Impact of the Accuracy of Land Cover Data Sets on the Accuracy of Land Cover Change Scenarios in the Mono River Basin, Togo, West Africa

The results reveal CILSS as the most accurate data set with a Kappa coefficient of 68% and an overall accuracy of 83%. CILSS data shows a decrease of savanna and forest whereas an increase of cropland over the period 1975 to 2013. The increase of cropland area of 30.97% from 1975 to 2013 can be related to the increase in population and their food demand, while the losses of forest area and the decrease of savanna are further amplified by using wood as energy sources and the lack of forest management. The three datasets were used to simulate future LULC changes using the Terrset Land Change Modeler. The validation of the model using CILSS data for 2013 showed a quality of 50.94%, it is only 40.04% for ESA and 20.13% for Globeland30. CILSS data was utilized to simulate the LULC distribution for the years 2020 and 2027 because of its satisfactory performances. The results show that a high spatial resolution is not a guarantee of high quality. The results of this study can be used for impact studies and to develop management strategies for mitigating negative effects of land use and land cover change.

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Impact of different and time-varying land cover data sets in a regional climate model on regional and local climate over Europe

10.5194/egusphere-egu2020-631 ◽

2020 ◽

Author(s):

Mingyue Zhang ◽

Jürgen Helmert ◽

Merja Tölle

Keyword(s):

Land Cover ◽

Regional Climate Model ◽

Climate Model ◽

Regional Climate ◽

Data Sets ◽

Time Varying ◽

Data Set ◽

Local Climate ◽

Land Cover Data ◽

Simulation Results

According to IPCC, Land use and Land Cover (LC) changes have a key role to adapt and mitigate future climate change aiming to stabilize temperature rise up to 2&#176;C. Land surface change at regional scale is associated to global climate change, such as global warming. It influences the earth&#8217;s water and energy cycles via influences on the heat, moisture and momentum transfer, and on the chemical composition of the atmosphere. These effects show variations due to different LC types, and due to their spatial and temporal resolutions. &#160;Thus, we incorporate a new time-varying land cover data set based on ESACCI into the regional climate model COSMO-CLM(v5.0). Further, the impact on the regional and local climate is compared to the standard operational LC data of GLC2000 and GlobCover 2009. Convection-permitting simulations with the three land cover data sets are performed at 0.0275&#176; horizontal resolution over Europe for the time period from 1992 to 2015.Overall, the simulation results show comparable agreement to observations. However, the simulation results based on GLC2000 and GlobCover 2009 (with 23 LC types) LC data sets show a fluctuation of 0.5K in temperature and 5% of precipitation. Even though the LC is classified into the same types, the difference in LC distribution and fraction leads to variations in climate simulation results. Using all of the 37 LC types of the ESACCI-LC data set show noticeable differences in distribution of temperature and precipitation compared to the simulations with GLC2000 and GlobCover 2009. Especially in forest areas, slight differences of the plant cover type (e.g. Evergreen or Deciduous) could result in up to 10% differences (increase or decrease) in temperature and precipitation over the simulation domain. Our results demonstrate how LC changes as well as different land cover type effect regional climate. There is need for proper and time-varying land cover data sets for regional climate model studies. The approach of including ESACCI-LC data set into regional climate model simulations also improved the external data generation system.We anticipate this research to be a starting point for involving time-varying LC data sets into regional climate models. Furthermore, it will give us a possibility to quantify the effect of time-varying LC data on regional climate accurately.Acknowledgement:1: Computational resources were made available by the German Climate Computing Center (DKRZ) through support from the Federal Ministry of Education and Research in Germany (BMBF). We acknowledge the funding of the German Research Foundation (DFG) through grant NR. 401857120.2: Appreciation for the support of J&#252;rg Luterbacher and Eva Nowatzki.&#160;

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Impact of the Accuracy of Land Cover Data sets on the Accuracy of Land Cover Change Scenarios in the Mono River Basin, Togo, West Africa

International Journal of Advanced Remote Sensing and GIS ◽

10.23953/cloud.ijarsg.422 ◽

2019 ◽

Vol 8 (1) ◽

pp. 3073-3095 ◽

Cited By ~ 1

Author(s):

Djan’na H. Koubodana ◽

◽

Bernd Diekkrüger ◽

Kristian Näschen ◽

Julien Adounkpe ◽

...

Keyword(s):

Land Cover ◽

West Africa ◽

Land Cover Change ◽

River Basin ◽

Data Sets ◽

Land Cover Data

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A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL

Remote Sensing ◽

10.3390/rs12182888 ◽

2020 ◽

Vol 12 (18) ◽

pp. 2888 ◽

Cited By ~ 1

Author(s):

Nishanta Khanal ◽

Mir Abdul Matin ◽

Kabir Uddin ◽

Ate Poortinga ◽

Farrukh Chishtie ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Time Series Data ◽

Input Image ◽

Series Data ◽

Data Set ◽

Temporal Smoothing ◽

Land Cover Data ◽

Cover Class

Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.

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Tropical Peatland Water Balance under Land Cover Change in Padang Island, Indonesia

10.5194/egusphere-egu21-7229 ◽

2021 ◽

Author(s):

Adibtya Asyhari ◽

Sofyan Kurnianto ◽

Yogi Suardiwerianto ◽

Rahila Junika Tanjungsari ◽

Muhammad Fikky Hidayat ◽

...

Keyword(s):

Land Cover ◽

Water Balance ◽

Land Cover Change ◽

Management Strategies ◽

Hydrological Regime ◽

Field Measurements ◽

Data Set ◽

Tropical Peatland ◽

The Past ◽

Model Setup

Changes in hydrological regime associated with land cover change may result in crucial implications to tropical peatland landscape since hydrology strongly controls peatland geomorphology, ecology, and biogeochemical cycle. Therefore, improved understanding of the land cover change impacts to the water balance is of significant importance in order to formulate responsible peatland management strategies. In this study, we investigated the water balance under historical land cover change within Padang Island, Indonesia, an ombrotropic tropical peatland landscape with heterogeneous land covers. For this purpose, we established a model setup using a coupled MIKE SHE and MIKE Hydro River. The model was calibrated and validated against comprehensive data set from field measurements. Land cover change impacts were evaluated by comparing the water balance under current and past condition. The past land cover distribution was derived from historical satellite imagery analysis covering the period of 25 years before the current condition. Meanwhile, the past topography data was generated following long-term subsidence monitoring data. Here, we will present the impacts of land cover change to water balance at the landscape level and their implications for management of tropical peatlands.

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An evaluation of multiple land-cover data sets to estimate cropland area in West Africa

International Journal of Remote Sensing ◽

10.1080/01431161.2016.1232869 ◽

2016 ◽

Vol 37 (22) ◽

pp. 5344-5364 ◽

Cited By ~ 2

Author(s):

Pradeep Adhikari ◽

Kirsten M. de Beurs

Keyword(s):

Land Cover ◽

West Africa ◽

Data Sets ◽

Cropland Area ◽

Land Cover Data

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A PRELIMINARY QUALITY ANALYSIS OF THE CLIMATE CHANGE INITIATIVE LAND COVER PRODUCTS FOR CONTINENTAL PORTUGAL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1213-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1213-1220

Author(s):

C. C. Fonte ◽

L. See ◽

M. Lesiv ◽

S. Fritz

Keyword(s):

Climate Change ◽

Land Cover ◽

Quality Analysis ◽

Data Sets ◽

Change Initiative ◽

Small Areas ◽

Land Cover Data ◽

Vegetation Class ◽

Land Cover Maps

Abstract. The aim of this paper is to perform a preliminary analysis of the compatibility and quality of the available time series of land cover data available for continental Portugal, in particular, Climate Change Initiative Land Cover maps, which are available annually from 1992 to 2015; CORINE Land Cover and the Urban Atlas for 2006 and 2012; and the Portuguese Carta de Ocupação do Solo for 2007 and 2010. Changes were first identified per product between the different data sets for consecutive dates and then a comparison was made between products. This was followed by validation of two study areas using the COS and UA as reference products. The results show that increases in urbanization are visible in all pairs of products but that the amount of change varies. Moreover, some changes are not in the same direction but may be attributable to classes with small areas and the coarser resolution of the CCI LC maps compared to the other products. The CCI LC maps also overestimate the forest/natural vegetation class by 11&ndash;13%, which is also the largest class in Portugal.

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Use of various remote sensing land cover products for plant functional type mapping over Siberia

Earth System Science Data ◽

10.5194/essd-5-331-2013 ◽

2013 ◽

Vol 5 (2) ◽

pp. 331-348 ◽

Cited By ~ 17

Author(s):

C. Ottlé ◽

J. Lescure ◽

F. Maignan ◽

B. Poulter ◽

T. Wang ◽

...

Keyword(s):

Land Cover ◽

Land Cover Classification ◽

Classification Systems ◽

Tree Cover ◽

Data Sets ◽

Earth System ◽

Data Set ◽

System Models ◽

Land Cover Data ◽

Earth System Models

Abstract. High-latitude ecosystems play an important role in the global carbon cycle and in regulating the climate system and are presently undergoing rapid environmental change. Accurate land cover data sets are required to both document these changes as well as to provide land-surface information for benchmarking and initializing Earth system models. Earth system models also require specific land cover classification systems based on plant functional types (PFTs), rather than species or ecosystems, and so post-processing of existing land cover data is often required. This study compares over Siberia, multiple land cover data sets against one another and with auxiliary data to identify key uncertainties that contribute to variability in PFT classifications that would introduce errors in Earth system modeling. Land cover classification systems from GLC 2000, GlobCover 2005 and 2009, and MODIS collections 5 and 5.1 are first aggregated to a common legend, and then compared to high-resolution land cover classification systems, vegetation continuous fields (MODIS VCFs) and satellite-derived tree heights (to discriminate against sparse, shrub, and forest vegetation). The GlobCover data set, with a lower threshold for tree cover and taller tree heights and a better spatial resolution, tends to have better distributions of tree cover compared to high-resolution data. It has therefore been chosen to build new PFT maps for the ORCHIDEE land surface model at 1 km scale. Compared to the original PFT data set, the new PFT maps based on GlobCover 2005 and an updated cross-walking approach mainly differ in the characterization of forests and degree of tree cover. The partition of grasslands and bare soils now appears more realistic compared with ground truth data. This new vegetation map provides a framework for further development of new PFTs in the ORCHIDEE model like shrubs, lichens and mosses, to represent the water and carbon cycles in northern latitudes better. Updated land cover data sets are critical for improving and maintaining the relevance of Earth system models for assessing climate and human impacts on biogeochemistry and biophysics. The new PFT map at 5 km scale is available for download from the PANGAEA website at 10.1594/PANGAEA.810709.

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Effects of Land Cover Changes on Net Primary Productivity in the Terrestrial Ecosystems of China from 2001 to 2012

Land ◽

10.3390/land9120480 ◽

2020 ◽

Vol 9 (12) ◽

pp. 480

Author(s):

Hanwei Li ◽

Juhua Ding ◽

Jiang Zhang ◽

Zhenan Yang ◽

Bin Yang ◽

...

Keyword(s):

Land Cover ◽

Land Cover Change ◽

Primary Productivity ◽

Forest Cover ◽

Net Primary Productivity ◽

Terrestrial Ecosystems ◽

Land Cover Types ◽

Cropland Area ◽

Land Cover Data ◽

Increasing Trends

The 2001–2012 MODIS MCD12Q1 land cover data and MOD17A3 NPP data were used to calculate changes in land cover in China and annual changes in net primary productivity (NPP) during a 12-year period and to quantitatively analyze the effects of land cover change on the NPP of China’s terrestrial ecosystems. The results revealed that during the study period, no changes in land cover type occurred in 7447.31 thousand km2 of China, while the area of vegetation cover increased by 160.97 thousand km2 in the rest of the country. Forest cover increased to 20.91%, which was mainly due to the conversion of large areas of savanna (345.19 thousand km2) and cropland (178.96 thousand km2) to forest. During the 12-year study period, the annual mean NPP of China was 2.70 PgC and increased by 0.25 PgC, from 2.50 to 2.75 PgC. Of this change, 0.21 PgC occurred in areas where there was no land cover change, while 0.04 PgC occurred in areas where there was land cover change. The contributions of forest and cropland to NPP exhibited increasing trends, while the contributions of shrubland and grassland to NPP decreased. Among these land cover types, the contributions of forest and cropland to the national NPP were the greatest, accounting for 40.97% and 27.95%, respectively, of the annual total NPP. There was no significant correlation between changes in forest area and changes in total annual NPP (R2 < 0.1), while the correlation coefficient for changes in cropland area and total annual NPP was 0.48. Additionally, the area of cropland converted to other land cover types was negatively correlated with the changes in NPP, and the loss of cropland caused a reduction in the national NPP.

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Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative

Geoscientific Model Development ◽

10.5194/gmd-8-2315-2015 ◽

2015 ◽

Vol 8 (7) ◽

pp. 2315-2328 ◽

Cited By ~ 94

Author(s):

B. Poulter ◽

N. MacBean ◽

A. Hartley ◽

I. Khlystova ◽

O. Arino ◽

...

Keyword(s):

Land Cover ◽

European Space Agency ◽

Data Sets ◽

Earth System ◽

Data Set ◽

System Models ◽

Global Land Cover ◽

Space Agency ◽

Land Cover Data ◽

Earth System Models

Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.

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