Land suitability for Mango for the FGARA project

This land suitability for Mango raster data (in GeoTIFF format) represents areas of potential suitability for this crop and its specific irrigation management systems in the Flinders and Gilbert catchments of North Queensland. \nThe data is coded 1-5: \n1 - Suitable with no limitations; \n2 - Suitable with minor limitations; \n3 - Suitable with moderate limitations; \n4 - Marginal; \n5 - Unsuitable. \nThe land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO).\nThis data is part of the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project and is designed to support sustainable regional development in Australia being of importance to Australian Governments and agricultural industries. The project identifies new opportunities for irrigation development in these remote areas by providing improved soil and land evaluation data to identify opportunities and promote detailed investigation.\nA companion dataset exists, “Confidence of suitability data for the FGARA project”. A link to this dataset can be found in the “related materials” section of this metadata record.\nLineage: These suitability raster data for Mango and its individual irrigation management systems have been created from a range of inputs and processing steps. Below is an overview. For more information refer to the CSIRO FGARA published reports and in particular: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO. Broadly, the steps were to:\n1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc).\n2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space.\n3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes.\n4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software.\n5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements.\n6. Choose land management options and create suitability rules for DSM attributes.\n7. Run suitability rules to produce limitation datasets using a modification on the FAO methods.\n8. Create final suitability data for all land management options.\nTwo companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

本芒果土地适宜性栅格数据（格式为GeoTIFF），表征了澳大利亚北昆士兰州弗林德斯与吉尔伯特流域（Flinders and Gilbert Catchments）内，适配该作物及其特定灌溉管理系统的潜在适宜区域。
该数据采用1至5的编码规则：
1 - 无限制适宜；
2 - 存在轻微限制的适宜；
3 - 存在中度限制的适宜；
4 - 勉强适宜；
5 - 不适宜。
本数据所采用的土地适宜性评价方法，是对联合国粮食及农业组织（Food and Agriculture Organisation of the UN, FAO）现有方法的改良版本。
本数据隶属于弗林德斯与吉尔伯特农业资源评估（Flinders and Gilbert Agricultural Resource Assessment, FGARA）项目，旨在支持澳大利亚的可持续区域发展，对澳大利亚政府与农业产业均具有重要价值。该项目通过提供优化后的土壤与土地评价数据，助力识别上述偏远地区的灌溉开发新机遇，并推动相关详细调研工作。
本数据集存在一个配套数据集——《FGARA项目适宜性置信度数据》，其链接可在本元数据记录的“相关材料”栏目中获取。
数据溯源：本芒果及其各灌溉管理系统的适宜性栅格数据，通过一系列输入与处理步骤生成，以下为流程概述。如需了解更多细节，请参考澳大利亚联邦科学与工业研究组织（Commonwealth Scientific and Industrial Research Organisation, CSIRO）发布的FGARA项目报告，尤其可参阅：Bartley R、Thomas MF、Clifford D、Phillip S、Brough D、Harms D、Willis R、Gregory L、Glover M、Moodie K、Sugars M、Eyre L、Smith DJ、Hicks W与Petheram C（2013）所著《土地适宜性：技术方法》——一份为弗林德斯与吉尔伯特农业资源评估（FGARA）项目提交给澳大利亚政府的技术报告，由CSIRO发布。整体流程如下：
1. 整合现有数据：涵盖各类格式的气候、地形、土壤、自然资源、遥感等相关数据，以及各类报告、空间矢量、空间栅格等数据载体。
2. 基于协变量空间，采用拉丁超立方统计抽样法选取额外的土壤与属性点位数据。
3. 开展野外调查，采集额外的土壤与属性数据，并明晰地貌与景观特征。
4. 依托RuleFit3软件中的规则集成预测学习方法，基于筛选后的输入数据与协变量数据构建模型。
5. 生成数字土壤制图（Digital Soil Mapping, DSM）核心属性输出数据。数字土壤制图（DSM）指通过定量关系，将野外与实验室观测数据与环境数据相结合，构建并填充地理参考数据库的过程。其依托土壤计量学（pedometrics）方法——即运用数学与统计模型，整合土壤观测数据与相关环境变量、遥感影像及部分地球物理测量数据中的信息。
6. 选定土地管理方案，并为DSM属性制定适宜性规则。
7. 基于FAO方法的改良版本，运行适宜性规则以生成限制因子数据集。
8. 生成所有土地管理方案对应的最终适宜性数据。
本数据集另有两个配套数据集。第一个配套数据集的链接已在本元数据记录的“相关材料”栏目中给出，标题为《FGARA项目适宜性置信度数据》。第二个配套数据集由CSIRO土地与水部门（CSIRO Land and Water）持有，其包含影响上述流域农业开发潜力的景观过程或景观条件相关的专家意见与知识，但这些信息在建模过程中未得到充分捕捉（同时涵盖马氏距离（Mahalanobis）方法低估置信度的专家意见区域）。
需特别关注的两类景观特征分别为：弗林德斯上游流域的玄武岩露头（协变量数据未对其进行充分捕捉），以及弗林德斯中部流域的次生盐渍化风险。如需了解更多细节，请参阅前述报告《土地适宜性：技术方法——弗林德斯与吉尔伯特农业资源评估（FGARA）项目提交给澳大利亚政府的技术报告》。