Impact of land use change dynamics on sustainability of groundwater resources using earth observation data

2019 ◽  
Vol 22 (6) ◽  
pp. 5185-5198 ◽  
Author(s):  
Pradipika Verma ◽  
Prafull Singh ◽  
S. K. Srivastava
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Groundwater Resources ◽  
Earth Observation ◽  
Observation Data ◽  
Change Dynamics ◽  
Earth Observation Data

Development of earth observation data cubes for monitoring land degradation processes in South Africa

2021 ◽  
Author(s):  
Insa Otte ◽  
Nosiseko Mashiyi ◽  
Pawel Kluter ◽  
Steven Hill ◽  
Andreas Hirner ◽  
...  
Keyword(s):  
South Africa ◽  
Land Use ◽  
Ecosystem Services ◽  
Land Use Change ◽  
South African ◽  
Land Degradation ◽  
Earth Observation ◽  
Observation Data ◽  
Data Cubes ◽  
Earth Observation Data

<p>Global biodiversity and ecosystem services are under high pressure of human impact. Although avoiding, reducing and reversing the impacts of human activities on ecosystems should be an urgent priority, the loss of biodiversity continues. One of the main drivers of biodiversity loss is land use change and land degradation. In South Africa land degradation has a long history and is of great concern. The SPACES II project SALDi (South African Land Degradation Monitor) aims for developing new, adaptive and sustainable tools for assessing land degradation by addressing the dynamics and functioning of multi-use landscapes with respect to land use change and ecosystem services. SPACES II is a German-South African “Science Partnerships for the Adaptation to Complex Earth System Processes”. Within SALDi ready-to-use earth observation (EO) data cubes are developed. EO data cubes are useful and effective tools using earth observations to deliver decision-ready products. By accessing, storing and processing of remote sensing products and time-series in data cubes, the efficient monitoring of land degradation can therefore be enabled. The SALDi data cubes from optical and radar satellite data include all necessary pre-processing steps and are generated to monitor vegetation dynamics of five years for six focus areas. Intra- and interannual variability in both, a high spatial and temporal resolution will be accounted to monitor land degradation. Therefore, spatial high resolution earth observation data from 2016 to 2021 from Sentinel-1 (C-Band radar) and Sentinel-2 (multispectral) will be integrated in the SALDi data cube for six research areas of 100 x 100 km. Additionally, a number of vegetation indices will be implemented to account for explicit land degradation and vegetation monitoring. Spatially explicit query tools will enable users of the system to focus on specific areas, like hydrological catchments or blocks of fields.</p>

Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India

2017 ◽  
Vol 33 (11) ◽  
pp. 1202-1222 ◽  
Author(s):  
Sudhir Kumar Singh ◽  
Prosper Basommi Laari ◽  
Sk. Mustak ◽  
Prashant K. Srivastava ◽  
Szilárd Szabó
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
River Basin ◽  
Earth Observation ◽  
Data Sets ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Earth Observation Data ◽  
Tons River Basin

scholarly journals EXPOSURE AND VULNERABILITY GEOSPATIAL ANALYSIS USING EARTH OBSERVATION DATA IN THE CITY OF LIEGE, BELGIUM

2017 ◽  
Vol IV-1/W1 ◽  
pp. 149-156
Author(s):  
N. Stephenne ◽  
B. Beaumont ◽  
E. Hallot ◽  
F. Lenartz ◽  
F. Lefebre ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Land Use ◽  
Air Pollution ◽  
Land Cover ◽  
Urban Heat Island ◽  
Earth Observation ◽  
Observation Data ◽  
Heat Island ◽  
Urban Heat ◽  
Earth Observation Data

Risk situation can be mitigated by prevention measures, early warning tools and adequate monitoring of past experiences where Earth Observation and geospatial analysis have an adding value. This paper discusses the potential use of Earth Observation data and especially Land Cover / Land Use map in addressing within the three aspects of the risk assessment: danger, exposure and vulnerability. Evidences of the harmful effects of air pollution or heat waves are widely admitted and should increase in the context of global warming. Moreover, urban areas are generally warmer than rural surroundings, the so-called urban heat island. Combined with in-situ measurements, this paper presents models of city or local climate (air pollution and urban heat island), with a resolution of less than one kilometer, developed by integrating several sources of information including Earth Observation data and in particular Land Cover / Land Use. This assessment of the danger is then be related to a map of exposure and vulnerable people. Using dasymetric method to disaggregate statistical information on Land Cover / Land Use data, the SmartPop project analyzes in parallel the map of danger with the maps of people exposure A special focus on some categories at risk such as the elderly has been proposed by Aubrecht and Ozceylan (2013). Perspectives of the project includes the integration of a new Land Cover / Land Use map in the danger, exposure and vulnerability models and proposition of several aspects of risk assessment with the stakeholders of Wallonia.

scholarly journals Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art

2019 ◽  
pp. 1-32 ◽  
Author(s):  
Prem Chandra Pandey ◽  
Nikos Koutsias ◽  
George P. Petropoulos ◽  
Prashant K. Srivastava ◽  
Eyal Ben Dor
Keyword(s):  
Land Use ◽  
Land Cover ◽  
State Of The Art ◽  
Earth Observation ◽  
The State ◽  
Data Sources ◽  
Observation Data ◽  
Land Use Land Cover ◽  
Earth Observation Data

scholarly journals Exploiting Earth Observation Data to Impute Groundwater Level Measurements with an Extreme Learning Machine

2020 ◽  
Vol 12 (12) ◽  
pp. 2044
Author(s):  
Steven Evans ◽  
Gustavious P. Williams ◽  
Norman L. Jones ◽  
Daniel P. Ames ◽  
E. James Nelson
Keyword(s):  
Soil Moisture ◽  
Missing Data ◽  
Extreme Learning Machine ◽  
Groundwater Resources ◽  
Earth Observation ◽  
Imputation Method ◽  
Observation Data ◽  
Monitoring Wells ◽  
Earth Observation Data ◽  
Learning Machine

Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture models, the Global Land Data Assimilation System (GLDAS) model and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) soil moisture model to impute the missing data. Our imputation method uses a machine learning technique called Extreme Learning Machine (ELM). Our implementation uses 11 input data-streams, all based on Earth observation data. We train and apply the model one well at a time. We selected ELM because it is a single hidden layer feedforward model that can be trained quickly on minimal data. We tested the ELM method using data from monitoring wells in the Cedar Valley and Beryl-Enterprise areas in southwest Utah, USA. We compute error estimates for the imputed data and show that ELM-computed estimates were more accurate than Kriging estimates. This ELM-based data imputation method can be used to impute missing data at wells. These complete time series can be used improve the accuracy of aquifer groundwater elevation maps in areas where in-situ well measurements are sparse, resulting in more accurate spatial estimates of the groundwater surface. The data we use are available globally from 1950 to the present, so this method can be used anywhere in the world.

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

GIS Business ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 12-14
Author(s):  
Eicher, A
Keyword(s):  
Earth Observation ◽  
Observation Data ◽  
Business World ◽  
The Earth ◽  
Earth Observation Data

Our goal is to establish the earth observation data in the business world Unser Ziel ist es, die Erdbeobachtungsdaten in der Geschäftswelt zu etablieren

Mapping slums and model population density using earth observation data and open source solutions

2019 ◽  
Author(s):  
Tais Grippa ◽  
Stefanos Georganos ◽  
Sabine Vanhuysse ◽  
Moritz Lennert ◽  
Nicholus Mboga ◽  
...  
Keyword(s):  
Population Density ◽  
Open Source ◽  
Earth Observation ◽  
Observation Data ◽  
Model Population ◽  
Earth Observation Data
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