scholarly journals Impact of Topography and Tidal Height on ALOS PALSAR Polarimetric Measurements to Estimate Aboveground Biomass of Mangrove Forest in Indonesia

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
S. Darmawan ◽  
W. Takeuchi ◽  
Y. Vetrita ◽  
K. Wikantika ◽  
D. K. Sari
Keyword(s):  
Aboveground Biomass ◽  
Mangrove Forest ◽  
High Tide ◽  
Region Of Interest ◽  
Tidal Height ◽  
Alos Palsar ◽  
The Impact ◽  
Topography Data ◽  
Height Data

This study is focused on investigating the impact of topography and tidal height on ALOS PALSAR polarimetric measurements on HH and HV for estimating aboveground biomass (AGB) of mangrove forest in Indonesia. We used multitemporal ALOS PALSAR polarimetric measurement that covered mangrove zone in Banyuasin, Cilacap, and Teluk Bintuni and also collected tidal height data within the same acquisition date with multitemporal ALOS PALSAR polarimetric measurement. We analyzed the distribution of flooding and nonflooding areas based on tidal height and SRTM topography data, created three profiles as region of interest (ROI), and got characteristics of backscatter value on HH and HV with different tidal height. The result of this study showed backscatter of the open mangrove zones during high tide with HH value less than −20 dB and HV value less than −25 dB whereas during low tide it showed an HH value around −20 to −10 dB and HV value around −25 to −10 dB. Backscatter of the middle mangrove zones at Cilacap, with low and flat topography, showed a deviation of backscatter on HV value of 1.6 dB. Finally, the average AGB of mangrove forest in Indonesia was estimated based on ALOS PALSAR polarimetric measurements.

scholarly journals Impact of tidal height on characteristics of ALOS PALSAR measurements to estimate above ground biomass of mangrove forest in Indonesia

2018 ◽  
Author(s):  
Ketut Wikantika
Keyword(s):  
Mangrove Forest ◽  
Impact Analysis ◽  
Time Series Data ◽  
Tidal Height ◽  
Series Data ◽  
Above Ground Biomass ◽  
Large Area ◽  
Alos Palsar ◽  
Ground Biomass ◽  
The Impact

Mangrove has the most carbon rich forests in the tropics. Mapping and monitoring biomass of mangrove forest is very important to manage ecosystem and field survey of mangrove biomass and productivity is very difficult due to muddy soil condition, heavy weight of the wood, very large area and tidal effect on mangrove area. Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) is available for identification and monitoring mangrove forest. The objective of this research is to investigate the impact of tidal height on characteristics of HH and HV derived from ALOS PALSAR for estimation above ground biomass of mangrove forest. Methodology consists of collecting of tidal height data in the study area, ALOS-PALSAR time series data, region of interest (ROI) on mangrove forest, characterization of HH and HV and impact analysis of tidal height on HH and HV. The result of this research has showed the impact of tidal height on characteristics HH and HV on mangrove forest types derived from ALOS-PALSAR and proposed the model for estimation aboveground biomass of mangrove forest.

scholarly journals Evaluating Forest Cover and Fragmentation in Costa Rica with a Corrected Global Tree Cover Map

2020 ◽  
Vol 12 (19) ◽  
pp. 3226
Author(s):  
Daniel Cunningham ◽  
Paul Cunningham ◽  
Matthew E. Fagan
Keyword(s):  
Costa Rica ◽  
Forest Cover ◽  
Agricultural Land ◽  
Forest Area ◽  
Tree Cover ◽  
Agricultural Crop ◽  
Forest Change ◽  
Alos Palsar ◽  
Tropical Regions ◽  
The Impact

Global tree cover products face challenges in accurately predicting tree cover across biophysical gradients, such as precipitation or agricultural cover. To generate a natural forest cover map for Costa Rica, biases in tree cover estimation in the most widely used tree cover product (the Global Forest Change product (GFC) were quantified and corrected, and the impact of map biases on estimates of forest cover and fragmentation was examined. First, a forest reference dataset was developed to examine how the difference between reference and GFC-predicted tree cover estimates varied along gradients of precipitation and elevation, and nonlinear statistical models were fit to predict the bias. Next, an agricultural land cover map was generated by classifying Landsat and ALOS PalSAR imagery (overall accuracy of 97%) to allow removing six common agricultural crops from estimates of tree cover. Finally, the GFC product was corrected through an integrated process using the nonlinear predictions of precipitation and elevation biases and the agricultural crop map as inputs. The accuracy of tree cover prediction increased by ≈29% over the original global forest change product (the R2 rose from 0.416 to 0.538). Using an optimized 89% tree cover threshold to create a forest/nonforest map, we found that fragmentation declined and core forest area and connectivity increased in the corrected forest cover map, especially in dry tropical forests, protected areas, and designated habitat corridors. By contrast, the core forest area decreased locally where agricultural fields were removed from estimates of natural tree cover. This research demonstrates a simple, transferable methodology to correct for observed biases in the Global Forest Change product. The use of uncorrected tree cover products may markedly over- or underestimate forest cover and fragmentation, especially in tropical regions with low precipitation, significant topography, and/or perennial agricultural production.

The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

2021 ◽  
Author(s):  
Isolde Glissenaar ◽  
Jack Landy ◽  
Alek Petty ◽  
Nathan Kurtz ◽  
Julienne Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Region Of Interest ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
The Impact

<p>The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.</p><p>We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.</p><p>The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.</p>

scholarly journals Influence of Atmospheric Rivers on Mountain Snowpack in the Western United States

2018 ◽  
Vol 31 (24) ◽  
pp. 9921-9940 ◽  
Author(s):  
N. Goldenson ◽  
L. R. Leung ◽  
C. M. Bitz ◽  
E. Blanchard-Wrigglesworth
Keyword(s):  
United States ◽  
Sierra Nevada ◽  
Snow Water Equivalent ◽  
Region Of Interest ◽  
Internal Variability ◽  
Western United States ◽  
Historical Period ◽  
Atmospheric Rivers ◽  
Atmospheric River ◽  
The Impact

In the coastal mountains of western North America, most extreme precipitation is associated with atmospheric rivers (ARs), narrow bands of moisture originating in the tropics. Here we quantify how interannual variability in atmospheric rivers influences snowpack in the western United States in observations and a model. We simulate the historical climate with the Model for Prediction Across Scales (MPAS) with physics from the Community Atmosphere Model, version 5 [CAM5 (MPAS-CAM5)], using prescribed sea surface temperatures. In the global variable-resolution domain, regional refinement (at ~30 km) is applied to our region of interest and upwind over the northeast Pacific. To better characterize internal variability, we conduct simulations with three ensemble members over 30 years of the historical period. In the Cascade Range, with some exceptions, winters with more atmospheric river days are associated with less snowpack. In California’s Sierra Nevada, winters with more ARs are associated with greater snowpack. The slope of the linear regression of observed snow water equivalent (SWE) on reanalysis-based AR count has the same sign as that arrived at using the model, but is statistically significant in observations only for California. In spring, internal variance plays an important role in determining whether atmospheric river days appear to be associated with greater or less snowpack. The cumulative (winter through spring) number of atmospheric river days, on the other hand, has a relationship with spring snowpack, which is consistent across ensemble members. Thus, the impact of atmospheric rivers on winter snowpack has a greater influence on spring snowpack than spring atmospheric rivers in the model for both regions and in California consistently in observations.

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