Comparing three transition potential modeling for identifying suitable sites for REDD+ projects

Parsamehr, Koosha; Gholamalifard, Mehdi; Kooch, Yahya

doi:10.1007/s41324-019-00273-1

Cited by 11 publications

(19 citation statements)

References 38 publications

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“…It can easily recognize the intricate patterns from the database and model the nonlinear relationships [44]. Although many neural networks have been developed but MLPNN is largely utilized in the various applications [45,46].…”

Section: Lulc Simulation and Predictionmentioning

confidence: 99%

“…The capability of MLPNN to model numerous or even entire transitions at one time makes MLPNN-MCM better than other models to predict the future pattern of LULC [27]. It also automatically produces several parameter values that take a lesser amount of data for training and reduce the calibration time of the model [46]. MLPNN also combines the different parameters that affects the various LULC transitions [26,27].…”

Section: Lulc Simulation and Predictionmentioning

confidence: 99%

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Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model

2021

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In this study, prediction of the future land use land cover (LULC) changes over Mumbai and its surrounding region, India, was conducted to have reference information in urban development. To obtain the historical dynamics of the LULC, a supervised classification algorithm was applied to the Landsat images of 1992, 2002, and 2011. Based on spatial drivers and LULC of 1992 and 2002, the multiple perceptron neural network (MLPNN)-based Markov chain model (MCM) was applied to simulate the LULC in 2011, which was further validated using kappa statistics. Thereafter, by using 2002 and 2011 LULC, MLPNN-MCM was applied to predict the LULC in 2050. This study predicted the prompt urban growth over the suburban regions of Mumbai, which shows, by 2050, the Urban class will occupy 46.87% (1328.77 km2) of the entire study area. As compared to the LULC in 2011, the Urban and Forest areas in 2050 will increase by 14.31% and 2.05%, respectively, while the area under the Agriculture/Sparsely Vegetated and Barren land will decline by 16.87%. The class of water and the coastal feature will experience minute fluctuations (<1%) in the future. The predicted LULC for 2050 can be used as a thematic map in various climatic, environmental, and urban planning models to achieve the aims of sustainable development over the region.

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Section: Lulc Simulation and Predictionmentioning

confidence: 99%

Section: Lulc Simulation and Predictionmentioning

confidence: 99%

Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model

2021

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“…In fact, achieving the goals and effective and successful results of REDD+ projects in Iran requires effective policy‐making, accurate and transparent project design, and step‐by‐step monitoring during project implementation. Factors such as sovereignty, capacity, and rights to occupy land, justice, transparency, indigenous people's rights and knowledge, and increasing local and institutional capacities have been some of the most important reasons for the failure of these projects in Iran (Parsamehr et al, 2019; Shooshtari & Gholamalifard, 2015). In addition, initiatives like REDD+ may improve the standard of living for people who depend on forests by reducing poverty, increasing income through payments made in exchange for carbon credits, and providing extra advantages like better land tenure or carbon ownership (Bayrak & Marafa, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…REDD+ projects are mainly handed over to the public and semi‐private sectors. Therefore, some of these projects are research‐based projects supported by research institutions, and some others are governmental‐based and no private sector is involved in the implementation of these projects (Parsamehr et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Impact of Land Cover Changes on Reducing Greenhouse Emissions: Site Selection, Baseline Modeling, and Strategic Environmental Assessment of REDD+ Projects

et al. 2023

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Reducing emissions from deforestation and forest degradation (REDD+) is way key to reduce the emission of greenhouse gases (GHGs) while also protecting vulnerable forest ecosystems. The purpose of this study was to recognize suitable areas for REDD+ Programme projects and calculate the reduction in CO2 emissions through the prevention of forest cover degradation in the Central Hyrcanian forests. For this purpose, the cover changes of the Central Hyrcanian forests were assessed using LANDSAT satellite images. Applying the voluntary carbon standard (VCS) methodology and the calibration period 1984–2014 (30 years), forest cover changes were predicted. The results showed that under the business‐as‐usual scenario, 155,698 ha of Central Hyrcanian forests will be declined by 2044. In general, the REDD+ Programme project implementation will prevent the release of 1,209,231 tCO2e. Based on the social cost of carbon (SCC) approach, the REDD+ Programme project implementation can save 12,092,310 US$. In addition, this approach can be used for the project design document (PDD) of the forest development mechanism.

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“…Because the emissions of GHGs are known to be strongly influenced by LULC change (Cooper et al, 2020;Hundera et al, 2020), scenario analysis with LULC models can play a major role in providing information to decision makers. By purpose of spatial targeting of REDD studies, LULC change models such as Land Change Modeler (LCM), Geomod, CA-MARCOV and CLUE-S have been used for the LULC changes prediction, specially forest loss trend (Feng et al, 2020;Tang et al, 2020;Parsamehr et al, 2019;Mena et al, 2017;Bununu et al, 2016;Kim, 2010;Hewson et al, 2019;Redowan, 2019). Some models such as LCM that permits the simulation of future scenario is integrated with a REDD steps to determine and model anthropogenic GHGs emission reductions (Bununu et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Targeting area and comparing the effect of different land use/land cover (LULC) scenarios on greenhouse gases (GHGs) emission reduction (Case study: Hyrcanian forests in Iran)

Kamyab¹,

Asadolahi²

2020

Preprint

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Background Because the greenhouse gases (GHGs) emissions are known to be strongly influenced by land use/land cover (LULC) change, reducing emissions from deforestation and degradation (REDD) mechanism has attracted much attention as a strategy for understanding how different LULC scenarios effect on the GHGs emissions. Transition to other LULC types is one of the major challenges of Iran's Hyrcanian forests in Golestan province. To consider how LULC change scenarios affect GHGs, REDD project was executed in a period of 30 years (2018–2048) at intervals of 5 years. In this regard, study area was divided into the project area and leakage belt based on the Multi Criteria Evaluation (MCE) derived forest suitability map. In the baseline scenario, it was assumed that the trend of past LULC changes will continue. Results By implementation of the project scenario, some degradation activities were controlled. Project scenario was executed with different project success rates (PSR) of 90, 80, 70, 60 and 50% to examine its efficiency rate in reducing GHGs emissions. According to the results, 38206.8 hectares of forests within the project area will be destroyed by 2047 under the baseline. The destroyed area will be reach 39784.4 hectares in the leakage belt. The highest rate of forest destruction in the project area will occur in the last 5 years (1352 hectares per year), so the highest CO2 and non-CO2 emissions equal to 662655.3 tons/year and 278.94 tCO2e/year will happen in the last 5 years (2042–2047). Based on the results, reducing the PSR affected the efficiency of the project scenario. The highest and lowest rates of emissions reduction were observed respectively with PSR of 90 and 50%. Conclusions That's very important for developing countries especially Iran that are facing many challenging forest conservation decisions. This study innovated in methodology by integrating the MCE into the REDD steps. The MCE as a spatial targeting method could be applied to increase the efficiency of the REDD project, as we illustrated for the case of Hyrcanian forests.

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Comparing three transition potential modeling for identifying suitable sites for REDD+ projects

Cited by 11 publications

References 38 publications

Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model

Prediction of Land Use and Land Cover Changes in Mumbai City, India, Using Remote Sensing Data and a Multilayer Perceptron Neural Network-Based Markov Chain Model

Impact of Land Cover Changes on Reducing Greenhouse Emissions: Site Selection, Baseline Modeling, and Strategic Environmental Assessment of REDD+ Projects

Targeting area and comparing the effect of different land use/land cover (LULC) scenarios on greenhouse gases (GHGs) emission reduction (Case study: Hyrcanian forests in Iran)

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