Monitoring Carbon Stock and Land-Use Change in 5000-Year-Old Juniper Forest Stand of Ziarat, Balochistan, through a Synergistic Approach

The Juniper forest reserve of Ziarat is one of the biggest Juniperus forests in the world. This study assessed the land-use changes and carbon stock of Ziarat. Different types of carbon pools were quantified in terms of storage in the study area in tons/ha i.e., above ground, soil, shrubs and litter. The Juniper species of this forest is putatively called Juniperus excelsa Beiberstein. To estimate above-ground biomass, different allometric equations were applied. Average above ground carbon stock of the forest was estimated as 8.34 ton/ha, 7.79 ton/ha and 8.4 ton/ha using each equation. Average carbon stock in soil, shrubs and litter was calculated as 24.35 ton/ha, 0.05 ton/ha and 1.52 ton/ha, respectively. Based on our results, soil carbon stock in the Juniper forest of Ziarat came out to be higher than the living biomass. Furthermore, the spatio-temporal classified maps for Ziarat showed that forest area has significantly decreased, while agricultural and barren lands increased from 1988 to 2018. This was supported by the fact that estimated carbon stock also showed a decreasing pattern between the evaluation periods of 1988 to 2018. Furthermore, the trend for land use and carbon stock was estimated post 2018 using a linear prediction model. The results corroborate the assumption that under a business as usual scenario, it is highly likely that the Juniperus forest will severely decline.

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Assessing the Opportunity Cost of Carbon Stock Caused by Land-Use Changes in Taiwan

Land ◽

10.3390/land10111240 ◽

2021 ◽

Vol 10 (11) ◽

pp. 1240

Author(s):

Ming-Yun Chu ◽

Wan-Yu Liu

Keyword(s):

Climate Change ◽

Land Use ◽

Carbon Storage ◽

Carbon Emissions ◽

Opportunity Cost ◽

Carbon Stock ◽

Agricultural Land ◽

Land Use Changes ◽

Market Price ◽

The Government

As compared with conventional approaches for reducing carbon emissions, the strategies of reducing emissions from deforestations and forest degradation (REDD) can greatly reduce costs. Hence, the United Nations Framework Convention on Climate Change regards the REDD strategies as a crucial approach to mitigate climate change. To respond to climate change, Taiwan passed the Greenhouse Gas Reduction and Management Act to control the emissions of greenhouse gases. In 2021, the Taiwan government has announced that it will achieve the carbon neutrality target by 2050. Accordingly, starting with focusing on the carbon sink, the REDD strategies have been considered a recognized and feasible strategy in Taiwan. This study analyzed the net present value and carbon storage for various land-use types to estimate the carbon stock and opportunity cost of land-use changes. When the change of agricultural land to artificial forests generated carbon stock, the opportunity cost of carbon stock was negative. Contrarily, restoring artificial forests (which refer to a kind of forest that is formed through artificial planting, cultivation, and conservation) to agricultural land would generate carbon emissions, but create additional income. Since the opportunity cost of carbon storage needs to be lower than the carbon market price so that landlords have incentives to conduct REDD+, the outcomes of this study can provide a reference for the government to set an appropriate subsidy or price for carbon sinks. It is suggested that the government should offer sufficient incentives to reforest collapsed land, and implement interventions, promote carbon trading policies, or regulate the development of agricultural land so as to maintain artificial broadleaf forests for increased carbon storage.

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Spatio-Temporal Analysis of Land Cover/Land Use Changes Using Geoinformatics (A Case Study of Margallah Hills National Park)

Indian Journal of Science and Technology ◽

10.17485/ijst/2014/v7i11.23 ◽

2014 ◽

Vol 7 (11) ◽

pp. 1832-1841

Author(s):

Syeda Maria Zafar

Keyword(s):

Land Use ◽

Land Cover ◽

National Park ◽

Land Use Changes ◽

Temporal Analysis ◽

Spatio Temporal

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Study on Coastline Changes of Xiamen City Based on Remote Sensing Images

E3S Web of Conferences ◽

10.1051/e3sconf/201913605003 ◽

2019 ◽

Vol 136 ◽

pp. 05003

Author(s):

Yanfang Qin ◽

Lin Ye ◽

Siming Chen

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Use Changes ◽

Remote Sensing Data ◽

Length Change ◽

Vector Data ◽

Remote Sensing Images ◽

Change Rate ◽

Xiamen City ◽

Spatio Temporal

Based on the Landsat remote sensing data, this paper had monitored the coastline changes of Xiamen city in recent 20 years. By extracting the coastline vector data of 1999, 2005, 2011 and 2017 respectively, the spatio-temporal characteristics of coastline changes on coastline length, change rate and land change area were analyzed, and the main driving factors were analyzed combined with the land use changes in the coastal swing area. The results show that: the total length of Xiamen's coastline increased from 235.16 km to 264.98 km during 1999-2017, and the land area increased from 1558.84 km2 to 1594.29 km2. The most significant changes occurred in Xiang'an district and Huli district with the coastline length increased by 16.38% during 2011-2017 and 22.14% during 1999-2005 respectively, while the changes were not very conspicuous in other areas. According to the land use changes in the coastal areas, the coastline changes in Xiamen City were mainly related to the expansion of construction land and port constructions in Haicang district, Xiang'an district and Huli district, as well as the expansion of aquaculture in the Xiang'an district.

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A Spatio-Temporal Storage Method for Land-Use Change

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.3078 ◽

2014 ◽

Vol 687-691 ◽

pp. 3078-3082

Author(s):

Ning Pan ◽

Ke Wang ◽

Jing Jing Tan

Keyword(s):

Data Mining ◽

Decision Making ◽

Land Use ◽

Land Use Change ◽

Land Use Planning ◽

Historical Data ◽

Land Use Changes ◽

Storage Method ◽

Temporal Aspect ◽

Spatio Temporal

Frequent land-use changes might produce a large amount of historical data which are valuable for data mining and decision-making. Based on the traditional Whole-state-recording Mode, the Special-state-recording Mode was proposed, focusing on the temporal aspect. This mode could optimize the land use database and reduce redundant change record. It could also improve data rollback and historical backtracking functions. The mode was successfully applied to land use planning in Zhejiang Province.

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On the absurdity of rapid urbanization: Spatio-temporal analysis of land-use changes in Morogoro, Tanzania

Cities ◽

10.1016/j.cities.2020.102876 ◽

2020 ◽

Vol 107 ◽

pp. 102876

Author(s):

Neema Simon Sumari ◽

Patrick Brandful Cobbinah ◽

Fanan Ujoh ◽

Gang Xu

Keyword(s):

Land Use ◽

Land Use Changes ◽

Temporal Analysis ◽

Rapid Urbanization ◽

Spatio Temporal

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A Neural Network for Modeling Multicategorical Parcel Use Change

International Journal of Applied Geospatial Research ◽

10.4018/jagr.2011070102 ◽

2011 ◽

Vol 2 (3) ◽

pp. 20-31 ◽

Cited By ~ 1

Author(s):

Kang Shou Lu ◽

John Morgan ◽

Jeffery Allen

Keyword(s):

Neural Network ◽

Land Use ◽

Multinomial Logistic Regression ◽

Land Use Changes ◽

Slight Variation ◽

Ann Model ◽

Temporal Prediction ◽

Percentage Points ◽

Spatio Temporal ◽

Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was used for network training in order to select an optimal model that avoids over-fitting problem. When trained and applied to predict changes in parcel use in a coastal county from 1990 to 2008, the ANN model performed well as measured by high prediction accuracy (82.0-98.5%) and high Kappa coefficient (81.4-97.5%) with only slight variation across five different land use categories. ANN also outperformed the benchmark multinomial logistic regression by average 17.5 percentage points in categorical accuracy and by 9.2 percentage points in overall accuracy. The authors used the ANN model to predict future parcel use change from 2007 to 2030.

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