Raster data providing site suitability results for the production of castor throughout Brazil. The pixel value range from 1 (currently not suitable) to 10 (highly suitable) for a suitability ranking in the given pixel location. The site suitability for castor was conducted using data associated with agronomic and disease characteristics. The various characteristics were subject to a weighted overlay analysis in conjunction with an analytical hierarchy process. The raster was the result of these analytics.

The dashboard is part of an ongoing project with the Office of Equity to assist in decision making for equity policies.The dashboard consumes the following map: Site Suitability - Equity Map.

This land suitability for Avocado 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Avocado 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two 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”.

This dataset is a structured, high-precision compilation of environmental and solar irradiance data sourced from NASA POWER, covering India's major geographic zones over the span of one year (January to December 2022). It includes seven critical parameters—such as solar radiation, temperature, cloud cover, albedo, and precipitation—essential for evaluating solar power generation potential. The primary goal of the dataset is to aid in the identification of optimal locations for solar energy infrastructure by applying geospatial and machine learning techniques. Carefully preprocessed for consistency and organized for ease of use, this dataset is not only useful for current solar site suitability analysis but also offers long-term value to researchers, urban planners, and policymakers. It supports advanced analytics like clustering, classification, and visualizations, and can serve as a foundation for predictive modeling, transfer learning, and sustainability-oriented decision-making in the field of renewable energy.

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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two 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”.

Hawaiian coastal wetlands provide important habitat for federally endangered waterbirds and socio-cultural resources for Native Hawaiians. Currently, Hawaiian coastal wetlands are degraded by development, sedimentation, and invasive species and, thus, require restoration. Little is known about their original structure and function due to the large-scale alteration of the lowland landscape since European contact. Here, we used 1) rapid field assessments of hydrology, vegetation, soils, and birds, 2) a comprehensive analysis of endangered bird habitat value, 3) site spatial characteristics, 4) sea-level rise projections for 2050 and 2100 and wetland migration potential, and 5) preferences of the Native Hawaiian community in a GIS site suitability analysis to prioritize restoration of coastal wetlands on the island of Molokaʻi. The site suitability analysis is the first, to our knowledge, to incorporate community preferences, habitat criteria for endangered waterbirds, and sea-level rise into prioritizing wetland sites for restoration. The rapid assessments showed that groundwater is a ubiquitous water source for coastal wetlands. A groundwater-fed, freshwater herbaceous peatland or “coastal fen” not previously described in Hawaiʻi was found adjacent to the coastline at a site being used to grow taro, a staple crop for Native Hawaiians. In traditional ecological knowledge, such a groundwater-fed, agro-ecological system is referred to as a loʻipūnāwai (spring pond). Overall, 39 plant species were found at the 12 sites; 26 of these were wetland species and 11 were native. Soil texture in the wetlands ranged from loamy sands to silt and silty clays and the mean % organic carbon content was 10.93% ± 12.24 (sd). In total, 79 federally endangered waterbirds, 13 Hawaiian coots (‘alae keʻokeʻo; Fulica alai) and 66 Hawaiian stilts (aeʻo; Himantopus mexicanus knudseni), were counted during the rapid field assessments. The site suitability analysis consistently ranked three sites the highest, Kaupapaloʻi o Kaʻamola, Kakahaiʻa National Wildlife Refuge, and ʻŌhiʻapilo Pond, under three different weighting approaches. Site prioritization represents both an actionable plan for coastal wetland restoration and an alternative protocol for restoration decision-making in places such as Hawaiʻi where no pristine “reference” sites exist for comparison.

Pacific lamprey (Entosphenus tridentata) are native fish to the Columbia River Basin. Over the past 60 years, anthropogenic disturbances have contributed to a 95% decline of historical population numbers. Member-tribes of the Columbia River Inter-Tribal Fish Commission have acknowledged the importance of Pacific lamprey to the Columbia River ecosystem and expressed concern about the loss of an essential tribal cultural resource. As a result, the Columbia River Inter-Tribal Fish Commission created the Tribal Pacific Lamprey Restoration Plan to halt their decline, re-establish the species, and restore the population to sustainable, harvestable levels throughout their historical range. Limited knowledge about the movement and preferred habitat of larval Pacific lamprey, such as optimal habitat conditions, demographic information, and species resilience, results in challenges to monitor and protect the species. Pacific lamprey is known to use the mainstem Columbia River to migrate between their spawning grounds and the Pacific Ocean. However, dams, levees, and culverts within the Columbia River Estuary and adjacent tributaries have restricted the lamprey’s access to spawning grounds and other upstream habitats. These restrictions have prompted conservation and restoration efforts to better understand how Pacific lamprey utilizes the Columbia River Estuary. Here, we address these knowledge gaps in an effort to aid restoration initiatives by completing a Habitat Suitability Analysis to determine where optimal larval Pacific lamprey habitat may exist in the Columbia River Estuary. The project identified the spatial and temporal distribution of suitable habitat for larval Pacific lamprey and generated recommendations to address habitat-related knowledge gaps and further evaluate anthropogenic threats to their recovery. The results of the Habitat Suitability Analysis suggest that habitat conditions in the Columbia River itself are unable to support larval lamprey year-round, but may provide suitable habitat on a seasonal basis due to spatial and temporal limitations. However, we stress that our analyses were necessarily limited to aquatic conditions and that the temperature of the water column used in our analyses may differ from the temperature within fine sediments, where larval lamprey burrow. Our results imply that suitable lamprey habitat is present at times throughout the year in the Columbia River Estuary, and these locations can be used to support habitat restoration and conservation strategies for improving the species’ recovery. Anthropogenic threats to the Columbia River continue to alter habitat conditions, including average water temperature, salinity, and sedimentation. Laboratory experiments have provided insight into the potential impacts of changing temperature and salinity on larval Pacific lamprey, where elevated water temperatures can affect their development and elevated salinity levels can result in larval mortality. In addition, anthropogenic disturbances such as dams, levees, and culverts have cut off the Columbia River Estuary’s floodplain habitats from the mainstem Columbia River, decreased sedimentation rates, and separated adult lamprey from the floodplains and tributaries that they use to spawn. The presence of these barriers in the region can inhibit the distribution of fine sediments in the river, limiting where larval lamprey burrow and develop. The burrowing behavior of larval lamprey has yet to fully be investigated in the Columbia River Estuary. Limited research may be due to the lack of resources for studying Pacific lamprey’s life cycle, habitat, and population dynamics since they are not federally designated as an endangered species, like resident salmonid species. This has further added to the challenge of understanding the species and restoring its population to sustainable numbers.

To the best of our knowledge, this project is the first to explore spatial and temporal trends of suitable larval Pacific lamprey habitat conditions in the Columbia River Estuary. The Habitat Suitability Analysis provides technical information about the presence and distribution of suitable conditions to address habitat-related uncertainties. The member-tribes of the Columbia River Inter-Tribal Fish Commission and their collaborators can incorporate the information into current and future Pacific lamprey restoration, conservation, and education programs to enhance general understanding of lamprey populations throughout the Columbia River Basin. Key recommendations are provided to address additional knowledge gaps and prioritize future restoration projects in the Columbia River Basin including the refinement of the Habitat Suitability Analysis, evaluation of barrier effects on Pacific lamprey passage, and assessment of climate change scenarios on larval lamprey habitat. Methods The Habitat Suitability Analysis uses salinity, temperature, and geomorphology data to identify suitable larval Pacific lamprey habitat in the Columbia River Estuary. In addition, the analysis uses hydrogeomorphic reach data of the Columbia River Estuary. The monthly salinity and temperature data was obtained from a Oregon Health & Science University's Center for Coastal Margin Observation & Prediction hindcast simulation database known as db33. This simulation's outputs were projections that were based on 20-year averages between 1999 and 2018 and resulted in daily summary statistic files; these files were binned by month to produce GeoTIFF files, consisting of 12 individual raster files for each month. In total, there are 12 salinity GeoTIFFs (units are in Practical Salinity Units, which are roughly equivalent to Parts Per Thousnd) and 12 temperature GeoTIFFs (units are in degrees Celsius). Each GeoTIFF summarized salinity or temperature conditions for that month of the year. For example, one raster file contains the summary statistics for all Aprils between 1999 and 2018. The geomorphology data and hydrogeomorphic reach data are layers from a Columbia River Estuary Ecosystem Classification geodatabase from the Lower Columbia Estuary Partnership's website. The geomorphology data (also known as geomorphic catena) is a vector layer that contains individual landforms within the Columbia River's ecosystem complexes that were created over the past 2,000 years. Examples include natural levees, bedrock, and floodplains. The hydrogeomorphic reach data is a vector layer that divides the Columbia River Estuary into eight separate regions based on the region's biophysical characteristics. This dataset also uses a shapefile layer of the Columbia River Basin called "Columbia Basin Streams" to define the research project's region of study. This shapefile layer was obtained from NOAA Fisheries' Columbia Basin Historical Ecology Project Data, though it was replaced by the hydrogeomorphic reach data during the analysis process All of the datasets were processed using the ArcGIS Pro 2.6.0 ModelBuilder by using a binary classification system to reclassify the salinity, temperature, and geomorphology data. This project had researched environmental parameters that were critical for larval Pacific lamprey survival and identified specific salinity and temperature ranges using scientific literature. Salinity and temperature values that fell within their respective ranges were assigned a 1, while salinity and temperature values that did not fall within the range were assigned a 0. This process was completed for each month of the year. The geomorphology data was assigned a binary classification based on whether the habitat within the layer was predominantly aquatic; layers that were predominantly aquatic would be suitable for larval Pacific lamprey were assigned a 1 while layers that were not predominantly aquatic would be unsuitable for larval Pacific lamprey and were assigned a 0. The researchers then used ArcGIS Pro's Raster Calculator tool to sum the reclassified output for each month, and then multiplying the monthly salinity results by the monthly temperature results and the geomorphic catena results. This resulted in 12 outputs per month where suitable habitat was either met or not met. The last step of the Habitat Suitability Analysis combined the resulting 12 output layers of monthly suitable habitat into a single Raster Calculator to add the number of months where suitable habitat was met.

These aquaculture suitability raster datasets (in GeoTIFF format) indicate areas of potential suitability for freshwater and marine aquaculture species in earthen or lined ponds. A multi-criteria analysis, involved the integration of soil data and biophysical characteristics within a GIS spatial analysis environment to predict potential sites to inform decision making. A set of limitations and rules were adapted from (McLeod et al., 2002) to determine suitability. These aquaculture datasets were generated within the ‘Land suitability’ activity in consultation with the agricultural viability activity of the Northern Australia Water Resource Assessment (NAWRA). The aquaculture suitability analysis is described in full in the CSIRO NAWRA published report ‘Aquaculture viability. A technical report to the Australian Government from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments. CSIRO, Australia.’ Irvin S, Coman G, Musson D and Doshi A (2018). There are five suitability classes coded 1-5. 1 – Highly suitable land with negligible limitations 2 – Suitable land with minor limitations 3 – Moderately suitable land with considerable limitations 4 – Currently unsuitable land with severe limitations 5 – Unsuitable land with extreme limitations. Each drop in suitability implies that more management input (and cost) is required to achieve incremental increases in production. The soil and land characteristics considered for all configurations include; clay content, sodicity and rockiness; and mainly refer to geotechnical considerations (e.g. construction and stability of pond walls). Other limitations, including slope, and the likely presence of gilgai microrelief and acid sulfate soils, infer more difficult, expensive and therefore less suitable development environments, and a greater degree of land preparation effort. Key considerations for earthen ponds included soil properties preventing pond leakage and soil acidity (pH); the latter taking into account negative growth responses of species from unfavourable pH values (i.e. biological limitation) as well as engineering, as pH may affect the structural integrity of earthen walls. Proximity to sea water was considered for marine species although the characteristics of tides and their suitability for marine aquaculture have not been applied in this analysis therefore the full inland distance of tidal waters has not been explored. The aquaculture suitability rules, including the limitation classes and suitability subclasses for each species by pond configuration, is provided in the above referenced publication. It is important to emphasize that this is a regional-scale assessment: further data collection and analyses would be required to plan development at a scheme, enterprise or property scale. Lineage: These aquaculture suitability raster datasets have been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO NAWRA published reports and in particular ‘Aquaculture viability. A technical report to the Australian Government from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments. CSIRO, Australia.’ 1. Collated existing data. 2. Selection of additional soil and land attribute site data locations. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attributes to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data. 6. Create Digital Soil Mapping (DSM) attribute raster datasets. 7. Aquaculture suitability rules created for DSM attributes. 8. Suitability rules were run to produce limitation subclass datasets. 9. Final suitability data created for aquaculture options. 10. Quality assessment of these aquaculture data was conducted by on-ground and expert (qualitative) examination of outputs.

The data contained in child items of this page were developed to support the Species Status Assessments conducted by the U.S. Fish & Wildlife Service and conservation planning for State, Federal, and non-government researchers, managers, landowners, and other partners for five focal herpetofauna species: gopher tortoise (Gopherus polyphemus), southern hognose snake (Heterodon simus), Florida pine snake (Pituophis melanoleucus mugitus), gopher frog (Lithobates capito), and striped newt (Notophthalmus perstriatus). These data were developed by the USGS Cooperative Fish & Wildlife Research Unit at the University of Georgia in collaboration with other partners. The three child items contain the following data: (1) responses of species experts, elicited from online surveys and in-person workshops, reflecting environmental, ecological, climatic, anthropogenic, or other attributes influential to each of the five focal species' status in the Southeast; (2) a spatial geodatabase of polygon feature layers representing habitat suitability classes (low, moderate, and high suitability) for each species, as estimated from range-wide habitat suitability models; and (3) a spatial geodatabase of rasters produced from the same habitat suitability models whose values range from 0 (least suitable habitat for the species) to 100 (most suitable). Collectively, the habitat suitability polygons and rasters extend across the range of these species in the Southeast US, including areas in Louisiana, Mississippi, Alabama, Florida, Georgia, South Carolina, and North Carolina. A full discussion of the compilation methodology and sources used to develop the habitat suitability data is available in the accompanying publication: Crawford, B.A., J.C. Maerz, & C.T. Moore. 2020. Expert-informed habitat suitability analysis for at-risk species assessment and conservation planning. Journal of Fish and Wildlife Management. in review.

GIS Suitability Analysis is a methodology to determine the appropriateness of a location or areas for system. It targets the assessment or measure of the potential for a specific activity development from a spatial perspective.

Suitability analysis can use GIS multicriteria decision analysis or a simple overlay of geographic (geospatial) constraints to determine areas or locations theoretically appropriate for a certain use of the space.

The downloadable ZIP file contains model documentation and contact information for the model creator. For more information, or a copy of the project report which provides greater model detail, please contact Ryan Urie - traigo12@gmail.com.This model was created from February through April 2010 as a central component of the developer's master's project in Bioregional Planning and Community Design at the University of Idaho to provide a tool for identifying appropriate locations for various land uses based on a variety of user-defined social, economic, ecological, and other criteria. It was developed using the Land-Use Conflict Identification Strategy developed by Carr and Zwick (2007). The purpose of this model is to allow users to identify suitable locations within a user-defined extent for any land use based on any number of social, economic, ecological, or other criteria the user chooses. The model as it is currently composed was designed to identify highly suitable locations for new residential, commercial, and industrial development in Kootenai County, Idaho using criteria, evaluations, and weightings chosen by the model's developer. After criteria were chosen, one or more data layers were gathered for each criterion from public sources. These layers were processed to result in a 60m-resolution raster showing the suitability of each criterion across the county. These criteria were ultimately combined with a weighting sum to result in an overall development suitability raster. The model is intended to serve only as an example of how a GIS-based land-use suitability analysis can be conceptualized and implemented using ArcGIS ModelBuilder, and under no circumstances should the model's outputs be applied to real-world decisions or activities. The model was designed to be extremely flexible so that later users may determine their own land-use suitability, suitability criteria, evaluation rationale, and criteria weights. As this was the first project of its kind completed by the model developer, no guarantees are made as to the quality of the model or the absence of errorsThis model has a hierarchical structure in which some forty individual land-use suitability criteria are combined by weighted summation into several land-use goals which are again combined by weighted summation to yield a final land-use suitability layer. As such, any inconsistencies or errors anywhere in the model tend to reveal themselves in the final output and the model is in a sense self-testing. For example, each individual criterion is presented as a raster with values from 1-9 in a defined spatial extent. Inconsistencies at any point in the model will reveal themselves in the final output in the form of an extent different from that desired, missing values, or values outside the 1-9 range.This model was created using the ArcGIS ModelBuilder function of ArcGIS 9.3. It was based heavily on the recommendations found in the text "Smart land-use analysis: the LUCIS model." The goal of the model is to determine the suitability of a chosen land-use at each point across a chosen area using the raster data format. In this case, the suitability for Development was evaluated across the area of Kootenai County, Idaho, though this is primarily for illustrative purposes. The basic process captured by the model is as follows: 1. Choose a land use suitability goal. 2. Select the goals and criteria that define this goal and get spatial data for each. 3. Use the gathered data to evaluate the quality of each criterion across the landscape, resulting in a raster with values from 1-9. 4. Apply weights to each criterion to indicate its relative contribution to the suitability goal. 5. Combine the weighted criteria to calculate and display the suitability of this land use at each point across the landscape. An individual model was first built for each of some forty individual criteria. Once these functioned successfully, individual criteria were combined with a weighted summation to yield one of three land-use goals (in this case, Residential, Commercial, or Industrial). A final model was then constructed to combined these three goals into a final suitability output. In addition, two conditional elements were placed on this final output (one to give already-developed areas a very high suitability score for development [a "9"] and a second to give permanently conserved areas and other undevelopable lands a very low suitability score for development [a "1"]). Because this model was meant to serve primarily as an illustration of how to do land-use suitability analysis, the criteria, evaluation rationales, and weightings were chosen by the modeler for expediency; however, a land-use analysis meant to guide real-world actions and decisions would need to rely far more heavily on a variety of scientific and stakeholder input.

These land suitability raster data (in GeoTIFF format) indicates areas of potential suitability for 126 crops and their specific irrigation management systems and seasons in the Fitzroy catchment of Western Australia. This data provides improved land evaluation information to identify opportunities and promote detailed investigation for a range of sustainable development options and was created within the ‘Land suitability’ activity of the Northern Australia Water Resource Assessment (NAWRA). There are five land suitability classes coded 1-5. 1 – Highly suitable land with negligible limitations 2 – Suitable land with minor limitations 3 – Moderately suitable land with considerable limitations 4 – Currently unsuitable land with severe limitations 5 – Unsuitable land with extreme limitations. The land suitability evaluation methods used to produce this data are a modification of the Food and Agriculture Organisation (FAO) land evaluation approach. The land suitability analysis is described in full in the CSIRO NAWRA published report referenced in the "Citation" field of this metadata record. A companion dataset showing the reliability of this suitability data (showing areas of the catchment where there is greater or lesser confidence in the accuracy of the suitability data) is also supplied. The naming convention for these data is; ‘crop’ underscore ‘season’ underscore ‘irrigation type’ underscore ‘catchment code’ underscore ‘data type’ eg ‘SorgForage_dry_fur_F_Suit’ is Sorghum forage dry season furrow irrigated Fitzroy catchment suitability. The codes for season are; wet – wet season; dry – dry season; per – perennial; wet-dry – planted late wet season and grown through the dry season eg navy bean, soybean; wet-long – longer growing crops that grow through a wet season eg sugarcane. The codes for irrigation type are; spray – overhead spray irrigation; tric – trickle irrigation; mini-spray – mini spray irrigation; flood – flood irrigation; fur – furrow irrigation; rainfed – rainfed. The codes for data type are; suit – suitability data, CI – reliability data expressed as confusion index. It is important to emphasize that this is a regional-scale assessment: further data collection and detailed soil physical, chemical and nutrient analyses would be required to plan development at a scheme, enterprise or property scale. Several limitations that may have a bearing on land suitability were out of scope and not assessed as part of this activity (see section 1.1 and 2.1.2 of the cited report), these limitations include biophysical and socio-cultural. For example these land suitability raster datasets do not include consideration of the licensing of water, flood risk, contiguous land, risk of irrigation induced secondary salinity, or land tenure and other legislative controls. Some of these may be addressed elsewhere in NAWRA eg flooding was investigated within the Earth observation remote sensing activity and the risk of irrigation induced secondary salinity was assessed as part of the groundwater investigations. Lineage: These suitability raster datasets have been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO NAWRA published reports and in particular 'Land suitability of the Fitzroy, Darwin and Mitchell catchments. A technical report from the CSIRO Northern Australia Water Resource Assessment, part of the National Water Infrastructure Development Fund: Water Resource Assessments, CSIRO, Australia'. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create Digital Soil Mapping (DSM) attributes raster datasets. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate 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. 7. Land management options were chosen and suitability rules created for DSM attributes. 8. Suitability rules were run to produce limitation subclass datasets using a modification on the FAO methods. 9. Final suitability data created for all land management options. 10. Companion predicted reliability data was produced. 11. QA Quality assessment of these land suitability data was conducted by two methods; Method 1: Statistical (quantitative) assessment of the "reliability" of the spatial output data presented as a raster of the Confusion Index. Method 2: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. Across each of the study areas a two week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling using the reliability data of the attribute. The modelled land suitability value was assessed against the actual on-ground value. These results are published in the report cited in this metadata record.

Supplementary information files for article: 'The future scope of large-scale solar in the UK: site suitability and target analysis'.Abstract:This paper uses site suitability analysis to identify locations for solar farms in the UK to help meet climate change targets. A set of maps, each representing a given suitability criterion, is created with geographical information systems (GIS) software. These are combined to give a Boolean map of areas which are appropriate for large-scale solar farm installation. Several scenarios are investigated by varying the criteria, which include geographical (land use) factors, solar energy resource and electrical distribution network constraints. Some are dictated by the physical and technical requirements of large-scale solar construction, and some by government or distribution network operator (DNO) policy. It is found that any suitability map which does not heed planning permission and grid constraints will overstate potential solar farm area by up to 97%. This research finds sufficient suitable land to meet Future Energy Scenarios (UK National Grid outlines for the coming energy landscape).

This land suitability for Banana 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. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This 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. A 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. Lineage: These suitability raster data for Banana 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: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. 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. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two 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”.

GIS Suitability Analysis is a methodology to determine the appropriateness of a location or areas for system. It targets the assessment or measure of the potential for a specific activity development from a spatial perspective.

Suitability analysis can use GIS multicriteria decision analysis or a simple overlay of geographic (geospatial) constraints to determine areas or locations theoretically appropriate for a certain use of the space.

This spatial dataset provides polygon information delineating land unit boundaries at a scale of 1: 50 000 and describes the dominant landform, soils and vegetation of the the Warumungu (NT Por 502) and Kurnturlpara (NT Por 4802) Aboriginal Land Trust areas, Northern Territory. The survey area is also locally known as Frewena and this name was adopted for the Soil Site information: FRE15 (ie Frewena 2015) The study area covers 172km2 and is located approximately 150km east of Tennant Creek via the Barkly Highway. The primary purpose of the investigation was to map, describe and evaluate soil landscapes to inform future irrigation development in an area with identified potential for groundwater extraction. Core landform, soil and vegetation attributes attached to the land units are included with the spatial dataset. CAUTION: Map unit boundaries were derived using satellite imagery in association with a digital elevation model, geological and topographic data. Landform, soil and vegetation field assessments conform to national standards and support mapping at a scale of 1:50 000. When assessing specific areas within the mapping it is recommended a site inspection is undertaken to establish unmapped variation and confirm mapping accuracy on the ground. The mapping does not indicate, imply or ascertain the likelihood of groundwater availability or the granting of appropriate water extraction licensing needed to satisfy the irrigation requirements of the potential agricultural development options needed.

These land suitability raster data (GeoTIFF format) indicates areas of potential suitability for 21 crop groups and their specific irrigation management systems and seasons in the Victoria catchment of the Northern Territory. This data provides improved land evaluation information to identify opportunities and promote detailed investigation for a range of sustainable development options and was created within the ‘Land suitability’ activity of the Victoria River Water Resource Assessment (VIWRA). There are five land suitability classes coded 1-5. 1 – Highly suitable land with negligible limitations 2 – Suitable land with minor limitations 3 – Moderately suitable land with considerable limitations 4 – Currently unsuitable land with severe limitations and 5 – Unsuitable land with extreme limitations. The land suitability evaluation methods used to produce this data are a modification of the Food and Agriculture Organisation (FAO) land evaluation approach. The land suitability analysis is described in full in the CSIRO VIWRA published report ‘Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. Companion datasets showing the reliability of these suitability data (showing areas of the catchment where there is greater or lesser confidence in the accuracy of the suitability data) are also supplied. The naming convention for these data is; ‘crop group’ underscore ‘major crop’ underscore ‘season code’ underscore ‘irrigation type code’ underscore ‘catchment code’ underscore ‘data type’ eg ‘CG7_CottonGrains_D_Fw_V_Suit’ is Cotton and grain crops grown in the dry season with furrow irrigation in the Victoria catchment suitability data. The codes for season are; W – wet season; D – dry season; P – perennial. The codes for irrigation type are; S – overhead spray irrigation; T – trickle irrigation; Fd – flood irrigation; Fw – furrow irrigation; R – rainfed. The codes for data type are; suit – suitability data, CI – reliability data expressed as confusion index. It is important to emphasize that this is a regional-scale assessment: further data collection and detailed soil physical, chemical and nutrient analyses would be required to plan development at a scheme, enterprise or property scale. Several limitations that may have a bearing on land suitability were out of scope and not assessed as part of this activity (refer to the report), these limitations include biophysical and socio-cultural. For example these land suitability raster datasets do not include consideration of the licensing of water, flood risk, contiguous land, risk of irrigation induced secondary salinity, or land tenure and other legislative controls. Some of these may be addressed elsewhere in VIWRA eg flooding was investigated by the Earth observation remote sensing group in the surface water activity. The Victoria River Water Resource Assessment provides a comprehensive overview and integrated evaluation of the feasibility of aquaculture and agriculture development in the Victoria catchment NT as well as the ecological, social and cultural (indigenous water values, rights and aspirations) impacts of development. Lineage: These suitability raster datasets have been generated from a range of inputs and processing steps. Following is an overview. For more information refer to the CSIRO VIWRA published report ' Soils and land suitability for the Victoria catchment, Northern Territory’. A technical report from the CSIRO Victoria River Water Resource Assessment to the Government of Australia. 1. Collated existing data (relating to: soils, climate, topography, natural resources, remotely sensed, of various formats: reports, spatial vector, spatial raster etc). 2. Selection of additional soil and land attribute site data locations by a conditioned Latin hypercube statistical sampling method applied across the covariate data space. 3. Fieldwork was carried out to collect new attribute data, soil samples for analysis and build an understanding of geomorphology and landscape processes. 4. Database analysis was performed to extract the data to specific selection criteria required for the attribute to be modelled. 5. The R statistical programming environment was used for the attribute computing. Models were built from selected input data and covariate data using predictive learning from a Random Forest approach implemented in the ranger R package. 6. Create Digital Soil Mapping (DSM) attribute raster datasets. DSM data is a geo-referenced dataset, generated from field observations and laboratory data, coupled with environmental covariate 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. 7. Land management options were chosen and suitability rules created for DSM attributes. 8. Suitability rules were run to produce limitation subclass datasets using a modification on the FAO methods. 9. Final suitability data created for all land management options. 10. Companion predicted reliability data was produced from the 500 individual Random Forest attribute models created. 11. QA Quality assessment of these land suitability data was conducted by two methods. Method 1: Statistical (quantitative) assessment of the "reliability" of the spatial output data presented as a raster of the Confusion Index. Method 2: Collecting independent external validation site data combined with on-ground expert (qualitative) examination of outputs during validation field trips. A two-week validation field trip was conducted using a new validation site set which was produced by a random sampling design based on conditioned Latin Hypercube sampling. The modelled land suitability value was assessed against the actual on-ground value. These results are published in the report referenced above.

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI, created using ArcGIS 10.2.2) values for Nevada during spring, which is a surrogate for habitat conditions during the sage-grouse breeding and nesting period. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Summer included telemetry locations (n = 14,058) from mid-March to June. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated using R Software (v 3.13) for each subregion and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

Soil and Land Suitability Assessment for Irrigated Agriculture on on part of Beswick Aboriginal Land Trust. Department survey code BESWI_25 The field survey was undertaken at a mapping scale of 1:25,000 The polygon spatial dataset describes findings from a soil and land suitability investigation undertaken during 2017-2018 to assess irrigated agricultural potential on Aboriginal lands in the Beswick area, south of Katherine.The primary purpose of the investigation was to map, describe and evaluate soil landscapes to inform future irrigation development in an area with identified potential for groundwater extraction. Core landform, soil and vegetation attributes attached to the land units are included with the spatial dataset. CAUTION: Map unit boundaries were derived using satellite imagery in association with a digital elevation model, geological and topographic data. Landform, soil and vegetation field assessments conform to national standards and support mapping at a scale of 1:25 000. When assessing specific areas within the mapping it is recommended a site inspection is undertaken to establish unmapped variation and confirm mapping accuracy on the ground. The mapping does not indicate, imply or ascertain the likelihood of groundwater availability or the granting of appropriate water extraction licensing needed to satisfy the irrigation requirements of the potential agricultural development options needed.

This spatial dataset provides polygon information delineating land unit boundaries at a scale of 1: 25 000 and describes the dominant landform, soils and vegetation of the Ti Tree Basin Area, Northern Territory. The study area is comprised of 7 separate sections and covers 135 km2 and located south of the Ti Tree township via the Stuart Highway and Cavenagh Road over NT Portions 3636, 725 and 6152. The primary purpose of the investigation was to map, describe and evaluate soil landscapes to inform future irrigation development in an area with identified potential for groundwater extraction. Core landform, soil and vegetation attributes attached to the land units are included with the spatial dataset. CAUTION: Map unit boundaries were derived using satellite imagery in association with a digital elevation model, geological and topographic data. Landform, soil and vegetation field assessments conform to national standards and support mapping at a scale of 1:25 000. When assessing specific areas within the mapping it is recommended a site inspection is undertaken to establish unmapped variation and confirm mapping accuracy on the ground. The mapping does not indicate, imply or ascertain the likelihood of groundwater availability or the granting of appropriate water extraction licensing needed to satisfy the irrigation requirements of the potential agricultural development options needed.