tibet Plateau. Based on the above analysis, we draw the following conclusions: and takes the object-oriented classification method to extract the Highland barley cultiva- We propose a multi-factor fusion based on sub-region classification method for effi- tion area on Qinghai–Tibet Plateau. Based on the above analysis, we draw the following ciently extracting Highland barley images. Depending on the agricultural divisions, we conclusions: use multiple factors (including elevation, slope, rainfall and hydrology) to restrict the We propose a multi-factor fusion based on sub-region classification method for effi- extent of Highland barley’s pattern spots; we use image segmentation technology to build ciently extracting Highland barley images. Depending on the agricultural divisions, we division-based Highland barley extraction samples; we use Gauss radial basis kernel in use multiple factors (including elevation, slope, rainfall and hydrology) to restrict the ex- support vector machines (SVMs) to obtain the extraction results with superior accuracy. tent of Highland barley’s pattern spots; we use image segmentation technology to build According to the extraction results, the total Highland barley cultivation area of division-based Highland barley extraction samples; we use Gauss radial basis kernel in 5 divisions is about 2.74 10 ha. We draw the first distribution map of Highland barley support vector machines (SVMs) to obtain the extraction results with superior accuracy. cultivation area on Qinghai–Tibet Plateau based on the data. The map shows that the largest According to the extraction results, the total Highland barley cultivation area of 5 amount of Highland barley is grown in the eastern and southern plateau. Through the map, divisions is about 2.74 × 10 ha. We draw the first distribution map of Highland barley we could learn the spatial distribution pattern of Highland barley from the patch scale cultivation area on Qinghai–Tibet Plateau based on the data. The map shows that the larg- (previously from the administrative unit scale). On this basis, we could conduct in-depth est amount of Highland barley is grown in the eastern and southern plateau. Through the studies on the distribution of HB suitable areas under different scenarios, which might map, we could learn the spatial distribution pattern of Highland barley from the patch optimize the Highland barley’s spatial distribution and provide reference indicators for scale (previously from the administrative unit scale). On this basis, we could conduct in- the quantitative assessment of its disaster exposure. At the same time, it can provide data Land 2021, 10, 1022 13 of 15 references for other researches about Qinghai–Tibet Plateau, such as future development of characteristic agriculture, security of state grain reserves and decision-making in response to climate change. The distribution of Highland barley cultivation land shows a distinct spatial hetero- geneity in different elevation ranges. Overall, Highland barley is distributed in the range of 2500–4500 m, up to 5200 m. When at the same elevation, the factors that affect Highland barley’s growing (such as topography, temperature, moisture, light, distribution of arable land and irrigation conditions) vary in each division, and therefore the cultivation scale is different to each other. At the same elevation, the Highland barley in CGEQ/HFD is planted on the fragmented and steep sloping lands that have the farthest distance from the river, while in ST/FPD, it is planted on the continuous and flat lands in the center of river valleys. Author Contributions: W.M. conducted the research, analyzed the data and wrote the paper; W.J. processed the data; F.L. guided the research and extensively updated the manuscript; J.W. conceived the research and provided project support; P.S. and X.F. helped process the data. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Second Qinghai–Tibet Plateau Scientific Expedition and Research Program (STEP), grant number 2019QZKK0606 and the National Key Research and Development Program of China, grant number 2016YFA0602402. Data Availability Statement: All data and materials are available upon request. Acknowledgments: We are particularly indebted to Peijun Shi and Xingsheng Xia from the Qinghai Normal University for their constructive suggestions on an earlier draft of this paper. Conflicts of Interest: The authors declare no conflict of interest. Appendix A. Highland Barley Extraction Area of Counties in Different Divisions Table A1. CGEQ/HFD. County Area (ha) County Area (ha) County Area (ha) Datong 1379.34 Ledu 812.67 Huzhu 3496.05 Hualong 4564.58 Tongren 1200.35 Xiahe 1258.40 Jianzha 380.07 Xunhua 389.99 Table A2. QT/APD. County Area (ha) County Area (ha) County Area (ha) Aba 4722.81 Pulan 498.01 Maerkang 642.17 Baqing 34.34 Rangtang 1079.89 Nima 31.72 Biru 1566.59 Ritu 173.12 Heishui 609.99 Gaier 133.97 Zoige 762.80 Jiali 199.64 Geji 12.83 Saga 225.09 Zhada 212.88 Yushu 2489.11 Songpan 583.56 Table A3. QG/FPD. County Area (ha) County Area (ha) County Area (ha) Delingha 1143.29 Huangzhong 1181.14 Haiyan 938.81 Diebu 1151.98 Lintan 1296.00 Hezuo 2760.95 Dulan 8187.53 Luqu 1046.70 Huangyuan 622.89 Gangcha 100.00 Menyuan 11,297.36 Zeku 1084.57 Golmud 1196.64 Qilian 1085.78 Zhouqu 1434.17 Gonghe 14,105.70 Tongde 5754.35 Zhuoni 861.86 Guide 626.45 Wulan 753.25 Guinan 17,148.20 Xinghai 5406.80 Land 2021, 10, 1022 14 of 15 Table A4. ST/FFD. County Area (ha) County Area (ha) County Area (ha) Basu 1578.10 Gongbujiangda 2032.27 Luolong 3051.00 Batang 1340.96 Gongjue 2706.73 Muli 1161.31 Bayi 240.93 Gongshan 229.10 Seda 721.10 Baiyu 3040.79 Jiacha 995.60 Suo 1433.10 Bianba 3088.20 Jiangda 4119.39 Weixi 1205.71 Bomi 1445.40 Jiulong 143.24 Xiangcheng 878.09 Chaya 2920.20 Karuo 4125.24 Xianggelila 3660.54 Chayu 892.10 Kangding 2119.79 Xinlong 2324.57 Danba 257.11 Lanping 237.42 Yajiang 1544.52 Daofu 2843.57 Langxia 594.45 Mangkang 2151.20 Daocheng 1835.31 Leiwuqi 2838.30 Milin 785.30 Derong 720.70 Litang 2569.01 Motuo 94.50 Deqin 791.55 Luhuo 2890.85 Yulong 919.35 Dingqing 6123.15 Lushui 532.73 Zuogong 1976.40 Table A5. ST/FPD. County Area (ha) County Area (ha) County Area (ha) Angren 3988.97 Jilong 672.89 Nimu 1325.03 Bailang 5316.98 Jiangzi 6008.92 Nielamu 1275.54 Banma 246.60 Kangma 2112.89 Qiongjie 893.18 Chengguan 266.38 Lazi 4372.94 Qushui 1699.76 Cuomei 662.16 Langkazi 1575.53 Qusong 800.33 Cuona 915.95 Linzhou 5714.82 Renbu 1157.63 Dazi 1357.72 Longzi 1458.27 Sajia 4146.10 Dingjie 2136.42 Maqin 435.15 Sangri 361.28 Dingri 6427.01 Mozhugongka 2373.86 Sangzhuzi 6069.92 Duilongdeqing 2637.92 Naidong 941.37 Xietongmen 1641.20 Gangba 790.47 Nanmulin 4322.75 Yadong 194.78 Gongga 2296.08 Nangqian 2432.18 Zhanang 1868.76 References 1. Wang, R.; Jiang, Y.; Zhang, A.; Gao, Y.; Wang, J. Review on crop exposure of natural disasters. J. Catastr. 2019, 34, 215–221. [CrossRef] 2. Xu, M. Study on World Barley Trade Pattern and Its Influence on Chinese Barley Industry. Ph.D. Thesis, Chinese Academy of Agricultural Sciences, Beijing, China, 2013. 3. Meng, J.; Li, H.; Zhang, Q.; Li, M.; Liu, S. 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Journal
Land – Multidisciplinary Digital Publishing Institute
Published: Sep 29, 2021
Keywords: Highland barley; Qinghai–Tibet Plateau; cultivation area extraction; object-oriented extraction method; spatial distribution of Highland barley