Geospatial data about Palm Bay, Florida Lot Lines. Export to CAD, GIS, PDF, CSV and access via API.

The GIS shapefile and summary tables provide irrigated agricultural land-use for Hendry and Palm Beach Counties, Florida through a cooperative project between the U.S Geological Survey (USGS) and the Florida Department of Agriculture and Consumer Services (FDACS), Office of Agricultural Water Policy. Information provided in the shapefile includes the location of irrigated land field verified for 2019, crop type, irrigation system type, and primary water source used in Hendry and Palm Beach Counties, Florida. A map image of the shapefile is provided in the attachment.

Geographic index for the Future Land feature layer by page layout and numbering convention.

We developed a map showing the global extent of industrial oil palm plantations following a three-steps procedure. First, we identified the top 29 palm oil producer countries based on FAO statistics of harvested area. Second, we carried out a literature review of published studies that have mapped industrial oil palm plantations, and compiled this information into a Geographic Information System. Third, we complemented this analysis for 13 countries, where no maps were available. For these thirteen countries, we delineated the boundaries of industrial using cloud-free LANDSAT mosaics acquired in 2017, created in Google Earth Engine.

We declared an area planted (the land is either already planted or under development), the moment we observed large rectangular elements, long linear boundaries, and distinctive grid- or contour-planting patterns appear on our images. These planting patterns characterize industrial plantations. They are easily detected by the human eye, but are ...

The dataset contains 634 100x100 km tiles, covering areas where oil palm plantations were detected. The file 'grid.shp' contains the grid that covers the potential distribution of oil palm. The file 'grid_withOP.shp' shows the 100x100 grid squares with presence of oil palm plantations. The classified images (‘oil_palm_map’ folder, in geotiff format) are the output of the convolutional neural network based on Sentinel-1 and Sentinel-2 half-year composites. The images have a spatial resolution of 10 meters and contain three classes: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm. The file ‘Validation_points_GlobalOilPalmLayer_2019.shp’ includes the 13,495 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the labelled class assigned by visual interpretation, and the attribute ‘predClass, which reflects the predicted class by the convolutional neural network. The ‘Class’ and ‘predClass’ values are the same as the raster files: [1] Industrial closed-canopy oil palm plantations, [2] Smallholder closed-canopy oil palm plantations, and [3] other land covers/uses that are not closed canopy oil palm.

See article for additional information:

Descals, Adrià, et al. "High-resolution global map of smallholder and industrial closed-canopy oil palm plantations." Earth System Science Data 13.3 (2021): 1211-1231.

Changelog v1:

- The analysis was extended to Sri Lanka, South India, and countries in Eastern Africa where oil palm can potentially grow.

- The validation dataset only includes the points drawn by simple random sampling and stratified random sampling in the grid cells where the IUCN industrial layer detected oil palm.

- The 'Class' and 'predClass' values in the validation dataset were reclassified with the same values as the raster images: [1] Industrial plantations, [2] Smallholder plantations, and [3] Other land covers/uses.

DATA SOURCES:

1. ONLY state sources (city/county/state administration, federal agencies, ministries, etc.). We DO NOT use unverified databases
2. Over 2300 sources. We use even the smallest sources, because they contain valuable data. This allows us to provide our users with the most complete data

DATA RELEVANCE:

1. Our data is updated daily, weekly, monthly depending on the sources
2. We collect, process and store all data, regardless of their relevance. Historical data is also valuable

DATA TYPES:

1. Specifications
2. Owners
3. Permits
4. Sales
5. Inspections
6. Violations
7. Assessed values
8. Taxes
9. Risks
10. Foreclosures
11. Property Tax Liens
12. Deed Restrictions

NUMBERS:

1. 2300 data sources in total
2. 155,000,000 properties in total
3. 4 billion records (listed in the "data types" block above) in total
4. 2 million new records every day

DATA USAGE:

1. Property check, investigation (even the smallest events are stored in our database)
2. Prospecting (more than 100 parameters to find the required records)
3. Tracking (our data allows us to track any changes)

The file ‘GlobalCoconutLayer_2020_v1-2.zip’ contains 878 raster tiles of 100x100 km in geotiff format. The raster files are the result of a convolutional neural network that classified Sentinel-1 and Sentinel-2 annual composites into a coconut palm layer for the year 2020. The images have a spatial resolution of 20 meters and contain two classes: [0] Other land covers that are not coconut palm. [1] Coconut palm.

The file ‘GlobalCoconutLayer_2020_densityMap_1km_v1-2.zip’ contains the 20-meter coconut palm classification aggregated to 1 km. The value of each pixel represents the coconut palm area (in squared meters) within the 1-km pixel.

The file ‘Validation_points_GlobalCoconutLayer_2020_v1-2.shp’ includes the 10,200 points that were used to validate the product. Each point includes the attribute ‘Class’, which is the class assigned by visual interpretation of sub-meter resolution images, and the attribute ‘predClass’, which reflects the predicted class by the convolutional neural network. The ‘predClass’ values are the same as the raster files: [0] Other land covers that are not coconut palm. [1] Coconut palm. The attribute ‘Class’ contains the following values: [0] Land cover could not be determined because sub-meter resolution data was not available. [1] Other land covers that are not coconut palm. [2] Sparse coconut palm. Low density of coconut palms; between 1 and 4 coconut palms within the 20-meter pixel. [3] Dense open-canopy coconut palm; more than 4 coconut palms within the 20-meter pixel but coconut trees do not reach the full canopy closure. [4] Closed -canopy coconut palm; more than 4 coconut palms within the 20-meter pixel and coconut palms fully cover the ground. [5] Palm species that are not coconut palm.

Changelog v1-2:

• Pixels classified as class ‘coconut’ were reclassified to class ‘other’ in West Bengal.

This layer represents the Parcels Boundaries joined with the Property Information for each parcel in Palm Beach County and is in the WGS84 projection. Note: If filtering this data for your Municipality, use the CITY_CODE column alone and not preselects.

Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines. The provided land cover maps follow the high carbon stock approach (HCSA) stratifying vegetation based on the estimated carbon density (aboveground biomass). A deep convolutional neural network was trained to estimate canopy top height from Sentinel-2 optical satellite images using reference data derived from GEDI lidar waveforms. Carbon density and high carbon stock classes were derived from these dense canopy height maps using calibration data from an airborne lidar campaign in Sabah, Borneo. The resulting maps have a ground sampling distance (GSD) of 10 m and are based on images between 1st of September 2020 and 1st of March 2021.

The style files (color_style_HCS.qml, color_style_canopy_top_height.qml) contain the color coding and can be loaded for visualization (e.g. in QGIS).

The indicative HCS maps contain 9 land cover categories noted as "Label: name [colorcode]":

0: Open land (OL) [#440154] 1: Scrub (S) [#404387] 2: Young regenerating forest (YRF) [#29788e] 3: Low density forest (LDF) [#22a884] 4: Medium density forest (MDF) [#7ad251] 5: High density forest (HDF) [#fde725] 10: Oil palm [#fcffa4] 11: Coconut [#a4feff] 50: Urban [#fa0000] 255: No data

Citation: Use of these data require citation of this dataset and the original research articles. These citations are as follows:

Lang, N., Schindler, K., & Wegner, J. D. (2021). High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR. arXiv preprint arXiv:2107.07431.

Rodríguez, A. C., D'Aronco, S., Schindler, K., & Wegner, J. D. (2021). Mapping oil palm density at country scale: An active learning approach. Remote Sensing of Environment, 261, 112479.

Lang, N., Rodríguez, A. C., Schindler, K., & Wegner, J. D. (2021). Canopy top height and indicative high carbon stock maps for Indonesia, Malaysia, and Philippines (Version 1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.5012448

Vector polygon map data of city limits from Palm Bay, Florida containing 1 feature.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

Geospatial data about Palm Beach County, Florida Municipal Boundaries. Export to CAD, GIS, PDF, CSV and access via API.

Vegetation classification maps of 235 Pacific atolls (1,925.6 km2 in total) featuring four land cover classes (broadleaf tree canopy, coconut palm canopy, low vegetation, and non-vegetated surface) at 2 m resolution. Coconut palms are mapped with a balanced accuracy of 85.3%, producer’s accuracy (sensitivity or recall) of 82.5%, user’s accuracy (positive predictive value) of 68.7%, and specificity of 88.1%. Balanced accuracies for broadleaf tree canopy and low vegetation were lower (75.5% and 70.3%, respectively), in part because these classes often appear similar in satellite imagery. Non-vegetated land was classified with a balanced accuracy of 87.7%. The 235 classification maps feature an overall accuracy of 71.1%, significantly higher than the no-information rate of 34.4% (p = 2.2e−16). Across the 235 mapped atolls, 36.6±1.0% of vegetated surfaces featured a coconut palm canopy. By area, 58.3±1.8% of tree canopies (i.e. excluding low-statured vegetation) were coconut palm. A patch classifier identified 310.9 km2 of dense, monodominant coconut stands across the 235 mapped atolls, representing 51.2% of the study-wide coconut area. The classification maps are provided as georeferenced GeoTIFF files as well as PDF files for ease of viewing. Tabular databases including per-atoll and per-islet land cover data are also included, along with geopolitical and historical data about each atoll. Methods Maps are based on an interative random forest classification with spectral and textural features extracted from WorldView-2 imagery and trained and validated by human observers using 44,000 training points and nearly 1,969 validation points. See related manuscripts for more methodological informaion: Burnett, M.W., French, R., Jones, B., Fischer, A., Holland, A., Roybal, I., White, T.D., Steibl, S., Anderegg, L.D.L., Young, H., Holmes, N.D., Wegmann, A., 2024. Satellite imagery reveals widespread coconut plantations on Pacific atolls. Environmental Research Letters 19, 124095. https://doi.org/10.1088/1748-9326/ad8c66

Burnett, M.W., White, T.D., McCauley, D.J., De Leo, G.A., Micheli, F., 2019. Quantifying coconut palm extent on Pacific islands using spectral and textural analysis of very high resolution imagery. International Journal of Remote Sensing 40, 7329–7355. https://doi.org/10.1080/01431161.2019.1594440

Land use map of the Jambi area for the year 2019 based on Sentinel-1 and Sentinel-2 data. / Oil palm coverage for the years 2015-2021 based on Sentinel-1 data. / Yearly changes in oil palm coverage and total change in oil palm coverage between 2015 and 2021.

This dataset provides an inventory of state-managed park properties within Palm Beach County, FL. It represents the locations, boundary of park managed by the State of Florida. The purpose of this dataset is to support planning, management, and analysis related to state park properties in the region.

Land cover map of 2018 is part of land cover change map series of 2005 and 2018, developed in order to support the biodiversity of oil palm and non-oil palm research lead by Amy Ickowitz in southern Papua (part of Boven Digul and Merauke).

This dataset is a 10m resolution, Land Use/Land Cover (LULC) GeoTIFF, created from several open source data products and covering the Republic of Indonesia and the Federation of Malaysia for the year 2019/2020. This dataset is used in an interactive R Shiny application to generate landscape metrics at user defined locations. Lineage: This hybrid landcover GeoTIFF was created by mosaicking together the open source data products (i) ESRI 2020 Landcover, (ii) Global industrial and smallholder oil palm, (iii) Global Human Settlement and (iv) Open Street Map. Oil palm and human settlement data was resampled with the same origin (so that pixels align) and projection (EPSG:3857) as the ESRI 2020 Landcover data. Open street map waterways and roads were first converted from vector to raster, then resampled and projected to align with the other layers. The layers were then mosaicked together with the following ranking (high to low): Roads (Open Street Map), Waterways (Open Street Map), Global Human Settlement, Industrial and smallholder oil palm, and ESRI 2020 landcover. The LULC classes in the hybrid dataset are bare ground, built area, clouds, crops, flooded vegetation, grass, human settlement, industrial oil palm, smallholder oil palm, roads, scrub/shrub, trees, and water.

The U.S. Geological Survey (USGS) is coordinating the aquisition of high accuracy elevation data. Three formats of the data are available for each data set: .cor files which contain complete lists of Global Positioning System point files, .asc files which are the same as the .cor files but have been reformatted to process into ARC/INFO coverages, and .e00 files which are the ARC/INFO coverages. The files are available in the same 7.5- by 7.5-minute coverages as USGS quadrangles. The elevation data is collected on a 400 by 400 meter grid. The elevations are referenced to the horizontal North American Datum of 1983 (NAD83) and vertical North American Vertical Datum of 1988 (NAVD88).

 This project is performing regional topographic surveys to collect and provide elevation data to parameterize hydrologic and ecological numerical simulation models that are being developed for ecosystem restoration activities. Surveying services are also being rendered to provide vertical reference points for numerous water level gauges.

 Modeling of sheet flow and water surface levels in the wetlands of South Florida is very sensitive to changes in elevation due to the expansive and extremely low relief terrain. Hydrologists have determined minimum vertical accuracy requirements for the elevation data for use as input to hydrologic models. As a result, elevation data with a vertical accuracy specification of +/-15 centimeters (cm) relative to the North American Vertical Datum of 1988 (NAVD88) are being collected in critical areas using state-of-the-art differential global positioning system (GPS) technology and data processing techniques.

The data set contains 622 images of 100x100 km and covers the areas where oil palm plantations were detected at the global scale. The classification of oil palm plantations was firstly applied over a larger area where oil palm can potentially grow. The file 'grid.shp' contains the grid that covers the potential distribution of oil palm. The current data set, however, only contains the images where the presence of oil palm was confirmed. The file 'grid_withOP.shp' shows the 100x100 grid squares with presence of oil palm plantations. The classification images (in geotiff format) are the output of a convolutional neural network that takes Sentinel-1 and Sentinel-2 half-year composites as input data. The images have a spatial resolution of 10 meters and show three classes: 1) Industrial mature oil palm plantations, 2) Smallholder mature oil palm plantations, and 3) other land uses that are not mature oil palm. The file 'Validation_points_GlobalOilPalmLayer_2019.shp' includes the 13,252 points that were used to validate the product. Each point includes the attribute 'Class', which is the class assigned by visual interpretation, and the attribute 'predClass, which reflects the predicted class.

Land cover map of 2005 is part of land cover change map series of 2005 and 2018, developed in order to support the biodiversity of oil palm and non-oil palm research lead by Amy Ickowitz in southern Papua (part of Boven Digul and Merauke).

The PROBA-V Land Use @100m map for 2015-2020 covers the pan-tropical zone (23°S to 23°N) and consists of six annual maps, each with four bands. Bands 1 (B1) and 2 (B2) represent the land use at the beginning and end of each year, classified into four categories: "oil palm plantation" (value 1), "other perennial plantation" (value 2), "tropical forest" (value 3), and "other land use" (value 0). For full years (2016-2019), change detection runs from January 1 to December 16, with B1 and B2 corresponding to land use just before January 1 (as the first change is detected on this date) and after December 16, respectively. For the first year (2015) the period runs from May 1 to December 16, and for the last year (2020) from January 1 to April 30. Band 3 (B3) assigns a biweekly cutting date and band 4 (B4) assigns a biweekly planting date (value 1-24).