Remote sensing dynamic monitoring of ecosystem service value of soil conservation with time series data

2014 ◽  
Author(s):  
Xiaohe Gu ◽  
Wei Guo ◽  
Yancang Wang
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Ecosystem Service ◽  
Soil Conservation ◽  
Time Series Data ◽  
Dynamic Monitoring ◽  
Series Data ◽  
Ecosystem Service Value ◽  
Service Value

scholarly journals Dynamic Monitoring of Forest Land in Fuling District Based on Multi-Source Time Series Remote Sensing Images

2019 ◽  
Vol 8 (1) ◽  
pp. 36 ◽  
Author(s):  
Bingxin Bai ◽  
Yumin Tan ◽  
Dong Guo ◽  
Bo Xu
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Time Series Data ◽  
Accuracy Assessment ◽  
Satellite Image ◽  
Dynamic Monitoring ◽  
Trend Detection ◽  
Series Data ◽  
Forest Land ◽  
Remote Sensing Images

Time series remote sensing images can be used to monitor the dynamic changes of forest lands. Due to consistent cloud cover and fog, a single sensor typically provides limited data for dynamic monitoring. This problem is solved by combining observations from multiple sensors to form a time series (a satellite image time series). In this paper, the pixel-based multi-source remote sensing image fusion (MulTiFuse) method is applied to combine the Landsat time series and Huanjing-1 A/B (HJ-1 A/B) data in the Fuling district of Chongqing, China. The fusion results are further corrected and improved with spatial features. Dynamic monitoring and analysis of the study area are subsequently performed on the improved time series data using the combination of Mann-Kendall trend detection method and Theil Sen Slope analysis. The monitoring results show that a majority of the forest land (60.08%) has experienced strong growth during the 1999–2013 period. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracies.

scholarly journals Spatial Pattern of Agricultural Productivity Trends in Malawi

2020 ◽  
Vol 12 (4) ◽  
pp. 1313
Author(s):  
Leah M. Mungai ◽  
Joseph P. Messina ◽  
Sieglinde Snapp
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Agricultural Productivity ◽  
Household Survey ◽  
Maize Yield ◽  
Series Data ◽  
Maize Crop

This study aims to assess spatial patterns of Malawian agricultural productivity trends to elucidate the influence of weather and edaphic properties on Moderate Resolution Imaging Spectroradiometer (MODIS)-Normalized Difference Vegetation Index (NDVI) seasonal time series data over a decade (2006–2017). Spatially-located positive trends in the time series that can’t otherwise be accounted for are considered as evidence of farmer management and agricultural intensification. A second set of data provides further insights, using spatial distribution of farmer reported maize yield, inorganic and organic inputs use, and farmer reported soil quality information from the Malawi Integrated Household Survey (IHS3) and (IHS4), implemented between 2010–2011 and 2016–2017, respectively. Overall, remote-sensing identified areas of intensifying agriculture as not fully explained by biophysical drivers. Further, productivity trends for maize crop across Malawi show a decreasing trend over a decade (2006–2017). This is consistent with survey data, as national farmer reported yields showed low yields across Malawi, where 61% (2010–11) and 69% (2016–17) reported yields as being less than 1000 Kilograms/Hectare. Yields were markedly low in the southern region of Malawi, similar to remote sensing observations. Our generalized models provide contextual information for stakeholders on sustainability of productivity and can assist in targeting resources in needed areas. More in-depth research would improve detection of drivers of agricultural variability.

scholarly journals Measuring the Semi-Century Ecosystem-Service Value Variation in Mekelle City Region, Northern Ethiopia

2021 ◽  
Vol 13 (18) ◽  
pp. 10015
Author(s):  
Shishay Kiros Weldegebriel ◽  
Kumelachew Yeshitela
Keyword(s):  
Land Use ◽  
Ecosystem Service ◽  
Assessment System ◽  
Series Data ◽  
Northern Ethiopia ◽  
City Region ◽  
Ecosystem Service Value ◽  
Ecosystem Degradation ◽  
Service Value ◽  
Mekelle City

The Mekelle city region is facing severe ecosystem degradation. The study area has experienced unprecedented land-use dynamics over the past 47 years, but the effect of these dynamics on ecosystem-service values remains unknown. Estimating the various ecosystem services from a city region perspective has not been attempted so far. The rationale of this study was to estimate the spatial–temporal ecosystem-service value variations. The methodology employed was land-use/land-cover (LULC) datasets of remotely sensed datasets of the years 1972, 1984, 2001, 2012, and 2019, and ecosystem service value coefficient, expert focus group discussion, and document review were used. The digital satellite images were processed, classified, and analyzed using Earth Resource Development Assessment System (ERDAS) Imagine. Computations of changes in the land-use categories were made using Arc GIS 10.5.1, Eviews for time series data analysis, and XLSTAT analytical tools were used. Over the whole study period from 1972 to 2019, a loss of USD 128.6 million was observed, which is a reduction of 501.9%. The study shows that due to land-use changes, the total ecosystem service value is decreasing annually, suggesting that much more severe ecosystem degradation is due to occur. The results are relevant to policy development and indicate that ecological restoration is the best option in the study area.

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