Cropland inventory in the command area of Krishnarajasagar project using satellite data

Inter-state disputes on water utilization from the Cauvery river have necessitated an accurate and timely estimate of the irrigated crop area in each season to optimise the water supply through the canal system and to suggest a departure from the traditional system of protective kharif irrigation to productive rabi irrigation. An inventory of the cropland in the command area of Krishnarajasagar Project was made by visually interpreting satellite imagery. Satellite data showed a reduction of about 6 per cent in the irrigated area from 1973 to 1986. Effect of scale of imagery and percentage of boundary pixels on accuracy of estimated irrigated area was studied. 1:250000 scale was found to be adequate for this large command area. The accuracy of estimated irrigated crop area was within 5 per cent of the DES estimate for the entire command area. At taluk-level, total enumeration gave an overestimate of irrigated area by 13 per cent compared to an overestimation of about 18 per cent using multistage-proportionate-probability sampling with a grid size of 2-5 km by 2-5 km. The per hectare cost of estimating the crop land using satellite remote sensing was found to be two to five times cheaper than the conventional system. An attempt was also made to evaluate various vegetation indices derived from Landsat-TM for identifying irrigated croplands and IRS-1A/LISS-II data for identification of crop types. It was concluded, from the results of this study, that satellite remote sensing is an effective time saving techqniue for providing seasonal assessment of irrigated cropland in the command areas.     

Improved ground sampling and crop yield estimation using satellite data

The existing procedures for crop yield estimation involve Crop Cutting Experiments ( CCEs) conducted during harvesting time in the plots selected based on a pre-designed sampling scheme using available ground data. These ground sampling designs do not consider the crop condition which is directly related to the yield during the season, for stratification and subsequent sample selection leading to biased distribution of plots. Moreover these experiments are capable of providing estimates only at larger areal units such as the total command area. Hence there is a need to improve the sampling design to achieve more accurate estimates. An alternate methodology exploiting the information on crop area and crop condition, derived from satellite remote sensing data on near real-time basis, for improving the ground sampling design has been proposed in this paper. The methodology is demonstrated in the Davangere and Malebennur divisions of the Bhadra project command area to estimate the average yield of paddy during Rabi 1992-93. The results obtained from conventional methodology and the improved procedure showed that the latter has increased the accuracy of estimates. The yield values obtained from CCE plots have been regressed with corresponding Normalized Difference Vegetation Index (NDVI) statistics and thus the derived paddy yield model is capable of providing the yield estimates at smaller area 1 units, such as within distributary command.     

Water balance study and conjunctive water use planning in an irrigation canal command area: A remote sensing perspective

Water budgeting of the D-36 and D-36 A distributaries confined between Pedda Vagu, Korutla Vagu and Kakatiya main canal of the Sri RamSagar Project (SRSP) Command area was conducted using remote sensing derived crop areas, land cover information, irrigation tank inventory and source-wise distribution of irrigated areas, together with conventional meteorological, canal flows and well inventory data. A semi-empirical water balance model was developed and validated using remote sensing derived objective information of the command area and the validated model used for predicting the groundwater table under normal rainfall conditions. Recharge and water balance in the study area indicated that the net recharge to the aquifer is negative to the tune of 2.54 Mm³ resulting in a fall of the groundwater table by 0.79 m during 1992-93. However, normalized groundwater recharge and water balance estimates indicate an impending waterlogging problem with an annual groundwater table rise of 0.35 m. In view of existing water management practices, a conjunctive water use plan of rotational operation of aquifers and canals is suggested.     

Use of high‐resolution satellite data for the structural and agricultural inventory of tank irrigation systems

Tanks are small storage reservoirs impounding the runoff from monsoon rains to regulate the supply of water mainly for irrigated command areas that are typically less than 200 ha. They account for one‐third of the irrigated areas in Tamil Nadu, Karnataka and Andhra Pradesh. Years of neglect and indifference in tank maintenance and management have eroded their functional efficiency and jeopardized their multifarious benefits. In Tamil Nadu, this has resulted in a decline in their contribution to irrigation from 40% in 1995 to 25% in 2000. The modernization of these tanks requires prioritization and investment. Remote sensing technology, with its unique advantages and the latest high‐resolution sensors, can provide the information on the agricultural, hydrological and structural conditions of the tank irrigation systems necessary for prioritization. The National Remote Sensing Centre (NRSC) has carried out a study of the Nanjur tank cascade in Tamil Nadu using high‐resolution data from the IKONOS satellite during the crop season of 2003–2004. This study demonstrated the use of high‐resolution satellite images to obtain an inventory of the different components of a tank irrigation system such as tank bunds, surplus weirs, supply channels and distribution networks. It was also found useful in updating the road–rail network at village level. The 1‐m merged satellite data were useful in mapping open wells and minor roads in a tank cascade. The cropping pattern in a tank system can be mapped at cadastral level using these images, which will be useful for micro‐level water and agricultural management. The 4‐m multispectral image was found to be sufficient for mapping different crops at field level. The high‐resolution image also provided information on intrafield variability in crop condition. The reliability and cost‐effectiveness of high‐resolution images from Indian satellites provide scope for the generation of information for tank system studies as well as for micro‐level natural resource management.     

Performance assessment of water resource project using remote sensing and a Geographical Information System - a case study

Rapid assessment of the capacity utilization of water resource project (catchment, reservoir and command area) is required to meet existing and future demands. Dynamic information derived from temporal satellites and spatial ground based data will give an accurate data. A methodology for quick appraisal has been developed. It was tested over the Bhogawati River basin in India. Spatial distribution of degraded areas in the catchment and potential cultivation areas in command areas were demarcated, in addition to the growth in the crop area during the past 30 years.     

Mapping paddy rice agriculture in southern China using multi-temporal MODIS images

Information on the area and spatial distribution of paddy rice fields is needed for trace gas emission estimates, management of water resources, and food security. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of surface water and rice crops) exists. The Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite has visible, near infrared and shortwave infrared bands; and therefore, a number of vegetation indices can be calculated, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) that is sensitive to leaf water and soil moisture. In this study, we developed a paddy rice mapping algorithm that uses time series of three vegetation indices (LSWI, EVI, and NDVI) derived from MODIS images to identify that initial period of flooding and transplanting in paddy rice fields, based on the sensitivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting. We ran the algorithm to map paddy rice fields in 13 provinces of southern China, using the 8-day composite MODIS Surface Reflectance products (500-m spatial resolution) in 2002. The resultant MODIS-derived paddy rice map was evaluated, using the National Land Cover Dataset (1:100,000 scale) derived from analysis of Landsat ETM+ images in 1999/2000. There were reasonable agreements in area estimates of paddy rice fields between the MODIS-derived map and the Landsat-based dataset at the provincial and county levels. The results of this study indicated that the MODIS-based paddy rice mapping algorithm could potentially be applied at large spatial scales to monitor paddy rice agriculture on a timely and frequent basis.     

Remote sensing and GIS for estimation of irrigation crop water demand

Satellite images supported by global positioning systems (GPS) and field visits were used to identify the cropping pattern of a large irrigation scheme in Central Asia. Two methods were used to estimate the crop evapotranspiration (ET). In the first, the ETs of the different crops were calculated from local field climatic data using the Penman–Monteith method of calculating crop water requirements as used in the Food and Agriculture Organization (FAO) CropWat programme. The satellite data were transferred to a geographical information system (GIS) and the area of each crop type was identified. Combining the two sets of data gave an estimate of ET and total evaporative water demand for each crop. ET was also calculated directly from the satellite data using a modified sensible heat flux approach (SEBAL). The Penman–Monteith approach estimated the ET to be 5.7, 3.3, 4.4 and 6.3?mm?d^?1 for cotton, mixed crop, alfalfa and rice respectively, whereas the ET estimated from the satellite data were 4.4, 3, 3.2 and 5.3?mm?d^?1, respectively. The possible causes of these differences are discussed. The FAO Penman–Monteith methodology for estimating crop water requirements is best for planning purposes but the SEBAL approach is potentially more useful for management in that it establishes the amount of water being used by the crop and can help identify where water is being wasted.     

Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images

In this paper, we developed a new geospatial database of paddy rice agriculture for 13 countries in South and Southeast Asia. These countries have ∼ 30% of the world population and ∼ 2/3 of the total rice land area in the world. We used 8-day composite images (500-m spatial resolution) in 2002 from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the NASA EOS Terra satellite. Paddy rice fields are characterized by an initial period of flooding and transplanting, during which period a mixture of surface water and rice seedlings exists. We applied a paddy rice mapping algorithm that uses a time series of MODIS-derived vegetation indices to identify the initial period of flooding and transplanting in paddy rice fields, based on the increased surface moisture. The resultant MODIS-derived paddy rice map was compared to national agricultural statistical data at national and subnational levels. Area estimates of paddy rice were highly correlated at the national level and positively correlated at the subnational levels, although the agreement at the national level was much stronger. Discrepancies in rice area between the MODIS-derived and statistical datasets in some countries can be largely attributed to: (1) the statistical dataset is a sown area estimate (includes multiple cropping practices); (2) failure of the 500-m resolution MODIS-based algorithm in identifying small patches of paddy rice fields, primarily in areas where topography restricts field sizes; and (3) contamination by cloud. While further testing is needed, these results demonstrate the potential of the MODIS-based algorithm to generate updated datasets of paddy rice agriculture on a timely basis. The resultant geospatial database on the area and spatial distribution of paddy rice is useful for irrigation, food security, and trace gas emission estimates in those countries.     

Deriving cropping system performance indices using remote sensing data and GIS

The cropping system approach is a holistic management of variant and invariant resources to optimize the food production. Various indices are used to assess and evaluate the efficiency and sustainability of the systems. These indices are generally computed from the data collected by traditional survey methods that are time consuming and non‐spatial. An attempt has been made to derive such indices using satellite remote sensing data for the state of West Bengal, India. Three indices—Multiple Cropping Index (MCI), Area Diversity Index (ADI) and Cultivated Land Utilization Index (CLUI)—were attempted. Multi‐date, multisensor data from Indian Remote Sensing Satellite (IRS) and Radarsat Synthetic Aperture Radar (SAR) were used to derive cropping pattern, crop rotation, and crop calendar. Crop type, acreage, rotation and crop duration were used as inputs to compute the indices at district and state level. The indices were categorized as high, medium and low to evaluate the performance of each of the 16 districts. The average MCI of the state derived was 140. At district level it varied from 104 to 177. The average ADI of state was 2.5 and varied from 1.5 to 5.0.     

Spatiotemporal modelling of methane flux from the rice fields of India using remote sensing and GIS

Rice fields have been accredited as an important source of anthropogenic methane, with estimates of annual emission ranging from 47 to 60 Tg per year, representing 8.5–10.9% of total emission from all sources. In this study, attempts have been made to derive the spatial and temporal pattern of methane emitted from the rice lands of India using an integrated methodology involving satellite remote sensing and geographic information system (GIS) techniques. Multidate SPOT VGT 10‐day Normalized Difference Vegetation Index (NDVI) composite data for a complete year were used to map the rice area, delineate single‐ and double‐cropped rice areas, crop calendar and growth stages. Rainfall, digital elevation and irrigation data were integrated to stratify the rice area into distinct categories related to methane emission. Preliminary analysis of the methane emission pattern was carried out using published values. The results show that around 91% of total methane emission results from wet‐season rice, contributing 4.66 Tg per year. The temporal pattern shows that August and September are the months of peak emission during the wet season, and March and April during the dry season.     

Ecological studies in the Ukai command area

The present study was directed towards studying the impact of the Ukai-Kakrapar irrigation project on the ecology of the command area with particular reference to changes in cropping pattern and land degradation due to waterlogging/salinity. The data used were multitemporal (1972-1981) LANDSAT imagery of the entire command area, multitemporai colour infrared plus black-and-white aerial photography and multispectral scanner data over a test area of about 1200 km² collected from November 1980 to February 1982. Land-use maps for the entire command area at 1:250 000 scale (LANDSAT) and land-use/cropping-pattern maps for the test area at 1:12 500 scale (aerial photography) have been prepared. The results indicate that due to the introduction of large-scale irrigation, the cropping pattern has changed and the acreage under heavy perennial crops such as sugar-cane and banana has increased beyond permissible limits resulting in a rapid rise in the water-table in the area. The areas delineated as waterlogged and salt-affected from the aerial and LANDSAT imagery, when correlated with the subsoil water-table data, were found to have the water-table within 0-1 5 to 1-5-3-0 m.     

基于STM32的连栋温室精准灌溉控制系统

以STM32F103芯片为控制核心,选用FDR型土壤水分传感器测定作物当前生长期的土壤水分含量,结合当前作物所需要的土壤含水量,实时计算出差值,给出当前作物所需要的灌溉量,通过PC机给出灌溉命令,控制恒压变频柜的运行和灌溉电磁阀的开关。同时控制器能将变频器和各个电磁阀的运行状态、当前灌溉量和实时的土壤含水量传输给PC机。该系统不仅能精确的自动控制灌溉,而且可以根据种植者的要求提供灌溉。该系统能实现多个温室的精准灌溉自动控制,具有结构简单、成本低和可靠性高,对于实现节水灌溉和发展高效农业具有指导意义。     

Modeling surface water-groundwater interaction in arid and semi-arid regions with intensive agriculture

In semi-arid and arid areas with intensive agriculture, surface water-groundwater (SW-GW) interaction and agricultural water use are two critical and closely interrelated hydrological processes. However, the impact of agricultural water use on the hydrologic cycle has been rarely explored by integrated SW-GW modeling, especially in large basins. This study coupled the Storm Water Management Model (SWMM), which is able to simulate highly engineered flow systems, with the Coupled Ground-Water and Surface-Water Flow Model (GSFLOW). The new model was applied to study the hydrologic cycle of the Zhangye Basin, northwest China, a typical arid to semi-arid area with significant irrigation. After the successful calibration, the model produced a holistic view of the hydrological cycle impact by the agricultural water use, and generated insights into the spatial and temporal patterns of the SW-GW interaction in the study area. Different water resources management scenarios were also evaluated via the modeling. The results showed that if the irrigation demand continuous to increase, the current management strategy would lead to acceleration of the groundwater depletion, and therefore introduce ecological problems to this basin. Overall, this study demonstrated the applicability of the new model and its value to the water resources management in arid and semi-arid areas.     

Cropping pattern of Uttar Pradesh using IRS-P6 (AWiFS) data

The cropping pattern (rotation) of a region depends on the soil, water availability, economic conditions and climatic factors. Remote sensing is one of the effective tools that can provide precise and up-to-date information on the performance of agricultural systems. Four seasons data from the Indian Remote Sensing Satellite (IRS)-P6 Advanced Wide Field Sensor (AWiFS) were used for the generation of the cropping pattern of Uttar Pradesh by geographic information system (GIS)-aided integration of digitally classified crop and land use inventories of the kharif, rabi and zaid crop seasons. Twelve different cropping patterns were delineated and mapped in the Indo-Gangetic plain of Uttar Pradesh. The forests covered about 6.32% of the total geographical area. The net cropped area was 20 282 159.46 ha (84.18% of the total geographical area) and the non-agricultural area observed was 3 437 376.00 ha (14.26% of the total geographical area). Rice was the single most dominant crop of the state, occupying about 32.94% of the total geographical area during the kharif season. Maize/jowar was the second major cereal crop, accounting for 13.77% of the total geographical area of the state. The major crops grown during the rabi season were wheat and pulses/oilseed, covering areas of 7 979 267.71 ha (33.12%) and 5 974 742.58 ha (24.80%), respectively. Rice-wheat, sugarcane and rice-pulses were the major cropping patterns, occupying about 3 958 739.85 ha (16.43%), 3 609 939.74 ha (14.98%) and 2 511 298.24 ha (10.42%), respectively. The areas under pulses/oilseed were significantly higher in the rabi season. Sugarcane-wheat and pulses shared an almost equal area (6.49%). The maize/jowar-wheat cropping pattern occupied 6.14% of the total geographical area of the state. Single cropping patterns (i.e. rice-fallow, fallow-pulses, fallow-wheat, maize-fallow and sugarcane-fallow) were minor, occupying 6.08, 2.94, 4.06, 2.69 and 2.51%, respectively. Waste land, including gulley, salt-affected, waterlogged and rock land, accounted for 3.80% of the total geographical area. The results of this study indicate that temporal IRS-P6 (AWiFS) data are very useful for studying spatial cropping patterns. The values of the Multiple Cropping Index (MCI) and the Cultivated Land Utilization Index (CLUI) show that the study area has a high cropping intensity.     

IRRIGATE: A dynamic integrated model combining a knowledge-based model and mechanistic biophysical models for border irrigation management

Water management practices in southern France (the Crau plain) need to be modified in order to ensure greater water use efficiency and less environmental damage while maintaining hay production levels. Farmers, water managers and policy makers have expressed the need for new methods to deal with these issues. We developed the biodecisional model IRRIGATE to test new irrigation schedules, new designs for water channels or fields and new distribution planning for a given water resource. IRRIGATE simulates the operation of a hay cropping system irrigated by flood irrigation and includes three main features: (i) border irrigation with various durations of irrigation events and various spatial orders of water distribution, (ii) species-rich grasslands highly sensitive to water deficit, (iii) interactions between irrigation and mowing. It is based on existing knowledge, adapted models and new modules based on experiments and survey data. It includes a rule-based model on the farm scale, simulating dynamically both irrigation and mowing management, and two biophysical models. The two biophysical models are a dynamic crop model on the field scale simulating plant and soil behaviour in relation to water supply, and a flood irrigation model on the border scale simulating an irrigation event according to plant and hydraulic parameters. Model outputs allow environmental (water supply, drainage), social (labour) and agronomic (yields, water productivity and irrigation efficiency) analyses of the performance of the cropping system. IRRIGATE was developed using firstly a conceptual framework describing the system modelled as three sub-systems (biophysical, technical, and decision) interacting within the farm. Then a component-based spatially explicit modelling based on the identification of the interactions between modules, the identification of temporal and spatial scales of modules and the re-use of previous models was used to develop the numerical model. An example of the use of the biodecisional model is presented showing the effects on a real farm of a severe water shortage in 2006.     

Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data

Recent satellite missions have provided new perspectives by offering high spatial resolution, a variety of spectral properties, and fast revisit rates to the same regions. In this study, we examined the utility of both broadband red-edge spectral information and texture features for classifying paddy rice crops in South Korea into three different growth stages. The rice grown in South Korea can be grouped into early-maturing, medium-maturing, and medium-late-maturing cultivars, and each cultivar is known to have a minimum and maximum productivity. Therefore, the accurate classification of paddy rice crops into a certain time line enables pre-estimation of the expected rice yields. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the paddy rice crops, particularly when single-season image data were used. In contrast, texture information resulted in only minor improvement or even a slight decline in accuracy, although it is known to be advantageous for object-based classification. This was due to the homogeneous nature of paddy rice fields, as different rice cultivars are similar in terms of their morphology. Based on these results, we conclude that the additional spectral information such as the red-edge band is more useful than the texture features to detect different crop conditions in relatively homogeneous rice paddy environments. We therefore confirm the potential of broadband red-edge information to improve the classification of paddy rice crops. However, there is still a need to examine the relationship between textural properties and paddy rice crop parameters in greater depth.     

Evaluation of optical satellite remote sensing for rice paddy phenology in monsoon Asia using a continuous in situ dataset

In monsoon Asia, optical satellite remote sensing for rice paddy phenology suffers from atmospheric contaminations mainly due to frequent cloud cover. We evaluated the quality of satellite remote sensing of paddy phenology: (1) through continuous in situ observations of a paddy field in Japan for 1.5 years, we investigated phenological signals in the reflectance spectrum of the paddy field; (2) we tested daily satellite data taken by Terra/Aqua MODIS (MOD09 and L1B products) with regard to the agreement with the in situ data and the influence of cloud contamination. As a result, the in situ spectral characteristics evidently indicated some phenological changes in the rice paddy field, such as irrigation start, padding, heading, harvest and ploughing. The Enhanced Vegetation Index (EVI) was the best vegetation index in terms of agreement with the in situ data. More than 65% of MODIS observations were contaminated with clouds in this region. However, the combined use of Terra and Aqua decreased the rate of cloud contamination of the daily data to 43%. In conclusion, the most robust dataset for monitoring rice paddy phenology in monsoon Asia would be daily EVI derived from a combination of Terra/MODIS and Aqua/MODIS.     

Characterizing changes in cropping patterns using sequential Landsat imagery: an adaptive threshold approach and application to Phoenix,Arizona

Since the 1970s, the Phoenix Active Management Area has experienced rapid urbanization, mostly through land conversions from agricultural lands to urban land use. Rapid urban expansion and population growth have placed unprecedented pressure on agricultural production in this region. Agricultural intensification, in particular double cropping, has been observed globally as an important response to the growing pressure on land. However, the intensification has a number of negative impacts on water quality, biodiversity, and biogeochemical cycles. Thus, quantifying the spatial pattern of cropping intensity is important for natural resource management. In this study, we developed an adaptive threshold approach to map cropping intensity using time series Landsat data and examined the spatiotemporal patterns of cropping intensity in the Phoenix Active Management Area from 1995 to 2010 at 5-year intervals. To map cropping intensity accurately, the adaptive threshold algorithm was designed specifically to address several issues caused by the complex cropping patterns in the study area. The adaptive threshold method has abilities to (1) distinguish true crop cycles from multiple false phenological peaks, (2) minimize errors caused by data noise and missing data, (3) identify alfalfa and interyear crops and to distinguish alfalfa from double crops, and (4) adapt to temporal profiles with different numbers of observations. The adaptive threshold algorithm is effective in characterizing cropping intensity with overall accuracies exceeding 97%. Results show that there is a dramatic decline in the area of total croplands (46.1%), single crops (46.3%), and double crops (43.4%) during the study period. There was a small conversion (1.9%) from single to double crop from 1995 to 2000, whereas a reverse conversion (1.3%) was observed from 2005 to 2010. Updated and accurate information on the spatial distribution of cropping intensity provide important implications on effective and sustainable cropping practices. In addition, joint investigation on cropping patterns and irrigation water use can shed light on future agricultural water demand, which is of paramount importance in this rapidly expanding arid region.     

Mapping paddy rice fields by applying machine learning algorithms to multi-temporal Sentinel-1A and Landsat data

Sentinel-1A synthetic aperture radar (SAR) data present an opportunity for acquiring crop information without restrictions caused by weather and illumination conditions, at a spatial resolution appropriate for individual rice fields and a temporal resolution sufficient to capture the growth profiles of different crop species. This study investigated the use of multi-temporal Sentinel-1A SAR data and Landsat-derived normalized difference vegetation index (NDVI) data to map the spatial distribution of paddy rice fields across parts of the Sanjiang plain, in northeast China. The satellite sensor data were acquired throughout the rice crop-growing season (May–October). A co-registered set of 10 dual polarization (VH/VV) SAR and NDVI images depicting crop phenological development were used as inputs to Support Vector Machine (SVM) and Random Forest (RF) machine learning classification algorithms in order to map paddy rice fields. The results showed a significant increase in overall classification when the NDVI time-series data were integrated with the various combinations of multi-temporal polarization channels (i.e. VH, VV, and VH/VV). The highest classification accuracies overall (95.2%) and for paddy rice (96.7%) were generated using the RF algorithm applied to combined multi-temporal VH polarization and NDVI data. The SVM classifier was most effective when applied to the dual polarization (i.e. VH and VV) SAR data alone and this generated overall and paddy rice classification accuracies of 91.6% and 82.5%, respectively. The results demonstrate the practicality of implementing RF or SVM machine learning algorithms to produce 10 m spatial resolution maps of paddy rice fields with limited ground data using a combination of multi-temporal SAR and NDVI data, where available, or SAR data alone. The methodological framework developed in this study is apposite for large-scale implementation across China and other major rice-growing regions of the world.     

黑河中游张掖绿洲灌溉渠系的数字化制图与结构分析

人工灌溉渠系对于干旱区内陆河绿洲的生存和发展具有重要作用。以黑河中游的张掖绿洲为例,在收集大量高分辨率遥感影像和地形图资料的基础上,利用GPS实地测量和GIS软件提取了全绿洲干、支、斗3级渠系信息,获得了翔实准确的灌溉渠系空间和属性数据,首次完成了张掖绿洲灌溉渠系的数字化制图并对该渠系网络的空间格局进行了分析。结果表明：张掖绿洲目前渠道总数约为6 300条,总长为8 749.51 km,密度为0.47 km/km²,干、支、斗渠的比例为1∶1.17∶2.4。5个县区中甘州区的灌溉渠系分布最密集,而山丹县渠系建设相对落后。 绿洲人工灌溉渠系建设方式和水资源利用开发程度是影响和改变本地区流域景观结构和土地利用方式的重要因素。