North Norfolk report auto generated by @geofolio

1. Table of Contents
Introduction
1. Land Cover
2. Topography
3. Climate
4. Soils
5. Hydrology and Water Resources
6. Agriculture
Attribution and References
Notes
Overview
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Introduction
The area of interest is located in United Kingdom, and overlaps the Level 1 administrative
divisions of England (100%). [1]
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2. Map: Geofolio. Background: Mapbox/Openstreetmap Contributors.
1. Land Cover
Detailed zoomable map of the area of interest is based on OpenStreetMap data, formatted in the style of Mapbox Streets. The smaller overviews are based on
OpenStreetMap data, formatted in Mapbox Light style.
Land cover data is collected from the ESA Climate Change Initiative (CCI) Land Cover dataset [2].
The ESA-CCI dataset contains annual land cover maps at 300m resolution from 1992 to 2015. The
distribution of land cover classes in the study area in 2015 are shown below, as well as the
percentage di erence compared to 1995. This may give an indication of trends in land cover
changes in the area.
Table 1. Land Cover Classification
Coverage [%] Classification Change
45.9% Herbaceous 1.3%
38.9% Water bodies 0.0%
4.2% Grassland 0.2%
2.9% Mosaic cropland and natural vegetation 0.0%
1.8% Urban areas 1.3%
1.7% Mosaic natural vegetation 0.0%
1.2% Shrub or herbaceous cover, flooded 0.0%
1.1% Tree cover, needleleaved, evergreen, closed to open 0.0%
1.0% Mosaic tree and shrub 0.0%
0.6% Mosaic herbaceous cover 0.0%
Note: Only classes with more than 0.5% area are listed in this table to prevent listing classes with very few pixels. Source: ESA Climate Change Initiative.
[2]
Land Cover Classification (2015)
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Cropland (rainfed) Herbaceous
Trees or shrubs Mosaic cropland and natural vegetation
Mosaic natural vegetation Tree cover, broadleaved, deciduous, closed to open
Tree cover, needleleaved, evergreen, closed to open Tree cover, mixed leaf type
Mosaic tree and shrub Mosaic herbaceous cover
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3. 2. Topography
Map: Geofolio. Data: ESA Climate Change Initiative. Backgrounds: Mapbox/Openstreetmap Contributors.
Grassland Sparse vegetation (tree, shrub, herbaceous cover)
Shrub or herbaceous cover, flooded Urban areas
Bare areas Consolidated bare areas
Unconsolidated bare areas Water bodies
Land cover classification data from ESA Climate Change Initiative. Data from 2015 is shown in the map. Classes which are not present in the area of interest are
not shown in the legend. Dataset is available at 300m spatial resolution, and created from MERIS, AVHRR, and PROVA-V satellite imagery.
Topography describes the shape and features of land surfaces. The topography of an area
usually also a ects other processes such as precipitation and temperature, as higher areas are
usually cooler and receive more precipitation than lower areas. Digital models of the surface can
also be used to calculate topographic derivates such as slope (steepness) and aspect (direction)
of a surface, or to estimate which direction water will flow and how much terrain lies upstream
or downstream from a particular point.
Elevation
There are several global elevation datasets available at a range of di erent resolutions. Most
commonly used are the SRTM (30m or 90m resolution), the ASTER GDEM dataset (30m
resolution), or the JAXA ALOS World 3D dataset (30m resolution).
Elevation data for this area of interest is collected from the SRTM GL1 v3 elevation dataset at
30m spatial resolution [3], which is shown in the map below. The elevation in the area ranges
from a minimum of -41m to a maximum of 261m, with an average elevation of 31m. While the
range between minimum and maximum elevation is 302m, the largest part (90%) of the area has
an elevation of between 0m and 116m.
SRTM GL1 (Version 3) Digital Elevation Model
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-14
6
27
48
69
90
111
Elevation data from SRTM GL1 Version 3 is collected via OpenTopography.org services. SRTM data was collected during a Space Shuttle mission in February
2000, and is available in latitudes between 60 degrees North and 56 degrees South. GL1 Version 3 is the latest version at 30m resolution, in which voids in the
dataset have been filled with ASTER GDEM2 the USGS GMTED2010 and USGS NED datasets
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4. 3. Climate
Map: Geofolio. Data: SRTM GL1 Version 3. Backgrounds: Mapbox/Openstreetmap Contributors.
dataset have been filled with ASTER GDEM2, the USGS GMTED2010, and USGS NED datasets.
Temperature and Precipitation
Current temperature and precipitation data is obtained from the WorldClim climate database
[4], which is a gridded set of global climate data with a spatial resolution of 1 sq. km.
The average precipitation in the area is 658mm per year, and there is a low variation throughout
the year, with the wettest month receiving an average of 67mm, and the driest month receiving
40mm.
The average temperature is 10 degrees Celcius, with a low variation throughout the year.
Average Monthly Precipitation (mm/month)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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60
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Average Monthly Temperature (degrees Celcius)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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30
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Annual Precipitation [mm]
Map: Geofolio. Data: WorldClim. Backgrounds: Mapbox/Openstreetmap Contributors.
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582
606
631
656
680
705
730
Map of average annual precipitation created by calculating the sum of
monthly averages.
Climate Change
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5. 4. Soils
5. Hydrology and Water Resources
The climate change section of this factsheet is under development. It will contain a short
explanation of climate change scenarios, as well as an overview of the most important e ects on
this area of interest.
A global dataset of soil parameter predictions has been created at ISRIC and is now maintained
the LandGIS service [5]. This dataset contains spatial predictions for a wide range of soil
parameters at a global scale. By combining data on actual measurements with prediction
algorithms, soil parameters can be predicted in an area based on measurements that have been
done in similar conditions.
Texture
Texture is an imporant soil parameter and refers to the proportion of sand, silt, and clay sized
particles in the soil. Sand particles are the largest (above 0.05mm), then silt (from 0.05 to
0.002mm), and clay particles are the smallest (smaller than 0.002mm).
The way in which these three proportions are combined defines the texture of the soil. Soil
texture can be indicative of the amount of water the soil can hold, how fast water can move
through it, and how workable and fertile the soil is.
In the area of interest, soil textures of loam (89.4%) , sandy loam (7.6%) , and clay loam (2.3%)
are predicted.
Soil Texture Predictions
Map: Geofolio. Data: LandGIS Contributors. Backgrounds: Mapbox/Openstreetmap Contributors.
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Clay Loam Silty Clay Loam
Sandy Clay Loam Loam
Silt Loam Sandy Loam
Map shows predicted USDA soil texture classes at 0cm depth. The classification is made by mapping predicted sand, silt, and loam fraction to a texture using
the USDA soil texture classification triangle.
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6. The hydrology and water resources section gives an introduction to the hydrological situation in
and around the area of interest. Catchment and watersheds have been extracted from the
HydroBASINS dataset [6], and delineated and colored if they flow out of the area of interest in a
di erent location. The river system is extracted from the global river classification (GloRiC)
dataset [7], and locations of dams from the global dams and reservoirs (GRanD) dataset by
Global Dam Watch [8].
Subcatchments and upstream area can be selected by hovering over the map, and the locations
of dams in the hydrological system have been marked with a red dot .
Hydrological Overview
Map: Geofolio. Data: HydroBASINS, GLORIC, GRanD. Backgrounds: Mapbox/Openstreetmap Contributors.
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The hydrological overview map shows catchments based on the HydroBASINS dataset. Subcatchments have disctinct colors based on where the outlet crosses
the boundary of the selected area of interest. It may well be that two catchments with di erent colors still merge shortly a er leaving the area of interest.
The locations of dams are marked with and are based on the Global Rivers and Dams (GRanD) dataset. The river network is from the Global River
Classification (GloRiC) dataset. Both HydroBASINS and GloRiC are based on the 90m SRTM hydrologically corrected elevation data by HydroSHEDS, which has
reduced accurracy in extreme latitudes.
6. Agriculture
The Global Food Security Analysis Support Data project (GFSAD30) is an e ort to map and
produce global cropland data products at 30m resolution [9, 10]. GFSAD30 is currently one of the
only datasets to specifically identify croplands at a high resolution. Croplands are defined as
land which is cultivated with plants harvested for food, feed, or fiber. Fallow land which is
uncultivated or out of season is also classified as cropland.
The selected area of interest has a total area of 6338 square kilometers, and contains a very
large proportion of cropland. In total, 89% is classified as cropland with the remaining 11%
classified as non-cropland. The cropland areas comprise a total of 5645 square kilometers
(564586 hectare) and are shown in the map below.
Cropland Mask (High Resolution)
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7. Map: Geofolio. Data: GFSAD30/Croplands.org. Backgrounds: Mapbox/Openstreetmap Contributors.
Cropland
Map shows a high-resolution (30m) cropland mask produced by the Global Food Security Analysis Support Data project (GFSAD30) project.
Cropland is defined as lands cultivated with plants harvested for food, feed, and fiber, including both seasonal crops (wheat, rice, etc) as well as continuous
plantations (co ee, tea, cocoa, etc). Fallow land which is uncultivated for a season is also included in this classification. The dataset was created using two
machine-learning algorithms and was further refined using Landsat imagery for the period 2013-2015.
Crops
There are no global datasets to identify exactly which crops are grown where, especially at the
same level of detail as the cropland map above. However, a global dataset by Monfreda et al.
[11] contains harvested areas for various crops at a regional resolution.
In and around the study area, the dataset by Monfreda et al. indicates that the most common
crops by (approximate) area harvested are wheat, mixed grass, and barley. Other crops like
sugar beer and rapeseed may also be cultivated in and around this area.
Crop Calendars
Crop calendars give an indication of the growing season of various crops in and around the study
area, and specify approximate periods during the year for sowing and harvesting of crops. The
crop calendars for this area have been extracted from a global crop calendar dataset by Sacks et
al., which combines calendars from USDA, FAO, and several other sources [12].
Table 2. Crop Calendars
Location Crop Source Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
United Kingdom Barley MWCACP Updates
United Kingdom Wheat (Winter) MWCACP Updates
United Kingdom Sugarbeets MWCACP Updates
United Kingdom Rapeseed (Winter) MWCACP Updates
United Kingdom Barley (Winter) MWCACP Updates
Planting Mid-Season Harvesting
Important! Crop calendars are generally applicable to larger areas, such as countries or large agro-ecological zones within a country. Therefore, be
aware that there may be significant di erences in national crop calendars compared to a small area of interest. Where and if possible, regional
calendars have been used. As always, assess the data for fitness of use for your particular purpose.
Source: Sacks et al. [12]
Notes
[1] Disclaimer: While data producers have done their best to make the data used in this factsheet as accurate as possible, we
cannot make any guarantees as to the quality or fitness for use for your particular case. Please verify this for yourself, and use the
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8. references section to find out more about the quality and fitness for use of the datasets that have been used to produce the
Geofolio maps.
[2] We welcome feedback and suggestions. The Geofolio factsheets are under active development, so please let us know how this
factsheet can be improved to better suit your needs, or if there are things which are unclear or not working e ectively. You may
e-mail us directly at info@geofolio.org, or get in touch via @geofolio on Twitter.
References
[1] GADM Global Administrative Areas Dataset. More
information at http://gadm.org/.
[2] ESA Climate Change Initiative - Land Cover led by
UCLouvain (2017) More information and CCI data from ESA at
https://www.esa-landcover-cci.org/.
[3] Shuttle Radar Topography Mission (SRTM GL1 v3) Global
30m Dataset, accessed via OpenTopography. More information
about SRTM GL1 v3 available from USGS.
[4] Fick, S.E. and R.J. Hijmans (2017). Worldclim 2: New 1-km
spatial resolution climate surfaces for global land areas.
International Journal of Climatology. See
https://worldclim.org/version2 for more information.
[5] Soil parameter predictions are part of the LandGIS service
by OpenGeoHub. Data Copyright LandGIS contributors, and
available under CC BY-SA license. See
https://openlandmap.org for more information.
[6] Lehner, B., Grill G. (2013). Global river hydrography and
network routing: baseline data and new approaches to study
the world’s large river systems. Hydrological Processes, 27(15):
2171–2186. Data is available via https://hydrosheds.org.
[7] Ouellet Dallaire, C., Lehner, B., Sayre, R., Thieme, M. (2018).
A multidisciplinary framework to derive global river reach
classifications at high spatial resolution. Environmental
Research Letters. Data is available via
https://hydrosheds.org/page/gloric.
[8] Lehner, B., C. Reidy Liermann, C. Revenga, C. Vörösmarty, B.
Fekete, P. Crouzet, P. Döll, M. Endejan, K. Frenken, J. Magome,
C. Nilsson, J.C. Robertson, R. Rodel, N. Sindorf, and D. Wisser.
(2011) High-resolution mapping of the world’s reservoirs and
dams for sustainable river-flow management. Frontiers in
Ecology and the Environment 9 (9): 494-502. Data available via
http://globaldamwatch.org/grand/.
[9] Teluguntla, P., Thenkabail, P.S., Xiong, J., Gumma, M.K.,
Giri, C., Milesi, C., Ozdogan, M., Congalton, R., Tilton, J.,
Sankey, T.R., Massey, R., Phalke, A., and Yadav, K. 2014. Global
Cropland Area Database (GCAD) derived from Remote Sensing
in Support of Food Security in the Twenty-first Century:
Current Achievements and Future Possibilities. Chapter 7, Vol.
II. Land Resources: Monitoring, Modelling, and Mapping,
Remote Sensing Handbook edited by Prasad S. Thenkabail.
[10] More information and references for GFSAD30 project and
data products please refer to
https://croplands.org/gfsadce30info. The cropland mask on
Geofolio uses a merged version of all of the regional (Africa,
Europe, etc) products.
[11] Monfreda, C., Ramankutty, N. & Foley, J. (2008) Farming
the planet: 2. Geographic distribution of crop areas, yields,
physiological types, and net primary production in the year
2000. Global Biogeochemical Cycles, 22. More information and
data available via http://www.earthstat.org/.
[12] Sacks, W.J., D. Deryng, J.A. Foley, and N. Ramankutty
(2010). Crop planting dates: An analysis of global patterns.
Global Ecology and Biogeography, 19: 607-620.
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