A study was made of the composition, grain-size distribution, and organic content of grab samples collected from Boston Harbor. In general, the coarsest mean sizes occur in the channels scoured by dredging or tidal action, and the finest in areas where no dredging has occurred and where tidal velocities are at a minimum.

This land cover data set is derived from the original raster based Globcover regional (Africa) archive. It has been post-processed to generate a vector version at national extent with the LCCS regional legend (46 classes). This database can be analyzed in the GLCN software Advanced Database Gateway (ADG), which provides a user-friendly interface and advanced functionalities to breakdown the LCCS classes in their classifiers for further aggregations and analysis.

The data set is intended for free public access.

The shape file's attributes contain the following fields: -Area (sqm) -ID -Gridcode (Globcover cell value) -LCCCode (unique LCCS code)

You can download a zip archive containing: -the shape file (.shp) -the ArcGis layer file with global legend (.lyr) -the ArcView 3 legend file (.avl) -the LCCS legend tables (.xls)

Supplemental Information:

This land cover product is a vector version (ESRI shape) of the Globcover archive that was published in 2008 as result of an initiative launched in 2004 by the European Space Agency (ESA). Globcover is currently the most recent (2005) and resoluted (300 m) datasets on land cover globally. Given the need of this valuable information for environmental studies, natural resources management and policy formulation, through activities of the Global Land Cover Network (GLCN) programme, the Globcover has been reprocessed to generate databases at national extent that can be analyzed through the Advanced Database Gateway software (ADG) by GLCN. ADG is a cross-cutting interrogation software that allows the easy and fast recombination of land cover polygons according to the individual end-user requirements. Aggregated land cover classes can be generated not only by name, but also using the set of existing classifiers. ADG uses land cover data with a Land Cover Classification System (LCCS) legend. The ADG software is available for download on the GLCN web site at http://www.glcn.org/sof_7_en.jsp

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Antonio Martucci

Data lineage:

This land cover database is provided as ESRI shape file (vector format) and derives from reprocessing the raster based Globcover database (regional version). Globcover has undergone the following process: a) vectoralization at the national extent using ESRI ArcGis (arcinfo) 9.3; b) topological reconstruction (custom AML scripts launched inside ArcGis-arcinfo 9.3); c) simplification of areas according to a minimum mapping unit of 0.1 skim (10 ha) (custom AML scripts launched inside ArcGis-arcinfo 9.3); application of the FAO/UNEP Land Cover Classification System (LCCS) legend (46 classes); final processing to assure full compatibility with the GLCN software Advanced Database Gateway (ADG).

Online resources:

Download - Land cover of Mozambique - Shape file format

GLOBCOVER on the ESA Web site

Global Land Cover Network - GLCN

Single photon light detection and ranging (SPL LiDAR) is an active remote sensing technology for:

mapping vegetation aspects including cover, density and height representing the earth's terrain and elevation contours

We acquired SPL data on an airborne acquisition platform under leaf-on conditions to support Forest Resources Inventory (FRI) development.

FRI provides:

information to support resource management planning and land use decisions within Ontario’s Managed Forest Zone information on tree species, density, heights, ages and distribution

The SPL data point density ranges from a min of 25pts/m. Each point represents heights of objects such as:

ground level terrain points heights of vegetation buildings

The LiDAR was classified according to the Ontario LiDAR classifications. Low, medium and tall vegetation are classed as 3, 4, 5 and 12 classes.

The FRI SPL products include the following digital elevation models:

digital terrain model canopy height model digital surface model intensity model (signal width to return ratio) forest inventory raster metrics forest inventory attributes predicted streams hydro break lines block control points

LiDAR fMVA data supports developing detailed 3D analysis of:

forest inventory terrain hydrology infrastructure transportation

We made significant investments in Single Photon LiDAR data, now available on the Open Data Catalogue Derivatives are available for streaming or through download.

The map reflects areas with LiDAR data available for download. Zoom in to see data tiles and download options. Select individual tiles to download the data.

You can download:

classified point cloud data can also be downloaded via .laz format derivatives in a compressed .tiff format Forest Resource Inventory leaf-on LiDAR Tile Index (Download: Shapefile | File Geodatabase | GeoPackage )

Web raster services

You can access the data through our web raster services. For more information and tutorials, read the Ontario Web Raster Services User Guide.

If you have questions about how to use the Web raster services, email Geospatial Ontario (GEO) at geospatial@ontario.ca.

Note: Internal Users replace “https://ws.” with “https://intra.ws.”

CHM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_CHM_SPL/ImageServer DSM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_DSM_SPL/ImageServer DTM - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Elevation/FRI_DTM_SPL/ImageServer T1 Imagery - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/FRI_Imagery_T1/ImageServer T2 Imagery - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/FRI_T2_Imagery/ImageServer Land Cover - https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/Thematic/Ontario_Land_Cover_Compilation_v2/ImageServer

Service Endpoint

https://services1.arcgis.com/TJH5KDher0W13Kgo/arcgis/rest/services/FRI_Data_Access/FeatureServer

Additional Documentation

     Forest Resources Inventory | ontario.ca

Status

On going: data is being continually updated

Maintenance and Update Frequency

As needed: data is updated as deemed necessary

Contact

Natural Resources Information Unit, Forest Resources Inventory Program, FRI@ontario.ca

2010 US Census block boundaries attributed with a subset of the PL94-171 demographic data released March 2011. Please contact the Hennepin GIS Office if you require additional PL94-171 fields. Downloaded the 2010 US Census block boundary shapefile from the US Census Bureau at the following address: http://www.census.gov/cgi-bin/geo/shapefiles2010/main Downloaded the PL94-171 demographic data from the following address: http://www.census.gov/rdo/data/2010_census_redistricting_data_pl_94-171_summary_files.html Post processed the demographic data per instructions. Stored in Access database. Set up SQL Query to create a subset of the data based on feedback from HSPHD and the GIS Office. Database documentation on file at the GIS Office. Imported the shapefile and demographic data into a File geodatabase. Joined and saved the data sets. Performed error checking by comparing against State and Maptitude versions. The TIGER/Line Files are shapefiles and related database files (.dbf) that are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line File is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census Blocks are statistical areas bounded on all sides by visible features, such as streets, roads, streams, and railroad tracks, and/or by nonvisible boundaries such as city, town, township, and county limits, and short line-of-sight extensions of streets and roads. Census blocks are relatively small in area; for example, a block in a city bounded by streets. However, census blocks in remote areas are often large and irregular and may even be many square miles in area. A common misunderstanding is that data users think census blocks are used geographically to build all other census geographic areas, rather all other census geographic areas are updated and then used as the primary constraints, along with roads and water features, to delineate the tabulation blocks. As a result, all 2010 Census blocks nest within every other 2010 Census geographic area, so that Census Bureau statistical data can be tabulated at the block level and aggregated up to the appropriate geographic areas. Census blocks cover all territory in the United States, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands). Blocks are the smallest geographic areas for which the Census Bureau publishes data from the decennial census. A block may consist of one or more faces.

Link to Attribute Table Information: http://gis.hennepin.us/OpenData/Metadata/2010%20Census%20Blocks.pdf

Use Limitations: This data (i) is furnished "AS IS" with no representation as to completeness or accuracy; (ii) is furnished with no warranty of any kind; and (iii) is not suitable for legal, engineering or surveying purposes. Hennepin County shall not be liable for any damage, injury or loss resulting from this data.

© Hennepin County GIS Office This layer is a component of Datasets for Hennepin County AGOL and Hennepin County Open Data..

The USGS Governmental Unit Boundaries service from The National Map (TNM) represents major civil areas for the Nation, including States or Territories, counties (or equivalents), Federal and Native American areas, congressional districts, minor civil divisions, incorporated places (such as cities and towns), and unincorporated places. Boundaries data are useful for understanding the extent of jurisdictional or administrative areas for a wide range of applications, including mapping or managing resources, and responding to natural disasters. Boundaries data also include extents of forest, grassland, park, wilderness, wildlife, and other reserve areas useful for recreational activities, such as hiking and backpacking. Boundaries data are acquired from a variety of government sources. The data represents the source data with minimal editing or review by USGS. Please refer to the feature-level metadata for information on the data source. The National Map boundaries data is commonly combined with other data themes, such as elevation, hydrography, structures, and transportation, to produce general reference base maps. The National Map download client allows free downloads of public domain boundaries data in either Esri File Geodatabase or Shapefile formats. See https://apps.nationalmap.gov/help/ for assistance with The National Map viewer, download client, services, or metadata.

Beaver ponds shape file. The following archive contains a shapefile of 10855 polygons corresponding to beaver ponds with a minimum mapping size of 500 m2. All these polygons are within a watershed (47,880743 -78,717634) located in Abibiti, Québec, Canada. Beaver ponds were delineated using the Normalized Difference Water Index (NDWI) over PlanetScope 3m spatial resolution scenes. The NDWI yields an index that ranges between -1 and 1, where open-water pixels approach 1 and terrestrial pixels -1. To set the threshold for water pixels, the Otsu adaptive threshold was used, the optimal threshold is identified by minimizing the inter-class variance and maximizing the intra-class variance.

The Watershed Boundary Dataset (WBD) from USGS' The National Map (TNM) defines the perimeter of drainage areas formed by the terrain and other landscape characteristics. The drainage areas are nested within each other so that a large drainage area, such as the Sacramento River, will be composed of multiple smaller drainage areas, such as the Feather River. Each of these smaller areas can further be subdivided into smaller and smaller drainage areas. The WBD uses six different levels in this hierarchy, with the smallest averaging about 30,000 acres. The WBD is made up of polygons nested into six levels of data respectively defined by Regions, Subregions, Basins, Subbasins, Watersheds, and Subwatersheds. For additional information on the WBD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography/watershed-boundary-dataset. DWR was the steward for NHD and WBD in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's WBD improvements are sent to USGS for certification and incorporation to the national geodatabase. The certified WBD is included within the National Hydrography Dataset downloadable file geodatabase, and is also available in shapefile format and as web map services accessible from the USGS website. (https://www.usgs.gov/core-science-systems/ngp/national-hydrography/access-national-hydrography-products).

The final static version of the WBD was published in January 2025. No edits to this dataset are being accepted by USGS. Future mapping of drainage areas will be done by USGS as the 3D Hydrography Program (3DHP) is built out. DWR and its stewardship partners are actively producing LiDAR-derived hydrography data for inclusion in the California portion of 3DHP data and eventually its companion set of drainage areas. For now, the WBD is considered the authoritative source for watershed delineations in California.

This dataset includes one file for each of the 51 counties that were collected, as well as a CA_Merged file with the parcels merged into a single file.Note – this data does not include attributes beyond the parcel ID number (PARNO) – that will be provided when available, most likely by the state of California.DownloadA 1.6 GB zipped file geodatabase is available for download - click here.DescriptionA geodatabase with parcel boundaries for 51 (out of 58) counties in the State of California. The original target was to collect data for the close of the 2013 fiscal year. As the collection progressed, it became clear that holding to that time standard was not practical. Out of expediency, the date requirement was relaxed, and the currently available dataset was collected for a majority of the counties. Most of these were distributed with minimal metadata.The table “ParcelInfo” includes the data that the data came into our possession, and our best estimate of the last time the parcel dataset was updated by the original source. Data sets listed as “Downloaded from” were downloaded from a publicly accessible web or FTP site from the county. Other data sets were provided directly to us by the county, though many of them may also be available for direct download. Â These data have been reprojected to California Albers NAD84, but have not been checked for topology, or aligned to county boundaries in any way. Tulare County’s dataset arrived with an undefined projection and was identified as being California State Plane NAD83 (US Feet) and was assigned by ICE as that projection prior to reprojection. Kings County’s dataset was delivered as individual shapefiles for each of the 50 assessor’s books maintained at the county. These were merged to a single feature class prior to importing to the database.The attribute tables were standardized and truncated to include only a PARNO (APN). The format of these fields has been left identical to the original dataset. The Data Interoperablity Extension ETL tool used in this process is included in the zip file. Where provided by the original data sources, metadata for the original data has been maintained. Please note that the attribute table structure changes were made at ICE, UC Davis, not at the original data sources.Parcel Source InformationCountyDateCollecDateCurrenNotesAlameda4/8/20142/13/2014Download from Alamenda CountyAlpine4/22/20141/26/2012Alpine County PlanningAmador5/21/20145/14/2014Amador County Transportation CommissionButte2/24/20141/6/2014Butte County Association of GovernmentsCalaveras5/13/2014Download from Calaveras County, exact date unknown, labelled 2013Contra Costa4/4/20144/4/2014Contra Costa Assessor’s OfficeDel Norte5/13/20145/8/2014Download from Del Norte CountyEl Dorado4/4/20144/3/2014El Dorado County AssessorFresno4/4/20144/4/2014Fresno County AssessorGlenn4/4/201410/13/2013Glenn County Public WorksHumboldt6/3/20144/25/2014Humbodt County AssessorImperial8/4/20147/18/2014Imperial County AssessorKern3/26/20143/16/2014Kern County AssessorKings4/21/20144/14/2014Kings CountyLake7/15/20147/19/2013Lake CountyLassen7/24/20147/24/2014Lassen CountyLos Angeles10/22/201410/9/2014Los Angeles CountyMadera7/28/2014Madera County, Date Current unclear likely 7/2014Marin5/13/20145/1/2014Marin County AssessorMendocino4/21/20143/27/2014Mendocino CountyMerced7/15/20141/16/2014Merced CountyMono4/7/20144/7/2014Mono CountyMonterey5/13/201410/31/2013Download from Monterey CountyNapa4/22/20144/22/2014Napa CountyNevada10/29/201410/26/2014Download from Nevada CountyOrange3/18/20143/18/2014Download from Orange CountyPlacer7/2/20147/2/2014Placer CountyRiverside3/17/20141/6/2014Download from Riverside CountySacramento4/2/20143/12/2014Sacramento CountySan Benito5/12/20144/30/2014San Benito CountySan Bernardino2/12/20142/12/2014Download from San Bernardino CountySan Diego4/18/20144/18/2014San Diego CountySan Francisco5/23/20145/23/2014Download from San Francisco CountySan Joaquin10/13/20147/1/2013San Joaquin County Fiscal year close dataSan Mateo2/12/20142/12/2014San Mateo CountySanta Barbara4/22/20149/17/2013Santa Barbara CountySanta Clara9/5/20143/24/2014Santa Clara County, Required a PRA requestSanta Cruz2/13/201411/13/2014Download from Santa Cruz CountyShasta4/23/20141/6/2014Download from Shasta CountySierra7/15/20141/20/2014Sierra CountySolano4/24/2014Download from Solano Couty, Boundaries appear to be from 2013Sonoma5/19/20144/3/2014Download from Sonoma CountyStanislaus4/23/20141/22/2014Download from Stanislaus CountySutter11/5/201410/14/2014Download from Sutter CountyTehama1/16/201512/9/2014Tehama CountyTrinity12/8/20141/20/2010Download from Trinity County, Note age of data 2010Tulare7/1/20146/24/2014Tulare CountyTuolumne5/13/201410/9/2013Download from Tuolumne CountyVentura11/4/20146/18/2014Download from Ventura CountyYolo11/4/20149/10/2014Download from Yolo CountyYuba11/12/201412/17/2013Download from Yuba County

Version InformationThe data is updated annually with fire perimeters from the previous calendar year.Firep23_1 was released in May 2024. Two hundred eighty four fires from the 2023 fire season were added to the database (21 from BLM, 102 from CAL FIRE, 72 from Contract Counties, 19 from LRA, 9 from NPS, 57 from USFS and 4 from USFW). The 2020 Cottonwood fire, 2021 Lone Rock and Union fires, as well as the 2022 Lost Lake fire were added. USFW submitted a higher accuracy perimeter to replace the 2022 River perimeter. A duplicate 2020 Erbes fire was removed. Additionally, 48 perimeters were digitized from an historical map included in a publication from Weeks, d. et al. The Utilization of El Dorado County Land. May 1934, Bulletin 572. University of California, Berkeley. There were 2,132 perimeters that received updated attribution, the bulk of which had IRWIN IDs added. The following fires were identified as meeting our collection criteria, but are not included in this version and will hopefully be added in the next update: Big Hill #2 (2023-CAHIA-001020). YEAR_ field changed to a short integer type. San Diego CAL FIRE UNIT_ID changed to SDU (the former code MVU is maintained in the UNIT_ID domains). COMPLEX_INCNUM renamed to COMPLEX_ID and is in process of transitioning from local incident number to the complex IRWIN ID. Perimeters managed in a complex in 2023 are added with the complex IRWIN ID. Those previously added will transition to complex IRWIN IDs in a future update.If you would like a full briefing on these adjustments, please contact the data steward, Kim Wallin (kimberly.wallin@fire.ca.gov), CAL FIRE FRAP._CAL FIRE (including contract counties), USDA Forest Service Region 5, USDI Bureau of Land Management & National Park Service, and other agencies jointly maintain a fire perimeter GIS layer for public and private lands throughout the state. The data covers fires back to 1878. Current criteria for data collection are as follows:CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 damaged/ destroyed residential or commercial structures, and/or caused ≥1 fatality.All cooperating agencies submit perimeters ≥10 acres._Discrepancies between wildfire perimeter data and CAL FIRE Redbook Large Damaging FiresLarge Damaging fires in California were first defined by the CAL FIRE Redbook, and has changed over time, and differs from the definition initially used to define wildfires required to be submitted for the initial compilation of this digital fire perimeter data. In contrast, the definition of fires whose perimeter should be collected has changed once in the approximately 30 years the data has been in existence. Below are descriptions of changes in data collection criteria used when compiling these two datasets. To facilitate comparison, this metadata includes a summary, by year, of fires in the Redbook, that do not appear in this fire perimeter dataset. It is followed by an enumeration of each “Redbook” fire missing from the spatial data. Wildfire Perimeter criteria:~1991: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three residence or one commercial structure or does $300,000 worth of damage 2002: 10 acres timber, 50 acres brush, 300 acres grass, damages or destroys three or more structures, or does $300,000 worth of damage~2010: 10 acres timber, 30 acres brush, 300 acres grass, damages or destroys three or more structures (doesn’t include out building, sheds, chicken coops, etc.)Large and Damaging Redbook Fire data criteria:1979: Fires of a minimum of 300 acres that burn at least: 30 acres timber or 300 acres brush, or 1500 acres woodland or grass1981: 1979 criteria plus fires that took ,3000 hours of California Department of Forestry and Fire Protection personnel time to suppress1992: 1981 criteria plus 1500 acres agricultural products, or destroys three residence or one commercial structure or does $300,000 damage1993: 1992 criteria but “three or more structures destroyed” replaces “destroys three residence or one commercial structure” and the 3,000 hours of California Department of Forestry personnel time to suppress is removed2006: 300 acres or larger and burned at least: 30 acres of timber, or 300 acres of brush, or 1,500 acres of woodland, or 1,500 acres of grass, or 1,500 acres of agricultural products, or 3 or more structures destroyed, or $300,000 or more dollar damage loss.2008: 300 acres and largerYear# of Missing Large and Damaging Redbook Fires197922198013198115198261983319842019855219861219875619882319898199091991219921619931719942219959199615199791998101999720004200152002162003520042200512006112007320084320093201022011020124201322014720151020162201711201862019220203202102022020230Total488Enumeration of fires in the Redbook that are missing from Fire Perimeter data. Three letter unit code follows fire name.1979-Sylvandale (HUU), Kiefer (AEU), Taylor(TUU), Parker#2(TCU), PGE#10, Crocker(SLU), Silver Spur (SLU), Parkhill (SLU), Tar Springs #2 (SLU), Langdon (SCU), Truelson (RRU), Bautista (RRU), Crocker (SLU), Spanish Ranch (SLU), Parkhill (SLU), Oak Springs(BDU), Ruddell (BDF), Santa Ana (BDU), Asst. #61 (MVU), Bernardo (MVU), Otay #20 1980– Lightning series (SKU), Lavida (RRU), Mission Creek (RRU), Horse (RRU), Providence (RRU), Almond (BDU), Dam (BDU), Jones (BDU), Sycamore (BDU), Lightning (MVU), Assist 73, 85, 138 (MVU)1981– Basalt (LNU), Lightning #25(LMU), Likely (MNF), USFS#5 (SNF), Round Valley (TUU), St. Elmo (KRN), Buchanan (TCU), Murietta (RRU), Goetz (RRU), Morongo #29 (RRU), Rancho (RRU), Euclid (BDU), Oat Mt. (LAC & VNC), Outside Origin #1 (MVU), Moreno (MVU)1982- Duzen (SRF), Rave (LMU), Sheep’s trail (KRN), Jury (KRN), Village (RRU), Yuma (BDF)1983- Lightning #4 (FKU), Kern Co. #13, #18 (KRN)1984-Bidwell (BTU), BLM D 284,337, PNF #115, Mill Creek (TGU), China hat (MMU), fey ranch, Kern Co #10, 25,26,27, Woodrow (KRN), Salt springs, Quartz (TCU), Bonanza (BEU), Pasquel (SBC), Orco asst. (ORC), Canel (local), Rattlesnake (BDF)1985- Hidden Valley, Magic (LNU), Bald Mt. (LNU), Iron Peak (MEU), Murrer (LMU), Rock Creek (BTU), USFS #29, 33, Bluenose, Amador, 8 mile (AEU), Backbone, Panoche, Los Gatos series, Panoche (FKU), Stan #7, Falls #2 (MMU), USFS #5 (TUU), Grizzley, Gann (TCU), Bumb, Piney Creek, HUNTER LIGGETT ASST#2, Pine, Lowes, Seco, Gorda-rat, Cherry (BEU), Las pilitas, Hwy 58 #2 (SLO), Lexington, Finley (SCU), Onions, Owens (BDU), Cabazon, Gavalin, Orco, Skinner, Shell, Pala (RRU), South Mt., Wheeler, Black Mt., Ferndale, (VNC), Archibald, Parsons, Pioneer (BDU), Decker, Gleason(LAC), Gopher, Roblar, Assist #38 (MVU)1986– Knopki (SRF), USFS #10 (NEU), Galvin (RRU), Powerline (RRU), Scout, Inscription (BDU), Intake (BDF), Assist #42 (MVU), Lightning series (FKU), Yosemite #1 (YNP), USFS Asst. (BEU), Dutch Kern #30 (KRN)1987- Peach (RRU), Ave 32 (TUU), Conover (RRU), Eagle #1 (LNU), State 767 aka Bull (RRU), Denny (TUU), Dog Bar (NEU), Crank (LMU), White Deer (FKU), Briceburg (LMU), Post (RRU), Antelope (RRU), Cougar-I (SKU), Pilitas (SLU) Freaner (SHU), Fouts Complex (LNU), Slides (TGU), French (BTU), Clark (PNF), Fay/Top (SQF), Under, Flume, Bear Wallow, Gulch, Bear-1, Trinity, Jessie, friendly, Cold, Tule, Strause, China/Chance, Bear, Backbone, Doe, (SHF) Travis Complex, Blake, Longwood (SRF), River-II, Jarrell, Stanislaus Complex 14k (STF), Big, Palmer, Indian (TNF) Branham (BLM), Paul, Snag (NPS), Sycamore, Trail, Stallion Spring, Middle (KRN), SLU-864 1988- Hwy 175 (LNU), Rumsey (LNU), Shell Creek (MEU), PG&E #19 (LNU), Fields (BTU), BLM 4516, 417 (LMU), Campbell (LNF), Burney (SHF), USFS #41 (SHF), Trinity (USFS #32), State #837 (RRU), State (RRU), State (350 acres), RRU), State #1807, Orange Co. Asst (RRU), State #1825 (RRU), State #2025, Spoor (BDU), State (MVU), Tonzi (AEU), Kern co #7,9 (KRN), Stent (TCU), 1989– Rock (Plumas), Feather (LMU), Olivas (BDU), State 1116 (RRU), Concorida (RRU), Prado (RRU), Black Mt. (MVU), Vail (CNF)1990– Shipman (HUU), Lightning 379 (LMU), Mud, Dye (TGU), State 914 (RRU), Shultz (Yorba) (BDU), Bingo Rincon #3 (MVU), Dehesa #2 (MVU), SLU 1626 (SLU)1991- Church (HUU), Kutras (SHF)1992– Lincoln, Fawn (NEU), Clover, fountain (SHU), state, state 891, state, state (RRU), Aberdeen (BDU), Wildcat, Rincon (MVU), Cleveland (AEU), Dry Creek (MMU), Arroyo Seco, Slick Rock (BEU), STF #135 (TCU)1993– Hoisington (HUU), PG&E #27 (with an undetermined cause, lol), Hall (TGU), state, assist, local (RRU), Stoddard, Opal Mt., Mill Creek (BDU), Otay #18, Assist/ Old coach (MVU), Eagle (CNF), Chevron USA, Sycamore (FKU), Guerrero, Duck1994– Schindel Escape (SHU), blank (PNF), lightning #58 (LMU), Bridge (NEU), Barkley (BTU), Lightning #66 (LMU), Local (RRU), Assist #22 & #79 (SLU), Branch (SLO), Piute (BDU), Assist/ Opal#2 (BDU), Local, State, State (RRU), Gilman fire 7/24 (RRU), Highway #74 (RRU), San Felipe, Assist #42, Scissors #2 (MVU), Assist/ Opal#2 (BDU), Complex (BDF), Spanish (SBC)1995-State 1983 acres, Lost Lake, State # 1030, State (1335 acres), State (5000 acres), Jenny, City (BDU), Marron #4, Asist #51 (SLO/VNC)1996- Modoc NF 707 (Ambrose), Borrego (MVU), Assist #16 (SLU), Deep Creek (BDU), Weber (BDU), State (Wesley) 500 acres (RRU), Weaver (MMU), Wasioja (SBC/LPF), Gale (FKU), FKU 15832 (FKU), State (Wesley) 500 acres, Cabazon (RRU), State Assist (aka Bee) (RRU), Borrego, Otay #269 (MVU), Slaughter house (MVU), Oak Flat (TUU)1997- Lightning #70 (LMU), Jackrabbit (RRU), Fernandez (TUU), Assist 84 (Military AFV) (SLU), Metz #4 (BEU), Copperhead (BEU), Millstream, Correia (MMU), Fernandez (TUU)1998- Worden, Swift, PG&E 39 (MMU), Chariot, Featherstone, Wildcat, Emery, Deluz (MVU), Cajalco Santiago (RRU)1999- Musty #2,3 (BTU), Border # 95 (MVU), Andrews,