GEE-TED: A tsetse ecological distribution model for Google Earth Engine Please refer to the associated publication: Fox, L., Peter, B.G., Frake, A.N. and Messina, J.P., 2023. A Bayesian maximum entropy model for predicting tsetse ecological distributions. International Journal of Health Geographics, 22(1), p.31. https://link.springer.com/article/10.1186/s12942-023-00349-0 Description GEE-TED is a Google Earth Engine (GEE; Gorelick et al. 2017) adaptation of a tsetse ecological distribution (TED) model developed by DeVisser et al. (2010), which was designed for use in ESRI's ArcGIS. TED uses time-series climate and land-use/land-cover (LULC) data to predict the probability of tsetse presence across space based on species habitat preferences (in this case Glossina Morsitans). Model parameterization includes (1) day and night temperatures (MODIS Land Surface Temperature; MOD11A2), (2) available moisture/humidity using a vegetation index as a proxry (MODIS NDVI; MOD13Q1), (3) LULC (MODIS Land Cover Type 1; MCD12Q1), (4) year selections, and (5) fly movement rate (meters/16-days). TED has also been used as a basis for the development of an agent-based model by Lin et al. (2015) and in a cost-benefit analysis of tsetse control in Tanzania by Yang et al. (2017). Parameterization in Fox et al. (2023): Suitable LULC types and climate thresholds used here are specific to Glossina Morsitans in Kenya and are based on the parameterization selections in DeVisser et al. (2010) and DeVisser and Messina (2009). Suitable temperatures range from 17–40°C during the day and 10–40°C at night and available moisture is characterized as NDVI > 0.39. Suitable LULC comprises predominantly woody vegetation; a complete list of suitable categories is available in DeVisser and Messina (2009). In the Fox et al. (Forthcoming) publication, two versions of MCD12Q1 were used to assess suitable LULC types: Versions 051 and 006. The GeoTIFF supplied in this dataset entry (GEE-TED_Kenya_2016-2017.tif) uses the aforementioned parameters to show the probable tsetse distribution across Kenya for the years 2016-2017. A static graphic of this GEE-TED output is shown below and an interactive version can be viewed at: https://cartoscience.users.earthengine.app/view/gee-ted. Figure associated with Fox et al. (2023) GEE code The code supplied below is generalizable across geographies and species; however, it is highly recommended that parameterization is given considerable attention to produce reliable results. Note that output visualization on-the-fly will take some time and it is recommended that results be exported as an asset within GEE or exported as a GeoTIFF. Note: Since completing the Fox et al. (2023) manuscript, GEE has removed Version 051 per NASA's deprecation of the product. The current release of GEE-TED now uses only MCD12Q1 Version 006; however, alternative LULC data selections can be used with minimal modification to the code. // Input options var tempMin = 10 // Temperature thresholds in degrees Celsius var tempMax = 40 var ndviMin = 0.39 // NDVI thresholds; proxy for available moisture/humidity var ndviMax = 1 var movement = 500 // Fly movement rate in meters/16-days var startYear = 2008 // The first 2 years will be used for model initialization var endYear = 2019 // Computed probability is based on startYear+2 to endYear var country = 'KE' // Country codes - https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var crs = 'EPSG:32737' // See https://epsg.io/ for appropriate country UTM zone var rescale = 250 // Output spatial resolution var labelSuffix = '02052020' // For file export labeling only //[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] MODIS/006/MCD12Q1 var lulcOptions006 = [1,1,1,1,1,1,1,1,1, 0, 1, 0, 0, 0, 0, 0, 0] // 1 = suitable 0 = unsuitable // No more input required ------------------------------ // var region = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017") .filterMetadata('country_co', 'equals', country) // Input parameter modifications var tempMinMod = (tempMin+273.15)/0.02 var tempMaxMod = (tempMax+273.15)/0.02 var ndviMinMod = ndviMin*10000 var ndviMaxMod = ndviMax*10000 var ndviResolution = 250 var movementRate = movement+(ndviResolution/2) // Loading image collections var lst = ee.ImageCollection('MODIS/006/MOD11A2').select('LST_Day_1km', 'LST_Night_1km') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select('NDVI') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var lulc006 = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1') // Lulc mode and boolean reclassification var lulcMask = lulc006.mode().remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],lulcOptions006) .eq(1).rename('remapped').clip(region) // Merge NDVI and LST image collections var combined = ndvi.combine(lst, true) var combinedList = combined.toList(10000) // Boolean reclassifications (suitable/unsuitable) for day/night temperatures and ndvi var con =...

Forest cover is rapidly changing at the global scale as a result of land-use change (principally deforestation in many tropical regions and afforestation in many temperate regions) and climate change. However, a detailed map of global forest gain is still lacking at fine spatial and temporal resolutions. In this study, we developed a new automatic framework to map annual forest gain across the globe, based on Landsat time series, the LandTrendr algorithm and the Google Earth Engine (GEE) platform. First, samples of stable forest collected based on the Global Forest Change product (GFC) were used to determine annual Normalized Burn Ratio (NBR) thresholds for forest gain detection. Secondly, with the NBR time-series from 1982 to 2020 and LandTrendr algorithm, we produced dataset of global forest gain year from 1984 to 2020 based on a set of decision rules. Our results reveal that large areas of forest gain occurred in China, Russia, Brazil and North America, and the vast majority of the global forest gain has occurred since 2000. The new dataset was consistent in both spatial extent and years of forest gain with data from field inventories and alternative remote sensing products. Our dataset is valuable for policy-relevant research on the net impact of forest cover change on the global carbon cycle and provides an efficient and transferable approach for monitoring other types of land cover dynamics.

Geospatial raster data and vector data created in the frame of the study "Mapping Arctic Lake Ice Backscatter Anomalies using Sentinel-1 Time Series on Google Earth Engine" submitted to the journal "Remote Sensing" and Python code to reproduce the results.

In addition to the full repository (Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies.zip), two reduced alternatives of this repository are available due to large file size of the full repository:

Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies_without_IW_result_data.zip contains the same data and Python scripts as the full repository, but results based on IW data and tiled EW delta sigma0 images directly exported from Google Earth Engine have been removed. The merged data (from tiled EW delta sigma0 images) and all other results deduced thereof are included.

Supplement_to_RS_Arctic_Lake_Ice_Backscatter_Anomalies_scripts_and_reference_data_only.zip contains only the Python scripts and reference data. The directory structure was retained for better reproducibility.

Please see the associated README-files for details.

Results from time-series analysis of Landsat images in characterizing global forest extent and change. The 'first' and 'last' bands are reference multispectral imagery from the first and last available years for Landsat spectral bands corresponding to red, NIR, SWIR1, and SWIR2. Reference composite imagery represents median observations from a set of quality-assessed growing-season observations for each of these bands. Please see the User Notes for this update, as well as the associated journal article: Hansen, Potapov, Moore, Hancher et al. "High-resolution global maps of 21st-century forest cover change." Science 342.6160 (2013): 850-853.

Abstract: We present a detailed geomorphological map (1:5000-scale) of a middle mountainous area in Jena, Germany. To overcome limitations associated with traditional field-based approaches and to extend the possibility of manually digital mapping in a structural way, we propose an approach using geographic information systems (GIS) and high-resolution digital data. The geomorphological map features were extracted by manually interpreting and analyzing the combination of different data sources using light detection and ranging (LiDAR) data. A combination of topographic and geological maps, digital orthophotos (DOPs), Google Earth images, field investigations, and derivatives from digital terrain models (DTMs) revealed that it is possible to generate and present the geomorphologic features involved in classical mapping approaches. We found that LiDAR-DTM and land surface parameters (LSPs) can provide better results when incorporating the visual interpretation of multidirectional hillshade and LSP composite maps. The genesis of landforms can be readily identified, and findings enabled us to systematically delineate landforms and geomorphological process domains. Although our approach provides a cost effective, objective, and reproducible alternative for the classical approach, we suggest that further use of digital data should be undertaken to support analysis and applications.

LANDFIRE (LF), Landscape Fire and Resource Management Planning Tools, is a shared program between the wildland fire management programs of the U.S. Department of Agriculture's Forest Service, U.S. Department of the Interior's Geological Survey, and The Nature Conservancy. LANDFIRE (LF) layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees. Existing Vegetation Height (EVH) represents the average height of the dominant vegetation for a 30-m cell. Canopy height is generated separately for tree, shrub, and herbaceous lifeforms using training data and other geospatial layers. EVH is determined by the average height weighted by species cover and based on the Existing Vegetation Type (EVT) lifeform. Decision tree models using field reference data, lidar, Landsat, and ancillary data are developed separately for each lifeform. Decision tree relationships are used to generate lifeform specific height class layers, which are merged into a single composite EVH layer. Disturbance data were used to develop LF Remap products for LFRDB plot filtering and to ensure 2015 and 2016 disturbances were included that were not visible in the source imagery. The EVC product is then reconciled through QA/QC measures to ensure life-form is synchronized with both Existing Vegetation Cover and EVT products. LF uses EVH in several subsequent layers, including the development of the fuel products. The LANDIFRE Vegetation datasets include: Biophysical Settings (BPS) Environmental Site Potential (ESP) Existing Vegetation Canopy Cover (EVC) Existing Vegetation Height (EVH). Existing Vegetation Type (EVT) These layers are created using predictive landscape models based on extensive field-referenced data, satellite imagery and biophysical gradient layers using classification and regression trees.

These data were generated to map spatial burn severity and emissions of each historically observed large wildfires (>404 hectares (ha)) that burned during 1984–2020 in the state of California in the US. Event-based assessments were conducted at 30-m resolution for all fires and daily emissions were calculated at 500-m resolution for fires burned since 2002. A total of 1697 wildfires were assessed using the Wildfire Burn Severity and Emissions Inventory(WBSE) framework developed by Xu et al 2022. The comprehensive, long-term event and daily emissions records described here could be used to study health effects of wildfire smoke, either by combining them with transport modeling to model air quality and estimate exposures, or by incorporating them into statistical models predicting health impacts as a direct function of estimated emissions. These data will also facilitate analyses of changing emissions impacts on the carbon cycle over the last three decades. High resolution severity and emissions raster maps are generated for each fire event to support further spatial analysis. While the emissions calculated for California with WBSE are not a substitute for real-time daily emissions estimates, it is designed to extend the estimated emissions record back to 1984 with a finer spatial resolution and provide more up-to-date estimates on emissions factors reflecting information from California's recent extreme fires.

JavaScript code to be implemented in Google Earth Engine(c). The multi-scale relief model (MSRM) is a new algorithm for the visual interpretation of landforms using DSMs. The significance of this new method lies in its capacity to extract landform morphology from both high- and low-resolution DSMs independently of the shape or scale of the landform under study. This method thus provides important advantages compared to previous approaches as it: (1) allows the use of worldwide medium resolution models, such as SRTM, ASTER GDEM, ALOS, and TanDEM-X; (2) offers an alternative to traditional photograph interpretation that does not rely on the quality of the imagery employed nor on the environmental conditions and time of its acquisition; and (3) can be easily implemented for large areas using traditional GIS/RS software. The algorithm is tested in the Sutlej-Yamuna interfluve, which is a very large low-relief alluvial plain in northwest India where 10 000 km of palaeoriver channels have been mapped using MSRM.

ArcGIS geological map of Heard Island created using legacy field and sample data together with satellite imagery and published in Fox, Jodi M., et al. "Construction of an intraplate island volcano: The volcanic history of Heard Island." Bulletin of Volcanology 83.5 (2021): 37. The geological map was created in ArcMap 10.0 using satellite imagery, aerial photography, and historical maps and data. An initial map was generated by outlining geological features observed in the remote sensing images and the aerial photographs. This map was then cross-referenced with all available published and unpublished data to verify rock type, stratigraphic unit, and contact relationships. Where uncertainty in rock type or composition existed, the feature has been assigned to the stratigraphic unit without using a rock type label. In addition to published data, we collated and reviewed legacy unpublished maps, rock collections and unpublished data including hand-drawn sketches and notebooks.

Criteria for allocation of rocks to formations were not changed from previous work (Barling 1990; Barling 1994; Lambeth 1948; Lambeth 1952; Stephenson 1964).

Summary of the stratigraphy of Heard Island is as follows:

1. Unconsolidated Deposits (Recent) - Moraines, beach pebble, gravel and sand deposits.
2. Coastal Volcanic Cones (less than 15 ka) - Basaltic ash and scoria cones and associated small lavas.
3. Newer Lavas (~750 ka- Present) - Comprises the Laurens Peninsula Group and the Big Ben Group. 3a. Laurens Peninsula Group - Trachyte, tephrite, trachyandesite and basanite porphyritic lavas. Phenocrysts include clinopyroxene, olivine, plagioclase, kaersutite, magnetite, ilmenite and apatite. High TiO2 and P2O5 content. 3b. Big Ben Group - Basalt-trachybasalt and basanite porphyritic lavas. Basalt-trachybasalt phenocryts include olivine, clinopyroxene, plagioclase and Fe-Ti oxides. Basanite phenocrysts and megacrysts include olivine and clinopyroxene
4. Drygalski Formation (3.63-2.5 Ma) - Subhorizontal. Volcaniclastic breccia, conglomerate, sandstone and mudstone. Conglomerates are clast and matrix supported. Clasts are mainly basalt with minor trachyte, limestone and chert. Pillow lavas. Tillite. Microfossils include foraminifera and palynomorphs. Macrofossil - Austrochlamys heardensis
5. Laurens Peninsula Limestone (Middle Eocene-Middle Oligocene) - Thin, white, grey and blue styolitic carbonate interbedded with thin, soft tuffaceous shales. Lense of chert. Microfossils include foraminifera, coccoliths and palynomorphs. Intruded by trachybasalt and dolerite dykes (5 cm-2 m thick) and dolerite and gabbro sills. Folded and tilted.

For creation of the Heard Island geological map limestone and carbonate rocks were allocated to the Laurens Peninsula Limestones. Fresh, unaltered basalts were allocated to the Newer Lavas (Barling 1990). The Drygalski Formation includes all noncarbonate sedimentary rocks, clastic facies, and basalts between the Laurens Peninsula Limestones and the Newer Lavas (Barling 1990). Defining the boundary between the Drygalski Formation and the Newer Lavas is problematic, here we used the absence of chlorite as a criterion for allocating basalts to the Newer Lavas and the presence of basaltic pillows to allocate rocks to the Drygalski Formation consistent with Barling (1990). Although not ideal, these criteria were retained in the absence of more robust alternatives. Ridges of sediment in front of or adjacent to glaciers (current or since retreated) were mapped as moraines. Glacial retreat has been significant since the 1940s (~20 vol.% reduction), and locations where glaciers have been observed but have since retreated are relatively well known (Ruddell 2006). Ridges of unconsolidated sediment that have unclear relationships with glaciers and that could have been produced by aeolian and/or alluvial processes were mapped as unconsolidated sediment.

Remote Sensing Resources Utilised: 1. Mosaic of QuickBird satellite images of Heard Island (0.6m resolution) collected between 2006 and 2009 provided by the Australian Antarctic Division Data Centre (AADC). 2. Satellite imagery from Google™ Earth. Images collected 1984-2016. 3. Landsat 8 imagery from NASA via the USGS EarthExplorer online platform. Images collected 2013-2020. 4. Analogue aerial photographs collected in 1987 and held at the AADC

Published Resources Utilised 1. Barling J (1990) The petrogenesis of the Newer Lavas on Heard Island unpublished thesis. Department of Earth Sciences, Monash University, Melbourne 2. Barling J (1994) Origin and evolution of a high-Ti ocean island basalt suite; the Laurens Peninsula Series. Heard Island, Indian Ocean Mineralogical Magazine 58A:49–50 3. Barling J, Goldstein SJ,Wheller GE, Nicholls IA (1988) Heard Island; an example of large isotopic variations on a small oceanic island. Chemical Geology 70:46–46 4. Barling J, Goldstein SL, Nicholls IA (1994) Geochemistry of Heard Island (southern Indian Ocean); character...

Abstract This study carried out a mapping procedure focusing on apple orchards considering the planted area, spatial location, altitude range, slope interval, and presence of anti-hail nets in the city of São Joaquim (Southern Santa Catarina Plateau, Brazil). Spectral images from the Sentinel-2 orbital platform acquired in August 2018 and an enhanced digital elevation model from the Shuttle Radar Topography Mission (SRTM) were used. In a GIS application configured with the SIRGAS 2000,4 reference system and UTM cartographic projection, Sentinel-2 constellation images and digital elevation models from the SRTM mission and more recently refined with sensor data Phased Array type L-band Synthetic Aperture Radar (PALSAR) were added. All images were resampled to a spatial resolution of 10m. The results were validated based on high spatial resolution images available from Google Earth. The results show that São Joaquim has a planted area of 7,974.80 ha, and only 12% use an anti-hail coverage system. The majority of the orchards range from one to five ha and belong to small producers. More than 50% of the orchards are between 1,200 and 1,400 m in altitude, with 45% of orchards located in areas with slopes between 8 to 20%. Interestingly, most of the orchards are concentrated in a radius of up to 20km from the urban center of São Joaquim, where industries and cooperatives are located for packaging, processing, and logistics. This study demonstrated that orbital data from Sentinel-2 can effectively quantify the distribution of apple orchards, being a viable and effective alternative for collecting information for agricultural monitoring. In this way, it enables efficient planning of apple production, such as technical assistance, marketing with producers, and production flow.

Images composites de radiance moyenne mensuelle utilisant les données nocturnes de la bande jour/nuit (DNB) de la suite radiométrique Visible Infrared Imaging Radiometer (VIIRS). Comme ces données sont composées mensuellement, il est impossible d'obtenir une couverture de données de bonne qualité pour ce mois dans de nombreuses régions du monde. Cela peut être dû à…

This dataset provides long-term records of Suspended Sediment Concentration (SSC) for the mainstream of the Yangtze River (.tif), extending from Yibin, Sichuan Province, to the river estuary. The dataset spans the period from 1984 to 2021 and was derived from 30-meter resolution Landsat satellite observations.SSC values were retrieved using remote sensing reflectance derived from Landsat imagery (TM, ETM+, OLI, and OLI-2), following atmospheric correction and empirical regression models validated against in-situ measurements. The dataset provides annual and multi-year composite products that are suitable for hydrological, geomorphological, and environmental studies of the Yangtze River.In addition to the processed dataset, we also provide the Google Earth Engine (GEE) code (.JS) used to generate the SSC products. The code allows users to reproduce the dataset or extend it with alternative calibration models, temporal windows, or spatial domains.