東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ null ]

切換: 標籤 | MARC模式 | ISBD

Land Cover Change and Its Implicatio...

Thapa, Saroj,

FindBook

Google Book

Amazon

博客來

Land Cover Change and Its Implications for Ecosystem Services in the Greater Shawnee National Forest /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Land Cover Change and Its Implications for Ecosystem Services in the Greater Shawnee National Forest // Saroj Thapa.
作者:	Thapa, Saroj,
面頁冊數:	1 electronic resource (202 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-04, Section: A.
Contained By:	Dissertations Abstracts International86-04A.
標題:	Environmental science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31332523
ISBN:	9798384465416

Land Cover Change and Its Implications for Ecosystem Services in the Greater Shawnee National Forest /
Thapa, Saroj,

Land Cover Change and Its Implications for Ecosystem Services in the Greater Shawnee National Forest /Saroj Thapa. - 1 electronic resource (202 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: A.

This dissertation employed a random forest algorithm for Land Use Land Cover (LULC) classification and proposed and tested a modified forest transition framework in the Greater Shawnee National Forest (GSNF), Illinois. Subsequently, a machine learning-based multilayer artificial neural network was used to assess the LULC of the GSNF between 2019 and 2050 utilizing IPCC-based projected climate data. The accuracy of LULC classification was evaluated using Kappa statistics and Producer and User accuracies. The Stepwise Regression, Support Vector Machine, Random Forest, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models were compared to quantify terrestrial carbon stock. Similarly, InVEST, FRAGSTAT, and Maxent models were used for habitat quality analysis and to estimate the probability of bobcat distribution. The terrestrial carbon stock, habitat quality, and bobcat distribution were quantified across three spatial resolutions, 30, 60, and 90 meters, to assess if there were substantial differences in the represented trends of these measures of Ecosystem Services (ES). The LULC analysis showed varying levels of temporal and spatial variabilities with increased deciduous forest (1.35%), mixed forest (26.40%), agricultural land (2.15%), and urbanized areas (6.70%) between 1990 and 2019. Notably, the LULC intensity analysis exhibited stability from 2001 to 2019, consistent with the forest transition framework proposed in the study. However, when integrating temperature and precipitation projections derived from the IPCC, notable changes in forest cover were observed from the western to eastern sectors within the central region of the GSNF. In all IPCC based scenarios, overall forest cover (deciduous, evergreen, and mixed) declined. The classification accuracy of the LULC assessment ranged from 92.9% to 95.9%, accompanied by kappa statistics ranging from 0.89 to 0.94. The prediction accuracy of LULC change was validated for the year 2019, ranging from 77.99% to 84.67%, with kappa statistics between 0.79 and 0.81, depending on the scenario, and predictions were extended to the year 2050.The terrestrial carbon stock in GSNF varied from 15 to 212 MgC per hectare across different models. The RF model performed best at 90 meters resolution with FIA-based data, with RMSE values of 17.45, 18.73, and 20.05, and R-squared values of 0.53, 0.48, and 0.43 for 2001, 2010, and 2019, respectively. The findings indicated that while the InVEST model provides a broad and generalized approach to quantifying carbon storage, the random forest (RF) model is essential for obtaining more accurate and precise estimations.LULC has gradually become more fragmented over time, leading to a decline in average habitat quality from 1990 (0.724±0.215) to 2019 (0.689±0.192). Regardless of increased forest density, the proportion of high-quality habitats (habitat quality score above 0.83) decreased by 5% during the study period. Interestingly, there was a notable increase in the probability score of bobcat distribution from 1990 (0.327±0.123) to 2019 (0.347±0.084). The study revealed a strong correlation between habitat quality and the probability of bobcat distributions, indicating a mutual influence between the two factors. This dissertation suggests that the LULC change of the GSNF follows the forest transition framework and has a significant implication on ecosystem services, such as carbon storage and habitat quality. These results are instrumental for sustainable land management to optimize terrestrial carbon stock and habitat quality, thereby mitigating the impacts of climate change.

English

ISBN: 9798384465416Subjects--Topical Terms:

677245
Environmental science.
Subjects--Index Terms:

Carbon stock

Land Cover Change and Its Implications for Ecosystem Services in the Greater Shawnee National Forest /
LDR:05207nmm a22004573i 4500 001 2431955
005 20250624125418.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798384465416
035 $a (MiAaPQD)AAI31332523
035 $a AAI31332523
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Thapa, Saroj, $e author. $3 3821001
245 1 0 $a Land Cover Change and Its Implications for Ecosystem Services in the Greater Shawnee National Forest / $c Saroj Thapa.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (202 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-04, Section: A.
500 $a Advisors: Gibson, David; Li, Ruopu Committee members: Holzmueller, Eric; Wang, Guangxing; Remo, Jonathan.
502 $b Ph.D. $c Southern Illinois University at Carbondale $d 2024.
520 $a This dissertation employed a random forest algorithm for Land Use Land Cover (LULC) classification and proposed and tested a modified forest transition framework in the Greater Shawnee National Forest (GSNF), Illinois. Subsequently, a machine learning-based multilayer artificial neural network was used to assess the LULC of the GSNF between 2019 and 2050 utilizing IPCC-based projected climate data. The accuracy of LULC classification was evaluated using Kappa statistics and Producer and User accuracies. The Stepwise Regression, Support Vector Machine, Random Forest, and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models were compared to quantify terrestrial carbon stock. Similarly, InVEST, FRAGSTAT, and Maxent models were used for habitat quality analysis and to estimate the probability of bobcat distribution. The terrestrial carbon stock, habitat quality, and bobcat distribution were quantified across three spatial resolutions, 30, 60, and 90 meters, to assess if there were substantial differences in the represented trends of these measures of Ecosystem Services (ES). The LULC analysis showed varying levels of temporal and spatial variabilities with increased deciduous forest (1.35%), mixed forest (26.40%), agricultural land (2.15%), and urbanized areas (6.70%) between 1990 and 2019. Notably, the LULC intensity analysis exhibited stability from 2001 to 2019, consistent with the forest transition framework proposed in the study. However, when integrating temperature and precipitation projections derived from the IPCC, notable changes in forest cover were observed from the western to eastern sectors within the central region of the GSNF. In all IPCC based scenarios, overall forest cover (deciduous, evergreen, and mixed) declined. The classification accuracy of the LULC assessment ranged from 92.9% to 95.9%, accompanied by kappa statistics ranging from 0.89 to 0.94. The prediction accuracy of LULC change was validated for the year 2019, ranging from 77.99% to 84.67%, with kappa statistics between 0.79 and 0.81, depending on the scenario, and predictions were extended to the year 2050.The terrestrial carbon stock in GSNF varied from 15 to 212 MgC per hectare across different models. The RF model performed best at 90 meters resolution with FIA-based data, with RMSE values of 17.45, 18.73, and 20.05, and R-squared values of 0.53, 0.48, and 0.43 for 2001, 2010, and 2019, respectively. The findings indicated that while the InVEST model provides a broad and generalized approach to quantifying carbon storage, the random forest (RF) model is essential for obtaining more accurate and precise estimations.LULC has gradually become more fragmented over time, leading to a decline in average habitat quality from 1990 (0.724±0.215) to 2019 (0.689±0.192). Regardless of increased forest density, the proportion of high-quality habitats (habitat quality score above 0.83) decreased by 5% during the study period. Interestingly, there was a notable increase in the probability score of bobcat distribution from 1990 (0.327±0.123) to 2019 (0.347±0.084). The study revealed a strong correlation between habitat quality and the probability of bobcat distributions, indicating a mutual influence between the two factors. This dissertation suggests that the LULC change of the GSNF follows the forest transition framework and has a significant implication on ecosystem services, such as carbon storage and habitat quality. These results are instrumental for sustainable land management to optimize terrestrial carbon stock and habitat quality, thereby mitigating the impacts of climate change.
546 $a English
590 $a School code: 0209
650 4 $a Environmental science. $3 677245
650 4 $a Land use planning. $3 2122760
650 4 $a Climate change. $2 bicssc $3 2079509
653 $a Carbon stock
653 $a Ecosystem services
653 $a Forest transition
653 $a Habitat quality
653 $a Land cover
653 $a Random forest
690 $a 0768
690 $a 0536
690 $a 0404
710 2 $a Southern Illinois University at Carbondale. $b Environmental Resources & Policy. $e degree granting institution. $3 3821002
720 1 $a Gibson, David $e degree supervisor.
720 1 $a Li, Ruopu $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-04A.
790 $a 0209
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31332523