What open data tells us - Reconstructing 55 years of global land use/cover change

2020 ◽  
Author(s):  
Karina Winkler ◽  
Richard Fuchs ◽  
Martin Herold ◽  
Mark Rounsevell
Keyword(s):  
Land Use ◽  
Spatial Resolution ◽  
Land Surface ◽  
Open Data ◽  
Data Driven ◽  
System Modelling ◽  
Observation Data ◽  
Land Change ◽  
Global Land ◽  
Data Driven Approach

<p>People have increasingly been shaping the surface of our planet. Land use/cover change – the most visible human footprint on Earth – is one of the main contributors to greenhouse gas emissions and biodiversity loss and, hence, is a key topic for current sustainability debates and climate change mitigation. To understand these land surface dynamics and its impacts, accurate reconstructions of global land use/cover change are necessary. Although more and more observational data sets are publicly available (e.g. from remote sensing), current land change assessments are still incomplete and either lack temporal consistency, spatial explicitness or thematic detail. Here, we show a consistent reconstruction of global land use/cover change from 1960-2015, using an open data-driven approach that combines national land use statistics with earth observation data of multiple sources and scales. Our land change reconstruction model HILDA+ (Historic Land Dynamics Assessment) accounts for data-derived gross changes within six main land use/cover classes at 1 km spatial resolution: Urban areas, cropland, pastures and rangeland, forest, (semi-)natural grass- or shrubland, other land. As a result, we present yearly land use/cover maps at 1 km spatial resolution, magnitudes and hot spot areas of change. Globally, around 20 % of the land surface – almost three times the size of Brazil - has undergone change within the last 55 years. Further, gross change is about seven times as high as yearly net change extent for forest, cropland and pasture dynamics. We prove that land change studies accounting for net change only can lead to severe underestimations of change extent and frequency. With this purely data-driven approach, we address current research needs of the earth system modelling community by providing new layers of land use/cover change with unprecedented level of detail. Learning from the recent past, understanding how management and land cover dynamics interactively affect the climate is essential for implementing measures of mitigation and sustainable land use policies. In this context, a solid information base can support informed decision-making.</p>

Estimates of the Earth surface influence on the accuracy of numerical prediction of air temperature in Belarus using the WRF model

2021 ◽  
Vol 4 ◽  
pp. 50-68
Author(s):  
S.А. Lysenko ◽  
◽  
P.О. Zaiko ◽  
Keyword(s):  
Land Use ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Lead Time ◽  
Weather Prediction ◽  
Wrf Model ◽  
Surface Model ◽  
Numerical Weather ◽  
Global Land

The spatial structure of land use and biophysical characteristics of land surface (albedo, leaf index, and vegetation cover) are updated using the GLASS (Global Land Surface Satellite) and GLC2019 (Global Land Cover, 2019) modern satellite databases for mesoscale numerical weather prediction with the WRF model for the territory of Belarus. The series of WRF-based numerical experiments was performed to verify the influence of the updated characteristics on the forecast quality for some difficult to predict winter cases. The model was initialized by the GFS (Global Forecast System, NCEP) global numerical weather prediction model. It is shown that the use of high-resolution land use data in the WRF and the consideration of the new albedo and leaf index distribution over the territory of Belarus can reduce the root-mean-square error (RMSE) of short-range (to 48 hours) forecasts of surface air temperature by 16–33% as compared to the GFS. The RMSE of the temperature forecast for the weather stations in Belarus for a forecast lead time of 12, 24, 36, and 48 hours decreased on average by 0.40°С (19%), 0.35°С (10%), 0.68°С (23%), and 0.56°С (15%), respectively. The most significant decrease in RMSE of the numerical forecast of temperature (up to 2.1 °С) was obtained for the daytime (for a lead time of 12 and 36 hours), when positive feedbacks between albedo and temperature of the land surface are manifested most. Keywords: numerical weather prediction, WRF, digital land surface model, albedo, leaf area index, forecast model validation

scholarly journals Characterizing Land Use/Land Cover Using Multi-Sensor Time Series from the Perspective of Land Surface Phenology

2019 ◽  
Vol 11 (14) ◽  
pp. 1677 ◽  
Author(s):  
Lan H. Nguyen ◽  
Geoffrey M. Henebry
Keyword(s):  
Land Use ◽  
Time Series ◽  
Land Cover ◽  
Land Surface ◽  
Quadratic Model ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Land Surface Phenology ◽  
Land Cover Map ◽  
Phenological Metrics

Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVI2), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVI2 = f(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVI2 = f(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area.

scholarly journals Mapping Agricultural Landuse Patterns from Time Series of Landsat 8 Using Random Forest Based Hierarchial Approach

2019 ◽  
Vol 11 (5) ◽  
pp. 601 ◽  
Author(s):  
Sajid Pareeth ◽  
Poolad Karimi ◽  
Mojtaba Shafiei ◽  
Charlotte De Fraiture
Keyword(s):  
Land Use ◽  
Time Series ◽  
Random Forest ◽  
Spatial Resolution ◽  
Temporal Dynamics ◽  
Learning Algorithm ◽  
Open Data ◽  
Landsat 8 ◽  
Processing Scheme ◽  
Irrigated Area

Increase in irrigated area, driven by demand for more food production, in the semi-arid regions of Asia and Africa is putting pressure on the already strained available water resources. To cope and manage this situation, monitoring spatial and temporal dynamics of the irrigated area land use at basin level is needed to ensure proper allocation of water. Publicly available satellite data at high spatial resolution and advances in remote sensing techniques offer a viable opportunity. In this study, we developed a new approach using time series of Landsat 8 (L8) data and Random Forest (RF) machine learning algorithm by introducing a hierarchical post-processing scheme to extract key Land Use Land Cover (LULC) types. We implemented this approach for Mashhad basin in Iran to develop a LULC map at 15 m spatial resolution with nine classes for the crop year 2015/2016. In addition, five irrigated land use types were extracted for three crop years—2013/2014, 2014/2015, and 2015/2016—using the RF models. The total irrigated area was estimated at 1796.16 km2, 1581.7 km2 and 1578.26 km2 for the cropping years 2013/2014, 2014/2015 and 2015/2016, respectively. The overall accuracy of the final LULC map was 87.2% with a kappa coefficient of 0.85. The methodology was implemented using open data and open source libraries. The ability of the RF models to extract key LULC types at basin level shows the usability of such approaches for operational near real time monitoring.

A Data-Driven Approach to Exploring Future Land Use and Transport Scenarios: The Online What If? Tool

2020 ◽  
Vol 27 (2) ◽  
pp. 21-44 ◽  
Author(s):  
Christopher Pettit ◽  
Sharon Biermann ◽  
Claudia Pelizaro ◽  
Ashley Bakelmun
Keyword(s):  
Land Use ◽  
Data Driven ◽  
Data Driven Approach

scholarly journals Parametric Design of a Microwave Radiometer for Land Surface Temperature Retrieval from Moon-Based Earth Observation Platform

2020 ◽  
Vol 12 (24) ◽  
pp. 4110
Author(s):  
Linan Yuan ◽  
Jingjuan Liao
Keyword(s):  
Surface Temperature ◽  
Spatial Resolution ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Microwave Radiometer ◽  
Earth Observation ◽  
Design Parameters ◽  
Observation Data ◽  
The Moon ◽  
Antenna Aperture

Increasing attention is being paid to the monitoring of global change, and remote sensing is an important means for acquiring global observation data. Due to the limitations of the orbital altitude, technological level, observation platform stability and design life of artificial satellites, spaceborne Earth observation platforms cannot quickly obtain global data. The Moon-based Earth observation (MEO) platform has unique advantages, including a wide observation range, short revisit period, large viewing angle and spatial resolution; thus, it provides a new observation method for quickly obtaining global Earth observation data. At present, the MEO platform has not yet entered the actual development stage, and the relevant parameters of the microwave sensors have not been determined. In this work, to explore whether a microwave radiometer is suitable for the MEO platform, the land surface temperature (LST) distribution at different times is estimated and the design parameters of the Moon-based microwave radiometer (MBMR) are analyzed based on the LST retrieval. Results show that the antenna aperture size of a Moon-based microwave radiometer is suitable for 120 m, and the bands include 18.7, 23.8, 36.5 and 89.0 GHz, each with horizontal and vertical polarization. Moreover, the optimal value of other parameters, such as the half-power beam width, spatial resolution, integration time of the radiometer system, temperature sensitivity, scan angle and antenna pattern simulations are also determined.

The Comparison between Empirical and Data-driven Computation in Predicting CO and NOx Emissions from Gas-Turbine-Based Power Plant

2021 ◽  
Author(s):  
T. Y. Wicaksono
Keyword(s):  
Gas Turbine ◽  
Monitoring System ◽  
Power Plants ◽  
Combustion Process ◽  
Open Data ◽  
Living Organism ◽  
Empirical Method ◽  
Data Driven ◽  
Gas Emissions ◽  
Data Driven Approach

The demand for the energy has been significantly increased over years led by the growth of global population. By the signing of the Paris Agreement in 2015, countries pledged to reduce the greenhouse gas effect including gas emissions to prevent and mitigate the global warming. The emissions control from power generation has then become a serious concern for countries to achieve their target in reducing gas emissions. Besides, the emitted gas such as Nitrogen oxides (NOx) or Carbon Monoxide (CO) that are resulted from the combustion process of fossil fuels in power plants is harmful pollutants to the living organism. The presence of those gas emissions can be predicted using Predictive Emissions Monitoring System (PEMS) or Continues Emissions Monitoring System (CEMS) methods. Continuous Emissions Monitoring System is a system that was designed to monitor the effluent gas streams resulted from the combustion processes. However, this empirical method still has several constraints in predicting the gas emissions where in some cases, it produces significant errors that caused by some uncontrollable aspects such as ambient temperature, pressure and humidity that can lead to miscalculation of operational risks and costs. Solving this problem, we conduct a PEMS with data-driven approach. In this study, we used the 2011-2015 open data from gas-turbine-based power plants in Turkey to train and test several supervised methods as a practical application to predict gas concentration. Predictive Emissions Monitoring System (PEMS) offers more advantages than Continuous Emissions Monitoring System (CEMS) especially in economic aspects. The system will monitor and predict the actual emissions from gas-turbine-based power plants operation. The results of this study indicate that the data-driven approach produces a good RMSE value. By having the gas emissions predicted, a mitigation plan can be set and the operational costs in the following years can be optimized by the company

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