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7 results for “Fractional Vegetation Cover”
30 m Normalized Difference Vegetation Index Maps of Pure Pixels over China for Estimation of Fractional Vegetation Cover (2014, 2018, 2022)
<p>Using multi-angle remote sensing data, we generated 30-m maps for the normalized difference vegetation index (NDVI) of fully-covered vegetation (<em>Vv</em>) and bare soils (<em>Vs</em>) across China in 2014, 2018 and 2022. These pixel-wise <em>Vv</em> and <em>Vs</em> maps can be integrated with the vegetation index (VI)-based model to facilitate the accurate and rapid estimation of fractional vegetation cover (FVC) across various spatial resolutions and large scales. The products were produced using a multi-angle algorithm (MultiVI), which effectively addressed the spatial variability inherent in <em>Vv</em> and <em>Vs</em> and enhanced the accuracy of FVC estimations in comparison to traditional statistical methods. The estimated FVC demonstrated a root mean square deviation (RMSD) of approximately 0.1 when evaluated against field-measured FVC across different experimental sites.</p>
Arctic vegetation cover fractions derived from Landsat time series (1984-2020) for the greater Mackenzie Delta Region (Western Canadian Arctic)
<p>Data to the publication by Nill et al. (2022) "<em>Arctic shrub expansion revealed by Landsat-derived multitemporal<br> vegetation cover fractions in the Western Canadian Arctic"</em></p> <p>The dataset features Landsat-derived fractional cover estimates of Arctic plant functional types (shrub, evergreen trees, herbaceous, lichen) and other land cover (barren, water) in the greater Mackenzie Delta Region, Canada.<br> We utilized regression-based unmixing based on synthetic training data in order to build multitemporal Kernel Ridge Regression (KRR) models for estimating fractional cover and validated our predictions based on independent very-high-resolution imagery (please be referred to publication for details).<br> <br> <strong>Dataset information</strong><br> The fraction cover predictions ("krr-avg") are provided separately for each epoch (1984-1990, 1991-1996, ..., 2015-2020) and class/cover type. The decadal change images ("dec-cng") between 1984 and 2020 are provided separately for each class/cover type. The naming convention of the files is as follows:</p> <p>XXXX-XXXX_YYY-YYY_int16-10e3_class-Z-Z</p> <ul> <li>XXXX-XXXX = epoch, e.g. 2015-2020</li> <li>YYY-YYY = dataset ("krr-avg" = fraction cover, "dec-cng" = decadal fraction cover change)</li> <li>Z-Z = class ID and associated class name (sh = shrub, cf = coniferous, hb = herbaceous, lc = lichen, wt = water, br = barren)</li> </ul> <p>The fraction cover values are % scaled by 10,000. For instance, a value of 1234 refers to 12.34%. Further image metadata:</p> <ul> <li><strong>Datatype:</strong> Signed 16-bit integer (Int16) </li> <li><strong>Data format: </strong>GeoTiff (.tif)</li> <li><strong>No data value:</strong> -9999</li> <li><strong>Projection:</strong> EPSG:3573 with custom central meridian; WKT string: 'PROJCS["WGS 84 / North Pole LAEA Canada",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Lambert_Azimuthal_Equal_Area"],PARAMETER["latitude_of_center",90],PARAMETER["longitude_of_center",-135],PARAMETER["false_easting",0],PARAMETER["false_northing",0],UNIT["metre",1],AXIS["Easting",EAST],AXIS["Northing",NORTH]]'</li> </ul> <p><strong>Publication</strong><br> Nill, L., Grünberg, I., Ullmann, T., Gessner, M., Boike, J. & Hostert, P. (2022): Arctic shrub expansion revealed by Landsat-derived multitemporal vegetation cover fractions in the Western Canadian Arctic. Remote Sensing of Environment, 2022, 281. https://doi.org/10.1016/j.rse.2022.113228</p> <p><strong>Further information</strong><br> For further information, please see the publication or contact Leon Nill (leon.nill@geo.hu-berlin.de).<br> A web-visualization of this dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/arctic-shrub/">here</a>.</p>
Vegetation cover fractions for the City of Berlin dervided from Landsat 5, Landsat 7 and Landsat 8 data between 1988 and 2018
<p>Fractional vegetation cover dataset used in the study "Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series".</p> <p>Landsat satellite imagery (Landsat-5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper) and Landsat-8 OLI (Operational Land Imager)) was acquired for seven years between 1988 and 2018. Imagery was pre-processed and a regression-based unmixing approach was performed in order to generate fraction maps of vegetated and non-vegetated surfaces. For complete method description please see:</p> <p>Wellmann, T., Schug, F., Haase, D., Pfulgmacher, D., van der Linden, S. (2020). Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series. <em>Landscape and Urban Planning</em></p>
Digital orthoimagery for fractional vegetation cover at site Alpha from Morgan et al Kuiseb River study
<p>Contains high-resolution digital orthoimagery (RGB and NIR) and derived products for site Alpha, from the following manuscript:</p> <p>Morgan et al. (in prep), Spatiotemporal analysis of vegetation cover change in a large ephemeral river: multi-sensor fusion of unmanned aerial vehicle (UAV) and Landsat imagery.</p>
Vegetation cover fraction in each town block across Japan
<p>Although the percent of green space is the most frequently used index for quantifying urban green, it is currently unavailable in most cities in Japan. Here we provide an open dataset of the index for each town block across the country using Sentinel-2 satellite images in Google Earth Engine. The dataset is calibrated and validated with airborne datasets obtained in nine cities in Tokyo.</p> <p> </p> <p><strong>日本全国の町丁目別の緑被率の公開</strong></p> <ul> <li>都市の緑を定量化する指標としては、緑被率が最もよく使われていますが、現在、日本のほとんどの都市では緑被率を利用することができません。ここでは、Sentinel-2の衛星画像をGoogle Earth Engineを用いて、日本全国の町丁目別の緑被率をオープンデータとして提供しています。このデータセットは、東京都内の9区の航空機データ(各区のWEBサイトで公開)を用いて校正・検証されています。</li> <li>ライセンスに関してデータは<a href="https://creativecommons.org/licenses/by/4.0/deed.ja">CC BY 4.0</a>で公開しています。</li> <li>校正・検証に関しての詳細は、<a href="https://doi.org/10.3130/aijt.28.521">日本建築学会技術報告集</a>に掲載しています。 </li> <li>「FRAC_VEG」が緑被率です。</li> <li>境界データは「平成27年国勢調査」を用いています。</li> </ul> <p> </p> <p><strong>データ概要</strong></p> <p>年:2020</p> <p>空間分解能:町丁目(平成27年国勢調査)</p> <p> </p> <p><strong>引用方法</strong></p> <p>・データの利用のみの場合</p> <p>Kiyono Tomoki, Fujiwara Kunihiko, & Tsurumi Ryuta. (2021). Vegetation cover fraction in each town block across Japan (1.0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5553516">https://doi.org/10.5281/zenodo.5553516</a></p> <p>・上記以外の場合(この取り組みそのものや,アルゴリズム等への言及)</p> <p>清野友規, 藤原邦彦, 鶴見隆太. Google Earth Engineを用いた町丁目別緑被率オープンデータ(全国版)の作成と評価, 日本建築学会技術報告集, 2022, 28 巻, 68 号, p. 521-526, https://doi.org/10.3130/aijt.28.521</p> <p> </p> <p><strong>問い合わせ</strong></p> <p>意見・要望・感想などお気軽にお問い合わせください。</p> <p>鶴見隆太(日建設計総合研究所) tsurumi.ryuta@nikken.jp</p> <p> </p> <p><strong>更新情報</strong></p> <p>v1.0.1 2021/10/7</p> <p>・エクセルでの文字化けを解消(文字コード:BOM付UTF-8)</p> <p>・町丁目のユニーク識別子であるKEY_CODE(11桁)を追加</p> <p>v0.1.0</p> <p>・初期バージョンをリリース</p> <p> </p> <p><strong>PI</strong></p> <p>Kiyono Tomoki</p>
Measured data of global fractional vegetation cover from 2013-2021 and algorithm code for calculating remote sensing products
<p>These data come from "A new computationally efficient algorithm to generate global fractional vegetation cover from Sentinel-2 imagery at 10m resolution", these include:</p> <p>1. Measured data of global fractional vegetation cover from 2013-2021 </p> <p>2. Algorithm code for calculating fractional vegetation cover, these codes are written by JavaScript in GEE (Google Earth Engine).</p>
High spatial resolution Fractional Vegetation Cover maps OAL-DE (Elbe river). Further details can be found in D4.5 of the OPERANDUM project.
<p>In order to reduce the risk posed by flooding, areas of woody vegetation have been removed along the riverbank of Elbe river in order to expedite the inflow and outflow of water from the main channel, and thus contribute to flattening the peak hydrographic response. Maintaining the effectiveness of this clearing requires that there is little or no regrowth of this woody vegetation. The NBS that have been implemented in OAL-Germany sees the use of various animals to graze these areas. NBS is devoted to prevent the re-growth of woody vegetation after an intervention which took place over a period from autumn 2014 to February 2015, when woody vegetation along the riverbank was cut back. Monitoring the effectiveness of the NBS is being performed by means of high spatial resolution remotely sensed data , i.e. Rapideye at 5 m spatial resolution. The preliminary analysis of this experiment consisted in the monitoring of the fractional vegetation cover over four of the seven NBS sites. </p> <p>The green fractional abundance (fc) was calculated by an algorithm based on scaling NDVI in-between the maximum and minimum NDVI values. A semi-empirical method based on the use of NDVI was used following Zeng et al, (2000), to calculate fc. </p> <p>The dataset contains layer stack of fractional abundance calculated for the images calculated by Rapideye images acquired on 18 April 2013, 15 April 2015, 17 March 2016, 9 April 2019. </p>
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