scholarly journals Forest Cover Assessment in Tuv province of Mongolia Using MODIS and Landsat-7 ETM+

2011 ◽  
pp. 51-60
Author(s):  
R Tsolmon ◽  
K Yanagida ◽  
M Erdenetuya ◽  
L Ochirhuyag
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Forest Cover ◽  
Agricultural Fields ◽  
Forest Cover Change ◽  
Vegetation Monitoring ◽  
Mixed Pixel ◽  
Modis Data ◽  
Landsat 7 ◽  
Academy Of Sciences

The study aimed at determining the relative proportions of forest cover and other components in a mixed pixel. For this purpose a linear mixing model was used for the derivation of a land cover classification map in two study areas of Tuv province, Mongolia. Main types of forest cover change are forests to burn scars and agricultural fields in the study areas. In this paper, two reflective channels 3 and 4 of LANDSAT ETM+ and reflective channels land 2 of MODIS data was used to map five and four land components respectively. Clouds proportion was derived using MODIS data. A synergy between high-resolution MODIS and Landsat ETM+ data may greatly enhance the operational success of satellite based vegetation monitoring, in providing multi-spectral data on parameters of the environment.DOI: http://dx.doi.org/10.5564/pmas.v0i4.40Proceedings of the Mongolian Academy of Sciences 2007 No 4 pp.50-59

scholarly journals Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 173
Author(s):  
Changjun Gu ◽  
Yili Zhang ◽  
Linshan Liu ◽  
Lanhui Li ◽  
Shicheng Li ◽  
...  
Keyword(s):  
Land Use ◽  
Ecosystem Services ◽  
Land Cover ◽  
Ecosystem Service ◽  
Forest Cover ◽  
Google Earth ◽  
Forest Cover Change ◽  
Sacred Landscape ◽  
Lulc Changes ◽  
Google Earth Engine

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

scholarly journals Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

2021 ◽  
Vol 13 (19) ◽  
pp. 3870
Author(s):  
Hilma S. Nghiyalwa ◽  
Marcel Urban ◽  
Jussi Baade ◽  
Izak P. J. Smit ◽  
Abel Ramoelo ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Parametric Methods ◽  
Mixed Pixel ◽  
Medium Resolution ◽  
Long Time ◽  
Pixel Analysis ◽  
Spatio Temporal ◽  
Resolution Data

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.

scholarly journals Response to Comment on "High-resolution global maps of 21st-century forest cover change"

Science ◽  
2014 ◽  
Vol 344 (6187) ◽  
pp. 981-981 ◽  
Author(s):  
M. Hansen ◽  
P. Potapov ◽  
B. Margono ◽  
S. Stehman ◽  
S. Turubanova ◽  
...  
Keyword(s):  
High Resolution ◽  
21St Century ◽  
Forest Cover ◽  
Forest Cover Change

scholarly journals Motif: an open-source R tool for pattern-based spatial analysis

2020 ◽  
Author(s):  
Jakub Nowosad
Keyword(s):  
Spatial Analysis ◽  
Land Cover ◽  
Open Source ◽  
Spatial Patterns ◽  
Forest Cover ◽  
R Package ◽  
Growth Monitoring ◽  
Forest Cover Change ◽  
Land Cover Data ◽  
Wide Range

*Context* Pattern-based spatial analysis provides methods to describe and quantitatively compare spatial patterns for categorical raster datasets. It allows for spatial search, change detection, and clustering of areas with similar patterns. *Objectives* We developed an R package **motif** as a set of open-source tools for pattern-based spatial analysis. *Methods* This package provides most of the functionality of existing software (except spatial segmentation), but also extends the existing ideas through support for multi-layer raster datasets. It accepts larger-than-RAM datasets and works across all of the major operating systems. *Results* In this study, we describe the software design of the tool, its capabilities, and present four case studies. They include calculation of spatial signatures based on land cover data for regular and irregular areas, search for regions with similar patterns of geomorphons, detection of changes in land cover patterns, and clustering of areas with similar spatial patterns of land cover and landforms. *Conclusions* The methods implemented in **motif** should be useful in a wide range of applications, including land management, sustainable development, environmental protection, forest cover change and urban growth monitoring, and agriculture expansion studies. The **motif** package homepage is https://nowosad.github.io/motif.

scholarly journals Retrieval of Soil Moisture by Integrating Sentinel-1A and MODIS Data over Agricultural Fields

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1726 ◽  
Author(s):  
Yizhi Han ◽  
Xiaojing Bai ◽  
Wei Shao ◽  
Jie Wang
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Vegetation Index ◽  
Coupled Model ◽  
Agricultural Drought ◽  
Agricultural Fields ◽  
Backscattering Coefficient ◽  
Modis Data ◽  
Vegetation Parameters ◽  
Scattering Contribution

Soil moisture is an essential variable in the land surface ecosystem, which plays an important role in agricultural drought monitoring, crop status monitoring, and crop yield prediction. High-resolution radar data can be combined with optical remote-sensing data to provide a new approach to estimate high-resolution soil moisture over vegetated areas. In this paper, the Sentinel-1A data and the Moderate Resolution Imaging Spectroradiometer (MODIS) data are combined to retrieve soil moisture over agricultural fields. The advanced integral equation model (AIEM) is utilized to calculate the scattering contribution of the bare soil surface. The water cloud model (WCM) is applied to model the backscattering coefficient of vegetated areas, which use two vegetation parameters to parameterize the scattering and attenuation properties of vegetation. Four different vegetation parameters extracted from MODIS products are combined to predict the scattering contribution of vegetation, including the leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI). The effective roughness parameters are chosen to parameterize the AIEM. The Sentinel-1A and MODIS data in 2017 are used to calibrate the coupled model, and the datasets in 2018 are used for soil moisture estimation. The calibration results indicate that the Sentinel-1A backscattering coefficient can be accurately predicted by the coupled model with the Pearson correlation coefficient (R) ranging from 0.58 to 0.81 and a root mean square error (RMSE) ranging from 0.996 to 1.401 dB. The modeled results show that the retrieved soil moisture can capture the seasonal dynamics of soil moisture with R ranging from 0.74 to 0.81. With the different vegetation parameter combinations used for parameterizing the scattering contribution of the canopy, the importance of suitable vegetation parameters for describing the scattering and attenuation properties of vegetation is confirmed. The LAI is recommended to characterize the scattering properties. There is no obvious clue for selecting vegetation descriptors to characterize the attenuation properties of vegetation. These promising results confirm the feasibility and validity of the coupled model for soil moisture retrieval from the Sentinel-1A and MODIS data.

Investigating the interactions between Forest Cover Change and Hydroclimatic patterns in the Bolivian Amazon basin over the last 30 years

2020 ◽  
Author(s):  
Sly Wongchuig Correa ◽  
Jhan Carlo Espinoza ◽  
Hans Segura ◽  
Thomas Condom ◽  
Clémentine Junquas
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Amazon Basin ◽  
Forest Cover ◽  
Hydrological Modeling ◽  
European Space Agency ◽  
Forest Cover Change ◽  
Wet Season ◽  
Climatic Index ◽  
Bolivian Amazon

<p>Large evidences support the strong impacts on rainfall amount and the increasing of dry-season length on the Amazonian forest. All of these effects are usually attributed to large scale atmospheric circulation and to land cover changes as part of anthropogenic effects. In this research we assess statistical and modeling approaches to investigate the interaction between changes in forest cover and hydroclimate processes on a regional and local scale.</p><p>Henceforth, the deforestation areas and climatic indexes for the southern Amazon basin (south of 14°S) were evaluated. The deforestation map was estimated for the 1992-2018 period, based on global land cover maps at 300 m of spatial resolution produced by the European Space Agency (ESA) Climate Change Initiative (CCI) by using several remote sensing datasets. The CHIRPS rainfall dataset (P) for the 1981-2018 period was used to estimate the dry day frequency (DDF, P<1mm) and the wet day frequency (WDF, P>10mm). In addition, the mean actual seasonal evapotranspiration (AET) was GLEAM and ET-Amazon evapotranspiration datasets for the 1980-2018 and 2003-2013 periods respectively. In order to determine the local and the regional climatic effect for each pixel of the climatic index (DDF, WDF and AET), the deforestation fraction was estimated considering different spatial radii of influence (20 to 50 km).</p><p>The first results indicate a particular pattern in the southern Bolivian Amazon where two groups of areas were identified, considering the common period of analysis (1992-2018). One of them shows a significant relationship between increasing trend of DDF and decreasing trend of WDF while deforestation fraction is high, what mainly occurs during the wet season. In addition, this region is clearly placed in areas with values of deforestation fraction above ~30%, a closest value to the usually estimated Amazon Tipping Point (~40%). Below this value, the second group is also located in regions with positive trends of DDF and negative trends of WDF. This region has probably a strongest link with the large-scale climate.</p><p>Considering these preliminary results, the statistical approaches developed in this research could give some insights about the interactions between forest change and the regional hydro climatology, which might improve the understanding of this interaction based on large-scale hydrological modeling.</p>

scholarly journals Comment on "High-resolution global maps of 21st-century forest cover change"

Science ◽  
2014 ◽  
Vol 344 (6187) ◽  
pp. 981-981 ◽  
Author(s):  
R. Tropek ◽  
O. Sedla ek ◽  
J. Beck ◽  
P. Keil ◽  
Z. Musilova ◽  
...  
Keyword(s):  
High Resolution ◽  
21St Century ◽  
Forest Cover ◽  
Forest Cover Change
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