Land Change Modeler Research Articles

Multi-Temporal Analysis of Past and Future Land-Cover Changes of the Third Pole

In the past few decades, both natural and human influences have contributed to the unpredictable rates of land use and land-cover change (LUCC) in glacially devastated places. Monitoring and identifying the geographic and temporal land-cover changes and driving forces in this unique type of area may help to give the scientific basis needed to understand the effects of climate change and human activities on LUCC. The Third Pole is one such landscape that provides inevitable key ecosystem services to over 2 billion people in Asia. However, this important landscape is increasingly being threatened by the impacts of climate change. Policy and program responses to the Third Pole’s mounting socioeconomic challenges are inadequate and lack scientific evidence. Using the land-change model (LCM) and historical data from 1992 onwards, our study attempted to (i) detect the spatial patterns of land use and land-cover changes in the Third Pole from 1992 to 2020; and (ii) project them into 2060. Our analysis shows that the land use and land-cover types in the Third pole are undergoing changes. About 0.07% of the snow and ice have melted in the last three decades, indicating global warming. This melt has resulted in increasing water bodies (0.08%), especially as glacial lakes. This has significantly increased the risk of glacial outburst floods. Other key alpine land-cover types that decreased are bare land (0.6%) and agricultural land (0.05%). These land types represent important habitats for wild flora and fauna, grazing land for livestock, and food for nomads, and their loss will directly degrade ecological services and the health and wellbeing of the nomads. Land cover of forest, shrubs, and scanty vegetation have all increased by 0.3%, 0.02%, and 0.77%, respectively, inducing socio-ecological changes in the Third pole mountains. Further predication analysis showed that snow and ice, along with bare land, will continue to recede whereas forest, grassland, water bodies, shrubland, sparse vegetation, and settlement will increase. These results indicate the increasing impact of global warming that will continue to change the Third Pole. These changes have serious implications for designing adaptation and mitigation interventions in the mountains. We recommend more detailed research to investigate the underlying factors that are changing the Third Pole to develop policy and programs to help humans, livestock, and biodiversity adapt to the changes in these remote and harsh mountains. This will also help to mitigate the effects on downstream communities.

Spatial Dynamics and Predictive Analysis of Vegetation Cover in the Ouémé River Delta in Benin (West Africa)

The vegetation cover of the Ouémé Delta constitutes a biodiversity hotspot for the wetlands in southern Benin. However, the overexploitation of natural resources in addition to the intensification of agricultural practices led to the degradation of the natural ecosystems in this region. The present work aims to reconstruct, using remote sensing, the spatial dynamics of land use in the Ouémé Delta in order to assess the recent changes and predict the trends in its vegetation cover. The methodology was based on remote sensing and GIS techniques. Altogether, this process helped us carry out the classification of Landsat images for a period of 30 years (stating year 1990, 2005, and 2020) via the Envi software. The spatial statistics resulting from this processing were combined using ArcGIS software to establish the transition matrices in order to monitor the conversion rates of the land cover classes obtained. Then, the prediction of the plant landscape by the year 2035 was performed using the “Land Change Modeler” extension available under IDRISI. The results showed seven (07) classes of occupation and land use. There were agglomerations, mosaics of fields and fallow land, water bodies, dense forests, gallery forests, swamp forests, and shrubby wooded savannahs. The observation of the vegetation cover over the period of 15 years from 1990 to 2005 showed a decrease from 71.55% to 63.42% in the surface area of the Ouémé Delta. A similar trend was noticed from 2005 to 2020 when it reached 55.19%, entailing a loss of 16.37% of the surface area of natural habitats in 30 years. The two drivers of such changes are the fertility of alluvial soils for agriculture along and urbanization. The predictive modeling developed for 2035 reveals a slight increase in the area of dense forests and shrubby wooded savannas, contrary to the lack of significant decrease in the area of gallery forests and swamp forests. This is key information that is expected to be useful to both policy and decision makers involved in the sustainable management and conservation of natural resources in the study area.

Impact evaluation and analysis at a river basin scale under projected climate and land-use change

Abstract This study evaluated the individual as well as integrated impacts of climate and land use change on streamflow over Meenachil River Basin, Kerala, India. The hydrological model SWAT was used to simulate future streamflow simulations under representative concentration pathway (RCP) 4.5 and 8.5 scenarios for the medium term (2025–2055) and long term (2056–2086). The land use land cover change was projected using land change modeler (LCM) of TerrSet software for the future period. Climate model simulations were taken to predict future streamflow at the regional scale, and an attempt was made to reduce the uncertainty associated with future predictions. According to the findings, streamflow was influenced by climate change (68.07%) and land use change (31.92%), with climate change having a higher contribution rate. The findings suggest that the combined impact of climate and land use change would increase streamflow in the future. The annual average streamflow is expected to decline (5.9%) in the medium term (2025–2055) under RCP 4.5 scenario and rise by 5.3% under RCP 8.5 scenario with reference to the observed streamflow for the period 1987–2017. However, in the long term, it is expected to rise by 10.56% under RCP 4.5 and 22.61% under RCP 8.5.

Predicting future coastal land use/cover change and associated sea-level impact on habitat quality in the Northwestern Coastline of Guinea-Bissau

The assessment of coastal land use/cover (LULC) change is one of the most precise techniques for detecting spatio-temporal change in the coastal system. This study, integrated Land Change Modeler, Habitat Quality Model, and Digital Shoreline Analysis System, to quantify spacio-temporal coastal LULC change and driving forces between 2000 and 2020. Combined the CA-Markov Model with Sea Level Affecting Marshes Model (SLAMM), merged local SLR data with future representative concentration pathway (RCP8.5) scenarios, and predicted future coastal LULC change and associated sea-level rise (SLR) impact on the coastal land use and habitat quality in short-, medium- and long-term. The study area had significant coastal LULC change between 2000 and 2020. The tidal flats, whose change was driven mainly by sea level, registered a total net gain of 57.93 km2. We also observed the significant loss of developed land whose change was influenced by tidal flat with a total loss of −75.58 km2. The tidal flat will experience a stunning net gain of 80.55 km2 between 2020 and 2060, making developed land the most negatively impacted land in the study area. The study led to the conclusion that the uncontrolled conversion of saltmarshes, mixed-forest, and mangroves into agriculture and infrastructures were the main factors affecting the coastal systems, including the faster coastal erosion and accretion observed during a 20-year period. The study also concluded that a low coastal elevation of −1 m and a slope of less than 2° have contributed to coastal change. Unprecedented changes will unavoidably pose a danger to coastal ecological services, socioeconomic growth, and food security. Timely efforts should be made by establishing sustainable mitigation methods to avoid the future impact.

Analysis of land use/land cover changes and prediction of future changes with land change modeler: Case of Belek, Turkey.

In the areas declared to be a tourism center by state planning, a rapid tourism-related development occurs depending on the investments in tourism, which causes a dramatic land use/land cover (LULC) change. Determining, monitoring, and modeling of LULC changes are required in order to ensure the conservation-use balance and sustainability within such vulnerable areas that are under development pressure. This study consists of four steps. In the first step, the Landsat images dated 1985, 2000, 2010, and 2021 were classified using the maximum likelihood method and the LULC of Belek Tourism Center located in Turkey were determined. The second step included the identification of areal and spatial changes between the LULC classes for the four periods. In the third step, the LULC changes in Belek Tourism Center for 2040 were modeled using the land change modeler. Last step evaluated the relationship between the modeled spatial development pattern and the current planning decisions. According to the results obtained during 36years, the rates of built-up, forest, and water body areas have increased by 11.91%, 13.67%, and 0.82%, respectively, whereas the rates of barren land and agricultural areas have reduced by 22.25% and 4.15%, respectively. The LULC map modeled for 2040 predicts the built-up areas to expand by 8.25% and the agricultural areas to shrink by 5.42% by comparison with 2021. This study will contribute as a key measure for planners, policy-, and decision-makers to make decisions related to sustainable land use in the areas declared to be a tourism center.

Impacts of climate and land use/cover changes on streamflow at Kibungo sub-catchment, Tanzania

The impacts of changing climate and land use/cover on streamflow in the Kibungo sub-catchment were evaluated using the Soil and Water Assessment Tool (SWAT). Rainfall, minimum, and maximum temperature data for six stations from the ensemble mean of the RCMs (RCA4, RACMO22T, CCLM4-8-17) were used under RCP 4.5 and RCP 8.5. The homogeneity and trend test were used to detect the change point and identify the pattern in the annual time series, respectively. Land Change Modeler (LCM) was used to predict land use maps of 2040 and 2070 from historical maps. Streamflow was simulated for 2021–2040 and 2041–2070 based on the homogeneity test results. The model calibration (2009–2016) and validation (2009–2016) for streamflow were successful. The homogeneity test detects a change point in 2040. A significant decrease in annual rainfall by 22.9 mm/yr (RCP 4.5) and 57 mm/yr (RCP 8.5) for 2021–2040 and an insignificant decrease was obtained during 2041–2070 under both emission scenarios. The annual temperature increased insignificantly by 0.004 °C/yr under RCP 4.5 while a significant increase of 0.21 °C/yr under RCP 8.5 was observed for 2021–2040. For 2041–2070, a significant increase of 0.016 °C/yr (RCP 4.5) and 0.045 °C/yr (RCP 8.5) was observed. The change in land use/cover resulted in increasing the build-up area (84%), agricultural fields (55.6%), and a decrease in the forest area (10.5%) during 2021–2040. During 2041–2070, the build-up area increased by 32.1%, the agricultural field by 36%, and the forest area decreased by 11%. Streamflow decreased significantly by 65.4 m3/yr (RCP 4.5) and 195.9 m3/yr (RCP 8.5) from 2021 to 2040. An insignificant decrease of 13.7 m3/s (RCP 4.5) and 7.63 m3/s (RCP 8.5) was observed during 2041–2070. This study provides an insight to the managers, planners, and policymakers on environmental protection/conservation for sustainable utilization of water resources at the Kibungo sub-catchment.

Land change modeler and CA-Markov chain analysis for land use land cover change using satellite data of Peshawar, Pakistan

Urbanization is a global phenomenon that caused many regions worldwide to face dramatic Land Use Land Cover (LULC) changes associated with urban sprawl and significant consequences. This research aims to assess and predict the urban growth of Peshawar city as well as LULC with a Cellular-Automata-Markov-Chain (CA-Markov-Chain). LULC distributions were mapped using Landsat (5, 7, and 8) data from 1998, 2003, 2008, 2013 and 2018. Long-term changes to the landscape were simulated using a CA-Markov model at 10-year intervals from 2018, 2028 and 2038. Finally, the overall accuracy and kappa coefficient were used to validate the models and LULC maps accuracies. Results indicate a substantial expansion in built-up area and vegetation class for Peshawar city replacing bare land. As a result of our research, the total built-up area was increased in 2018 was 272 km2. Peshawar's vegetation cover has increased by 25.6% throughout the study period, especially 12.7% under the billion-tree project (from 2013 to 2018). According to CA-Markov Chain analysis, we can predict the growth of built-up area and vegetation area from 299.75 km2 (23.71%) to 318.92 km2 (25.23%) and 701.38 km2 (55.48%) to 748.69 km2 (59.23%) between 2028 and 2038, based on the development from 1998 to 2018. This study is very significant for the urban designing, urban development, policy makers and law enforcement departments.

From an agent-based laboratory to the real world: Effects of “neighborhood” size on urban sprawl

Agent-based models (ABMs) have been established as a valuable research tool in the study of complex human-environment systems. However, it is still challenging to produce generalizable and practical results with ABMs for theory development. We use the case of the effects of “neighborhood” size on urban sprawl to illustrate the practice of generating and testing general conclusions from ABMs. In agent-based urban land-change models, homebuyer agents assess land utilities with a bundle of attributes including the quality of focal or zonal neighborhoods. The size of such neighborhoods, or more precisely the spatial assessment units (SAUs), has significant impacts on simulated urban patterns, yet this sensitivity of modeled impacts has not been validated with empirical evidence. Using an ABM that features markets and competitive bidding, we explored how the neighborhood or SAU size affects urban development patterns under various scenarios of homebuyer preferences, which produced a consistent finding that bigger SAUs tended to generate more sprawled patterns. To bring this finding to the real world, we evaluated relationships in data between sprawl indices in US metropolitan areas and the average sizes of their school districts—one type of real-world SAU in neighborhood delineation and a strong input to homebuyers' assessment of housing quality. While the finding has practical policy implications for urban planning and public education, this case exemplifies a path for simple ABMs to support empirically grounded theory development.

Impact of historical and future land use land cover on spatial-temporal variation of discharge and sediment load of Upper Tapi Basin, India

Abstract Trend detection of discharge and sediment load is vital to adopt suitable conservation measures as per the changes occurring. In the present study, trends of daily streamflow and sediment load for Upper Tapi Basin are analyzed using Mann-Kendall, modified Mann-Kendall, and innovative trend analysis and change points are found using Pettitt tests from 1985 to 2020 to determine time series' trend with the statistical significance. Land use land cover (LULC) for Upper Tapi Basin is prepared for three decades (1989–2020). Innovative trend analysis has shown non-monotonicity in the time series with a decreasing trend. The streamflow is found to be significantly decreasing in the basin. A decline in sediment load is observed in Burhanpur and Gopalkheda while there is an increasing trend in Yerli G.S. The rise in anthropogenic activities is the main reason behind the reduction in suspended sediment load (SSL) over the basin. The decadal analysis of LULC showed an increase in built-up area (18.3%), barren land (5–8%), waterbodies (1.2–1.9%) agricultural land (50–60.79%) and a decrease in vegetation (from 43 to 21%). The LULC for 2030 is predicted with Land Change Modeler (LCM) in IDRISI Terrset. The estimated LULC shows that the built-up area will expand as agricultural land decreases. The overall study indicates that anthropogenic activities will increase in the future. The findings of this study will aid in decision making for river basin water resource management, as well as understanding the influence of human-caused activities on flow and sediment load variance.

Are the Sundarbans, the World's largest mangroves region under threat?—An ecosystem‐based geospatial approach to assess changes past, present, and future in relation to natural and human‐induced factors

AbstractThe World's most extensive mangrove region, the Sundarbans ecosystem, is highly biodiverse but severely stressed. Past, present, and future potential changes in Sundarbans mangroves have been assessed from multi‐temporal satellite data of 1980, 2000, and 2020, elevation data, and topographical maps using geospatial techniques, digital shoreline analysis system, land change modelling, and ground truth verification. The mangrove loss was 16,025 ha between 1980 and 2020 and is predicted to be 22,286 ha between 2020 and 2050. Major changes were observed in mangroves, waterbodies, mudflats, agriculture, and aquaculture. The shoreline change by endpoint rate indicated the erosion rate by 5.81 m yr−1 in 2000–2020 compared to −0.90 m yr−1 in 1980–2000. The weighted linear regression indicated the average erosion and accretion rate of −5.96 m‐yr −1 and 4.92 m yr−1, respectively, with erosion in 76% of transects and accretion in the remaining 24% between 1980 and 2020. A sea‐level rise by 1 m will inundate 17,486 ha of mangroves. The finding revealed the dynamic nature of mangroves, past and expected future loss, the severely eroding transects, hotspots, and the consequences of mangrove loss on depending the population. As fringe mangroves will be at greater risk, speedy measures are needed to stabilize the highly eroding regions.

Land Use and Land Cover Change Assessment and Future Predictions in the Matenchose Watershed, Rift Valley Basin, Using CA-Markov Simulation

Land use and land cover change (LULC) is known worldwide as a key factor of environmental modification that significantly affects natural resources. The aim of this study was to evaluate the dynamics of land use and land cover in the Matenchose watershed from the years 1991, 2003, and 2020, and future prediction of land use changes for 2050. Landsat TM for 1991, ETM+ for 2003, and Landsat-8 OLI were used for LULC classification for 2020. A supervised image sorting method exhausting a maximum likelihood classification system was used, with the application using ERDAS Imagine software. Depending on the classified LULC, the future LULC 2050 was predicted using CA-Markov and Land Change Models by considering the different drivers of LULC dynamics. The 1991 LULC data showed that the watershed was predominantly covered by grassland (35%), and the 2003 and 2020 LULC data showed that the watershed was predominantly covered by cultivated land (36% and 52%, respectively). The predicted results showed that cultivated land and settlement increased by 6.36% and 6.53%, respectively, while forestland and grassland decreased by 63.76% and 22.325, respectively, from 2020 to 2050. Conversion of other LULC categories to cultivated land was most detrimental to the increase in soil erosion, while forest and grassland were paramount in reducing soil loss. The concept that population expansion and relocation have led to an increase in agricultural land and forested areas was further reinforced by the findings of key informant interviews. This study result might help appropriate decision making and improve land use policies in land management options.

Land-cover/land-use change dynamics modeling based on land change modeler

Land-cover/land-use change dynamics modeling based on land change modeler

Cambio de uso agrícola y vegetación (1947-2019) en la región de Los Ríos, Tabasco, México

Introduction: The spread of agricultural use leads to changes in vegetation cover, loss of biodiversity and ecosystem services. Objective: To analyze land use change and its effect on natural vegetation in the region of Los Ríos, Tabasco, Mexico, during the period 1947-2019. Materials and methods: A total of 14 land use and vegetation classes were identified using aerial photographs from 1947 and supervised classification of satellite images, verifying those from 1947 to 2000 with published cartography and those from 2019 (Landsat 8) in the field. Land use and vegetation cover change was analyzed by overlaying and comparing the maps with the Land Change Modeler module integrated in the TerrSet program. Results and discussion: In 72 years, natural vegetation was mostly replaced by crops, grassland and forest plantations, followed by human settlements. These uses, together, represented 14.2 % of the region’s surface and increased to 61.8 %; that is, an increase of 435 %. These uses replaced areas of rainforest, secondary vegetation and hydrophytes, which went from 82.3 % to 29.7 %, representing a loss of 64 % of these coverages. Between 1947 and 1984, natural vegetation suffered the greatest loss of area (53.7 %). Conclusions: The loss of natural vegetation in the region occurred because of the increase in agricultural land and human settlements. The increase in agricultural land was driven by government programs without considering the environmental factor.

Past and Future Land Use/Land Cover Changes in the Ethiopian Fincha Sub-Basin

The increasing human pressure on African regions is recognizable when looking at Land Use Land Cover (LULC) change maps, generally derived from satellite imagery. Using the Ethiopian Fincha watershed as a case study, the present work focuses on (i) identifying historical LULC change in the period 1989–2019; (ii) estimating LULC in the next thirty years, combining Geographical Information Systems (GIS) with Land Change Modelling (LCM). Landsat 5/8 images were combined with field evidence to map LULC in three reference years (1989, 2004, 2019), while the Multi-Layer Markov Chain (MPL-MC) model of LCM was applied to forecast LULC in 2030, 2040, and 2050. The watershed was classified into six classes: waterbody, grass/swamp, built-up, agriculture; forest; and shrub. The results have shown that, in the past 30 years, the Fincha watershed experienced a reduction in forest and shrubs of about −40% and −13%, respectively, mainly due to ever-increasing agricultural activities, and such a trend is also expected in the future. In fact, for the period 2019–2050, LCM simulated a significant decrease in both forest and shrubs (around −70% and −20%, respectively), in favor of more areas covered by grass (19%) and built-up (20%). It is worth noting that a decrease in natural forests can drive an increase in soil erosion, fostering siltation in the water reservoirs located in the sub-basin. The study pointed out the urgency of taking actions in the sub-basin to counteract such changes, which can eventually lead to a less sustainable environment.

Historical and future dynamics of land use land cover and its drivers in Ajora‐Woybo watershed, Omo‐Gibe basin, Ethiopia

AbstractLand use land cover (LULC) dynamics have long been recognized as a significant driver of natural resource change. As a result, understanding the spatial and temporal variation of LULC in the watershed is essential for effective natural resource management and long‐term development. This study attempts to analyze the dynamics and change drivers from 1990 to 2020 and predict the situation for 2035 and 2050 in the Ajora‐Woybo watershed. ArcGIS 10.3 and ERDAS 2015 were used to analyze quantitative data from Landsat imagery. For supervised image classification, a Maximum‐Likelihood classification algorithm was used. To identify driver variables, focus groups and key informants' interviews were done. TerrSet 18.31 software was used to predict LULC utilizing the Multi‐Layer Perceptron Neural Network and Cellular Automata‐Markov Chain models incorporated in Land Change Modeler. Six LULC classes were discovered: cultivated land, built‐up, shrub land, forest land, bare land, and water body. Cultivated land, built‐up area, and bare land have increased at the expense of shrub land and forest land over the last three decades. Trends in water bodies show both decreasing and increasing trends. According to the predicted outcomes, cultivated land, built‐up and bare land has increased, while shrub land and forest land have declined. Finally, agricultural expansion, population growth, wood extraction, resettlement, urbanization, and lack of environmental consideration were identified as the major drivers of LULC change. The study demonstrated that there have been significant changes in the watershed LULC. As a result, reversing the predicted conditions is critical to ensuring the watershed long‐term viability.

Updating and backdating analyses for mitigating uncertainties in land change modeling: a case study of the Ci Kapundung upper water catchment area, Java Island, Indonesia

In developing countries, data gaps are common and lead to uncertainties in land cover change analysis. This study demonstrates how to mitigate uncertainties in modeling land change in the Ci Kapundung upper water catchment area by comparing the outcomes of two simulation phases. A conventional cellular automata (CA)–Markov model was complemented with a multilayer perceptron (MLP) to project future land cover maps in the study area. The CA–Markov–MLP model results in high uncertainties in forested sites where a data gap is apparent in the input data (land cover maps and driver variables) and parameters. The results show that the model accuracy is improved from 47.90% in the first phase to 81.36% in the second phase. Both first and second phases integrate six demographic–economic and environmental drivers in the modeling, but the second phase also incorporates an updating and backdating analysis to revise the base-maps. This study suggests that uncertainties can be mitigated by linking such base-map revision process with the updating and backdating analyses using remote sensing datasets retrieved at different times.

Simulating the Future Land Use and Land Cover by Implementing Machine Learning Knowledge on the South-Western Zone of Rajshahi District, Bangladesh

Urbanization causes major urban pressures and changes in habitat, ecology, and environmental diversity. Particularly in developing countries, they are often characterized with strong urban sprawl. This research aimed to detect the temporal LULC change with the driving forces of LULC and forecast the future LULC via simulation in Rajshahi, Bangladesh. The LULC was classified through supervised classification using Erdas Imagine. To predict future land-use/ land cover change, the Land Change Modeler (LCM) of Terrset application was used with the implementation of CA and Markov chain analysis. “Built-up area” and “bare land” increased whereas “water bodies” and “vegetation” landforms decreased from 1991 to 2011. Although, “builtup area” and “vegetation” increased as “water bodies” and “bare land” decreased from 2001 to 2021. The driving forces of the LULC change were identified through correlation. The conventional driving forces were found to be ‘population,’ ‘literacy rate,’ ‘population density,’ ‘household income’ and ‘average household size’. When the outcome of the coefficient is positive, the driving force works proportionally. Average accuracy of the prediction model is approximately 85% and the predicted image was validated with the classified image of 2021 and the average accuracy was 92.03%. LULC for the years 2001, 2011 and 2021 along with the driving forces served as the basis to predict land-use/land cover for 2031, and 2041. This research helps to analyse urban planning policies and environmental management, and to take proper measure for proper control of LULC change Journal of Engineering Science 13(1), 2022, 61-67

Modeling forest cover dynamics in Bangladesh using multilayer perceptron neural network with Markov chain

Raghunandan Hills Reserve is an important protected area in Bangladesh that supports some remnant patches of natural forest and is the habitat of several globally threatened primates including Western Hoolock Gibbon, Northern Pig-tailed Macaque, and Capped Langur. However, deforestation and forest degradation due to anthropogenic factors, such as illegal logging and fuelwood collection are age-old problems at Raghunandan. The areas of the reserve vulnerable to future conversions due to the possible proximate or underlaying causes were unknown. This study analyzed the historical trend of forest and land-use/landcover transitions at Raghunandan Hills Reserve from 1995 until 2015 at a 10-year interval using Monte Carlo spectral unmixing and knowledge-based classification approaches to Landsat satellite images in Claslite and ArcGIS software. Based on the past trend, it then predicted the future trend of forest land-use/landcover transitions for 2025 and 2035 using an artificial multi-layer perceptron neural network with Markov Chain machine learning algorithm integrated into the land change modeler module of IDRISI/TerrSet software. Results indicated that ∼30 % to 35% of the total area of the reserve was covered by forest, which included patches of natural forest and plantations, whereas the remaining area was occupied by non-forest categories like scattered degraded forests, grasses, and shrubs. Forest cover declined during 1995–2005, and then increased slightly during 2005–2015 due to afforestation activities. This trend is likely to continue in the future with forest cover occupying nearly 40% of the reserve by 2025 and 2035. Along with identifying the areas where the forest is likely to be expanded, the areas of the reserve vulnerable to deforestation (hotspots) were also highlighted and quantified in the form of maps and statistics. The findings have useful implications for any forest conservation initiatives including the global climate change mitigation program reducing emissions from deforestation and forest degradation+, which requires identifying at-risk areas of planned and unplanned deforestation.

Detection and classification of land cover change using ISOCLUST classifier and Land Change Modeler

The agricultural sector is a mainstay sector in the Indonesian economy. The government can detect and classify land cover change phenomena using remote sensing technology to present agricultural improvement solutions. Remote sensing is an effective tool for monitoring change that occurs continuously and in a large area. This technology can record the earth’s surface from a height of hundreds of kilometers in outer space. This paper uses the ISOCLUST classifier (by producing 6 classes) and Land Change Modeler on Landsat image data in the study area located in Malang Regency, East Java Province. ISOCLUST is an iterative supervised/unsupervised hybrid classifier. Land Change Modeler is an innovative land planning and decision support software that simplifies change analysis, resource management, and habitat assessment. The results of the study using ISOCLUST showed that the overall Kappa was in medium category, 0.4018 and 0.5711. The agriculture area increased to 14.94%, the open forest area increased to 6.46% between 2015 and 2020, while sparse vegetation decreased by 19.36%. The process of detection and classification of land cover using the ISOCLUST classifier and Land Change Modeler shows that the use of remote sensing satellite data is still a consideration in government policy recommendations.

Modeling of carbon sequestration with land use and land cover in the northeastern part of the Nile Delta, Egypt

The world is currently concerned about the phenomenon of climate change and global warming because of the natural disasters that threaten the lives of living organisms on the surface of the earth. Despite this, there is a dearth of studies dealing with reducing carbon emissions in Egypt. We needed to cover this defect in an important area of Egypt, which is the eastern delta region including Manzala Lake. Land use and land cover (LU/LC) represents a vital role in carbon regulation in coastal areas. Our study uses three Landsat images in 2001, 2013, and 2021 to estimate the amount of carbon storage and sequestration and used Land Change Modeler (LCM) with Cellular Automata Markov Chain (CA-MC) model to predict land cover changes in 2031. Results show that in the period between 2013 and 2021, about half of the floating plants have been destroyed and removed. Our results demonstrate that the increased area for agricultural lands from 2013 to 2021 (2306 Mg C/km2) leads to an increased amount of carbon stoke but the decreased area for floating plants (-4508 Mg C/km2) leads to a decrease in carbon storage. Results showed that a remarkable decrease in carbon storage in the future was attributed to anthropogenic activities in the study area and also as a result of the un exploiting of environmental resources. Finally, the total cost of carbon was 1323.4, 1437.9, 1184.8, and 1296.5 $US/ha for 2001, 2013, 2021, and 2032, respectively. Results showed that the minimum cost for 2021 that a noticeable shortage due to the removal of vegetation and an increase the area of lake water. These conversions have to some extent affected the capacity of coastal habitats to sequester and store CO2. For carbon storage, we recommend afforestation of desert areas. The current design for carbon storage and sequestration is a good guide for government and decision makers for suitable sustainable development, climatic regulation, and carbon budget.