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Novel Uses of Active and Passive Rem...

Aslan.

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Novel Uses of Active and Passive Remotely Sensed Data for Monitoring Spatiotemporal Dynamics of Mangroves.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Novel Uses of Active and Passive Remotely Sensed Data for Monitoring Spatiotemporal Dynamics of Mangroves./
作者:	Aslan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	176 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
Contained By:	Dissertation Abstracts International78-09B(E).
標題:	Environmental science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10276162
ISBN:	9781369756814

Novel Uses of Active and Passive Remotely Sensed Data for Monitoring Spatiotemporal Dynamics of Mangroves.
Aslan.

Novel Uses of Active and Passive Remotely Sensed Data for Monitoring Spatiotemporal Dynamics of Mangroves. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 176 p.

Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.

Thesis (Ph.D.)--Indiana University, 2017.

Mangrove forests provide numerous goods and services to coastal ecosystems, and play a major role in supporting coastal livelihoods. These forests are currently under serious threat, especially in developing countries, primarily due to anthropogenic disturbance. Although remote sensing has been proven to be a fast, cost-effective, and efficient tool for mapping and monitoring the spatial distribution of mangroves, problems still remain in establishing an accurate and robust method for inventorying and monitoring changes in mangrove forests globally. This research aims at overcoming some of these existing problems by developing new methods for combining active and passive remote sensing for monitoring changes in mangrove forests of a wide global region in high spatiotemporal resolutions.

ISBN: 9781369756814Subjects--Topical Terms:

677245
Environmental science.

Novel Uses of Active and Passive Remotely Sensed Data for Monitoring Spatiotemporal Dynamics of Mangroves.
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245 1 0 $a Novel Uses of Active and Passive Remotely Sensed Data for Monitoring Spatiotemporal Dynamics of Mangroves.
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300 $a 176 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-09(E), Section: B.
500 $a Adviser: Scott M. Robeson.
502 $a Thesis (Ph.D.)--Indiana University, 2017.
520 $a Mangrove forests provide numerous goods and services to coastal ecosystems, and play a major role in supporting coastal livelihoods. These forests are currently under serious threat, especially in developing countries, primarily due to anthropogenic disturbance. Although remote sensing has been proven to be a fast, cost-effective, and efficient tool for mapping and monitoring the spatial distribution of mangroves, problems still remain in establishing an accurate and robust method for inventorying and monitoring changes in mangrove forests globally. This research aims at overcoming some of these existing problems by developing new methods for combining active and passive remote sensing for monitoring changes in mangrove forests of a wide global region in high spatiotemporal resolutions.
520 $a Three sub-topics of this research are interconnected by taking advantage of diverse characteristics of active and passive remotely sensed data in detecting mangrove physical properties. The first study develops a suit of models to map spatial distribution, canopy height, and biomass of mangroves, up to the genus level, in Mimika district of Papua, Indonesia. These models show high overall accuracy and kappa coefficient (94.38% and 0.94, respectively) for land cover classification, with relatively small mean absolute error (MAE) for mangrove canopy height estimation (3.00 m) at 30 m pixels of the Shuttle Radar Topographic Mission (SRTM) data. Also, the study results in a low range of difference between modeled and measured standing biomass (3.48%) when compared at landscape scale level. In the second study, time series analysis of radar data is used to demonstrate that of the 96,300 ha or 95.7% of original mangrove forests in Mahakam Delta of Kalimantan, Indonesia, almost 62% had been deforested mainly to establish shrimp/fish ponds. The pond production rates varied over time, most probably due to the market demand and other socioeconomic reasons, but the average rate of deforestation was 4.48% y-1. This study also quantifies, for the first time, that an average lifespan of ponds in the delta is around 10 to 13 years.
520 $a Since most satellite-based studies of canopy height estimation utilized SRTM data collected in 2000, which does not account for habitat change over the past 16 years, the third study evaluates the reliability of extracting canopy heights derived from the PRISM World 3D-30 m (AW3D30) digital surface model (DSM) from ALOS satellite produced between 2006 to 2011 in three sites which vary in latitudinal position and mangrove degradation levels, ranging from relatively pristine (Mimika, Indonesia and Sundarbans, Bangladesh) to severely degraded mangrove ecosystems (Mahakam Delta, Indonesia). The objective of this study was to examine whether the AW3D30 DSM can be used to differentiate between undisturbed and disturbed forests by analyzing the distribution of different succession stages of mangroves. We employed our previously developed canopy height model for assessing mangrove canopy height. The study demonstrated that the viability of AW3D30 DSM data for estimating mangrove canopy height showed by a good agreement between modeled and measured canopy height from these two sites, as illustrated by a low value of MAE: 3.38 m (Mimika site), 2.54 m (Sundarbans site), and 2.89 m (Mahakam Delta site), respectively. These results also indicate that AW3D30 DSM data can differentiate the variability of canopy height estimation between relative intact mangroves and severely degraded mangroves.
520 $a Results obtained from these three studies highlight that the methodologies developed in this study potentially can be used as an operational procedure for assessing mangroves changes globally driven by mostly anthropogenic causes.
590 $a School code: 0093.
650 4 $a Environmental science. $3 677245
650 4 $a Remote sensing. $3 535394
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