Land Use Change Dynamics Research Articles

Remote sensing and spatial databases for investigating latent urban-rural dynamics in rural, inland districts of Southern Italy

It is well-known that rural-urban patterns help to capture socioeconomic interactions between different settlement forms. The sustainability challenge requires to consider the evolution of these patterns as a reliable indicator of the dynamics of land use change and potential land degradation processes occurred in a time frame. In this research, by using multisource data (Corine Land Cover, Keyhole KH-9 and Landsat satellite images), we trace the diachronic evolution (1990-2018) of the rural-urban pattern in the provinces of Avellino and Benevento (Campania region, Southern Italy) with a specific focus on the key municipality of Ariano Irpino (1975-2018). The analysis confirms the considerable urban growth occurred in the study area, mostly in the form of urban sprawl phenomena decoupled from population growth. This happens concurrently with a transformation of the agricultural sector projected toward a greater specialization favouring agritourism activities and valuable crops (e.g., vineyards). These findings can support policy makers in future planning activities by mixing conservation, mitigation, and restoration actions.

Effect of Land Use and Land Cover Change on Soil Erosion in Alaba Sub-Watersheds of Central Rift Valley Basin, Ethiopia

The magnitude of soil erosion rates has been accelerating in the Central Rift Valley Area of Ethiopia due to land use and land cover change (LULCC). This study aimed to analyze the trends and changes in land use and land cover dynamics and to analyze the impact of LULC change on soil erosion over the Alaba Sub-Watershed, Central Rift Valley Area of Ethiopia. This study would help to plan and use appropriate land use planning, soil, and water conservation practices, thereby reducing soil erosion in the study area. The soil loss rates were estimated using an empirical prediction model of the Revised Universal Soil Loss Equation (RUSLE) outlined in the ArcGIS environment. During the study period between 1987 and 2018, the rate of change in cultivated land and bare land was raised by 9.09% and 1.08%, respectively. On the contrary, vegetation cover and grasslands had dropped by –2.03%, and -1.42% respectively. From trends of LULC changes, cultivated land revealed an increasing trend increasing from 181.28 hectares to 351.23 hectares for the period 1987 to 2018, respectively. On the other hand, vegetation and grasslands showed a declining trend from -271.92,-22.66 hectares to -305.91, -124.63 hectares, respectively. The findings of the study generally elucidate that the LULCC has a detrimental impact on soil erosion. The estimated total annual actual soil loss in the study landscape was 25707.77 tons in 1987, 29310.71 tons in 2000, and 31769.32 tons in 2018. It was also found that the mean annual soil loss rate of the study catchment was 22.69t/ha/yr., 25.87t/ha/yr. and 28.04t/ha/yr. in 1987, 2000, and 2018 respectively. About 35% and 42% of the Sub-watershed experienced high to very severe soil erosion in 1987 and 2018, respectively. The results indicate that the estimated erosion rate for about 39 % of the sub-basin area exceeds the maximum tolerable soil erosion threshold and this shows that the erosion risk condition is deteriorating in the study landscape. The top three priority levels delineated for urgent SWC measures represent those areas within the large increase in erosion risk levels, with an area of 188 hectares and accounting for 17 % of the sub-watershed area. Therefore, further detailed investigations based on data from primary and secondary sources would be important in identifying driving socioeconomic forces and consequences of LULCCs and suggesting possible alternative options to establish sustainable resource management practices in the study area.

Financial influence on global risks of zoonotic emerging and re-emerging diseases: an integrative analysis

Emerging and re-emerging infectious diseases (EIDs), such as Ebola virus disease and highly pathogenic influenza, are serious threats to human health and wellbeing worldwide. The financial sector has an important, yet often ignored, influence as owners and investors in industries that are associated with anthropogenic land-use changes in ecosystems linked to increased EIDs risks. We aimed to analyse financial influence associated with EIDs risks that are affected by anthropogenic land-use changes. We also aimed to provide empirical assessments of such influence to help guide engagements by governments, private organisations, and non-governmental organisations with the financial sector to advance a planetary health agenda. For this integrative analysis, we identified regions in the world where there was evidence of a connection between EIDs and anthropogenic land-use changes between Nov 9, 1999, and Oct 25, 2021, through a targeted literature review of academic literature and grey literature to identify evidence of drivers of anthropogenic land-use change and their association with commodity production in these regions. We only included publications in English that showed a connection between deforestation and the production of one or more commodities. Publications merely describing spatial or temporal land-use change dynamics (eg, a reduction of forest or an increase of palm-oil plantations) were excluded. As we were assessing financial influence on corporate activities through ownership specifically, we focused our analysis on publicly listed companies. Equity data and data about ownership structure were extracted from Orbis, a company information database. We assessed financial influence by identifying financial entities with the largest equity ownership, descriptively mapping transboundary connections between investors and publicly listed companies. 227 public and private companies operating in five economic sectors (ie, production of palm oil, pulp and wood products, cocoa, soybeans, and beef) between Dec 15, 2020, and March 8, 2021, were identified. Of these 227, 99 (44%) were publicly listed companies, with 2310 unique shareholders. These publicly listed companies operated in six geographical regions, resulting in nine case-study regions. 54 (55%) companies with complete geographical information were included in the countries network. Four financial entities (ie, Dimensional, Vanguard, BlackRock, and Norway's sovereign wealth fund) each had ownership in 39 companies or more in three of the case-study regions (ie, north America, east Asia, and Europe). Four large US-based asset managers (ie, Vanguard, BlackRock, T Rowe Price, and State Street) were the largest owners of publicly listed companies in terms of total equity size, with ownership amounts for these four entities ranging from US$8 billion to $21 billion. The specific patterns of cross-national ownership depended on the region of interest; for example, financial influence on EIDs risks that was associated with commodity production in southeast and east Asia came from not only global asset managers but also Malaysian, Chinese, Japanese, and Korean financial entities. India, Brazil, the USA, Mexico, and Argentina were the countries towards which investments were most directed. Although commodity supply chains and financial markets are highly globalised, a small number of investors and countries could be viewed as disproportionally influential in sectors that increase EIDs risks. Such financial influence could be used to develop and implement effective policies to reduce ecological degradation and mitigate EIDs risks and their effects on population health. Formas and Networks of Financial Rupture-how cascading changes in the climate and ecosystems could impact on the financial sector.

Assessment of ecological security risk in rocky desertification area based on land-use change model

Social and economic development, production and living needs, other human behavior requirements and various ecological engineering construction are considered as the main influencing factors of land spatial pattern changes and land ecological security risks in rocky desertification areas. From the perspective of land-use spatial change, the impact of human activities and resource utilization on land ecological security risks can be more intuitively analyzed. Based on the land-use change survey data of Phase 4 of Honghe Prefecture, China, the study measures the characteristics of land-use transfer flow and land-use dynamic change, analyzes the main influencing factors and comprehensively constructs the land ecological security risk index model, on which the land ecological security risk is classified. The results showed that the land ecological security risk degree in the three stages of the study area from 2014 to 2020 was 2016–2018 > 2018–2020 > 2014–2016, and had obvious regional characteristics. Among many influencing factors, the different activities/projects (positive or negative) types, time and intensity affect the land-use transfer flow and dynamic change characteristics, resulting in corresponding changes in the risk of land-use ecological security. Among them, man-made destruction and construction activities have the most significant negative effects on land ecological status. The empirical analysis proved that building an ecological security risk index based on the direction, area, and rate of spatial changes in land-use can accurately reflect the degree of desertification risk in rocky desertification areas. The study results can provide important information on the impact of regional development and the environmental governance policies of the land-use change in rocky desertification areas.

DYNAMIQUE DOCCUPATION DES TERRES ET TRANSFORMATIONS DES ECOSYSTEMES AUTOUR DES USINES DEGRENAGE DE COTON DE LA SOFITEX DES VILLES DE BOBO DIOULASSO ET DE BANFORA AU BURKINA FASO

The study concerns the analysis of the dynamics of land occupation around the factories Bobo 1, Bobo 2 and Banfora 1, Banfora 2 at Burkina Faso. The types of land use in these areas were analyzed in order to understand the changes resulting from the industrial activities, urbanization and other factors. The tools used are Envi 5.3 for the processing of satellite images, the Excel table for the processing and production of graphics and ArcGIS 10.5 for data mapping. The observed are the conversion of agricultural land into industrial areas, urban expansion and the modification of the natural ecosystem. The areas occupied by residential areas increased on average by 615.76 hectares/year between 1986 and 2021 around the Banfora factories and by 1885.41 hectares/year between 1976 and 2021, for the Bobo-Dioulasso units. The comparison between the Bobo 1, 2 and Banfora 1, 2 factories revealed similar but also distinct patterns of land cover change. The study contributed to a better understanding of the dynamics of land use change and provided information to guide urban planning policies, industrial development strategies and environmental preservation initiatives in these two cities.

Spatiotemporal Dynamic Analysis and Simulation Prediction of Land Use and Landscape Patterns from the Perspective of Sustainable Development in Tourist Cities

The unreasonable development of land use and use of landscape patterns are the primary causes of the unsustainable growth of tourism cities. Dawa District, a well-known tourist destination in China, was chosen as the research object in order to scientifically evaluate and simulate the dynamic changes in land use. Landsat remote sensing images of the Dawa District from 2011, 2016, and 2021 were processed, using the development of regional tourism in 2016 as the dividing point. Using Arcgis10.3, ENVI5.3, and Fragstats tools, GIS spatial analysis and landscape index analysis were used to explore the spatiotemporal dynamic characteristics of land use and landscape patterns. The CA-Markov model was used to simulate and predict the land use landscape pattern in 2026. The results show that the rapid expansion of built-up areas in 2011–2021 and the increasing marginal complexity and fragmentation of landscape patterns are the main evolution trends. In 2016, emphasis was placed on sustainable land resources, and the area of forested water gradually recovered. However, the patch density (PD) and number of patches (NP) indices reflecting fragmentation still showed a 1.5–2 fold increase. It is expected that in 2026, the dual pressure of urban development and ecological sustainable construction will enhance connectivity and aggregation, and the ecological environment will gradually recover. At the same time, the competition for various types of land will become increasingly prominent, and agricultural activities will be affected. In summary, optimization strategies are proposed from the perspectives of government, land, landscape, and industry, aiming to promote the sustainable development of land and landscape resources in tourist cities.

From railroad imperialism to neoliberal reprimarization: Lessons from regime-shifts in the Global Soybean Complex

Soybeans are ubiquitous in the global food system. As a major forest risk commodity, they are also at the heart of efforts to untangle the dynamics of land use change and associated impacts resulting from distant drivers. However, land system science has so far largely ignored the historically and socially embedded nature of these entanglements. This results in snapshot-like representations relying on neoclassical approaches to production and consumption. Here, we trace the evolution of the global soybean complex (GSC) since the late nineteenth century. We analyze how in the context of external developments soybeans have been channeled into different provisioning systems. This has occurred in a series of socio-ecological fixes, facilitated by socio-technological innovations and public sector interventions, motivated by different impediments to capital accumulation. Today, several emerging socio-technological practices promise to transform the GSC towards sustainability. We argue that the contemporary GSC inherits defining properties from the past, particularly the postwar strategy of using industrial animal farming to add value to surplus grains and oilseeds. The expanding GSC is therefore not merely a result of increasing demand, but rather the outcome of different provisioning systems’ continued dependence on soybeans. Future transitions will depend on public interventions and the influence of vested interest in current socio-metabolic patterns.

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study’s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond.

MULTI-TEMPORAL URBAN LAND-USE CHANGE DETECTION AND PREDICTION USING CNN-BASED CA-MARKOV MODEL FROM GAOFEN SATELLITE IMAGES

Abstract. The intelligent interpretation of land-use change has become a research frontier. Reasonably and effectively utilizing limited land resources and making scientific predictions to promote sustainable utilization of land resources is significant for establishing a resource-saving and environmentally friendly society. Remote sensing technology can efficiently complete multi-temporal and dynamic land-use change detection, especially using high-spatial resolution remote sensing images. However, the existing land-use change and prediction have not been combined. In addition, land-use change detection mainly relies on shallow feature design, resulting in low prediction accuracy and weak generalization performance. To solve the above problems, we proposed a CNN-based CA Markov model using multi-temporal GaoFen satellite remote sensing images for the change detection and prediction of land cover. Taking the city of Panzhihua in China as an example, the study constructed training sample data that includes a multi-temporal remote sensing training dataset from 2006, 2010, 2015, and 2021 using GaoFen satellite remote sensing images. Meanwhile, a multitemporal CNN land-use detection model was constructed to generate a land-use transfer matrix by training the dataset. Furthermore, the comprehensive driving factors were selected, including terrain factors (height and slope) and social factors (economic and population density). Then, the CA-Markov model was constructed to predict the land-use development trend in Panzhihua City after ten years. Compared with the traditional methods, experimental results demonstrate that the proposed model can improve the model's automatic interpretation ability and prediction accuracy with an increase of 24.6% in the FoM index and 4.37% in the Kappa coefficient.

Mapping and spatiotemporal dynamics of land-use and land-cover change based on the Google Earth Engine cloud platform from Landsat imagery: A case study of Zhoushan Island, China

Land resources are an essential foundation for socioeconomic development. Island land resources are limited, the type changes are particularly frequent, and the environment is fragile. Therefore, large-scale, long-term, and high-accuracy land-use classification and spatiotemporal characteristic analysis are of great significance for the sustainable development of islands. Based on the advantages of remote sensing indices and principal component analysis in accurate classification, and taking Zhoushan Archipelago, China, as the study area, in this work long-term satellite remote sensing data were used to perform land-use classification and spatiotemporal characteristic analysis. The classification results showed that the land-use types could be exactly classified, with the overall accuracy and Kappa coefficient greater than 94% and 0.93, respectively. The results of the spatiotemporal characteristic analysis showed that the built-up land and forest land areas increased by 90.00 km2 and 36.83 km2, respectively, while the area of the cropland/grassland decreased by 69.77 km2. The areas of the water bodies, tidal flats, and bare land exhibited slight change trends. The spatial coverage of Zhoushan Island continuously expanded toward the coast, encroaching on nearby sea areas and tidal flats. The cropland/grassland was the most transferred-out area, at up to 108.94 km2, and built-up land was the most transferred-in areas, at up to 73.31 km2. This study provides a data basis and technical support for the scientific management of land resources.

Progress toward the sustainable development of world cultural heritage sites facing land-cover changes

The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals (SDGs) that form part of the United Nations 2030 Sustainable Development Agenda. In terms of anthropogenic factors threatening the conservation of heritage properties, such a metric aids in the assessment of achievements toward heritage sustainability solving the problem of insufficient data availability. Therefore, in this study, 589 cultural World Heritage List (WHL) properties from 115 countries were analyzed, encompassing globally distributed and statistically significant samples of "monuments and groups of buildings" (73.2%), "sites" (19.3%), and "cultural landscapes" (7.5%). Land-cover changes in the WHL properties between 2015 and 2020 were automatically extracted from big data collections of high-resolution satellite imagery accessed via Google Earth Engine using intelligent remote sensing classification. Sustainability indexes (SIs) were estimated for the protection zones of each property, and the results were employed, for the first time, to assess the progress of each country toward SDG Target 11.4. Despite the apparent advances in SIs (10.4%), most countries either exhibited steady (20.0%) or declining (69.6%) SIs due to limited cultural investigations and enhanced negative anthropogenic disturbances. This study confirms that land-cover changes are among serious threats for heritage conservation, with heritage in some countries wherein the need to address this threat is most crucial, and the proposed spatiotemporal monitoring approach is recommended.

Data-driven approach to spatiotemporal dynamic risk assessment of urban flooding based on shared socio-economic pathways

Driven by the change in intense land use and land cover (LULC) due to fast urbanization, urban flooding events have become the most frequent and influential hazards over the last few decades. Accurately predicting possible flood-prone locations under the dynamic fluctuations of LULC is crucial for sustainable urban development. However, there has been sparse studies on systematic integration of LULC changes into anticipate urban development scenarios coupled with flooding vulnerability assessment. Therefore, this study proposed a robust and powerful cascade modeling chain consisting of Maximum Entropy, System Dynamics and Patch-generating Land Use Simulation in combination with shared socio-economicpathwaysfor projecting temporal and spatial dynamic changes associated with urban flooding vulnerability. Taking Guangdong Hong Kong Macao Greater Bay Area (GBA) as case study, the results showed that the increase in urban flooding was largely caused by the expansion of construction land. Overall, a substantial distinction within the scenarioswas observed and the flooding vulnerability was ranked in the order of SSP126 < SSP245 < SSP585. Under SSP585, the areas with high flooding risk will be expected to increase significantly,accounting for26% of the total areas,and nearly half of the built-upareas are exposed to flooding risk by 2050.Under SSP245, the built-up areas exposed tomedium and high flooding riskwere anticipated to cover nearly a fifth of the total areas.Under SSP126 scenario, nearly no change was predicted in the area with high flooding risk, with only increase of 1%,future urbanization hotspots associated with serious flooding risks will likely to be found on thefringeof the GBA’s construction areas,in line with the extent of construction land expansion.The finding of this study will shed a comprehensive insight into the identification of spatial and temporal distribution of urban flooding vulnerability to facilitate the exploration of flood mitigation measures associated with dynamic changes in urban land use.

Dynamics of Land Use Change and The Relationship with Food Sufficiency in Pacitan Regency

Infrastructure development in an area affects changes in existing land use, one of them to meet demand in development. Agricultural land use change can affect agricultural production and productivity. This study aims to determine the conditions of land use change and food sufficiency in Pacitan Regency and the relationship between the two variables. The data used in the analysis are data on land use and food production from 2009-2017 to calculate food sufficiency. The research method is quantitative descriptive with Pearson correlation statistics. The results showed that agricultural land use in Pacitan Regency was dominated by dry land agriculture. Paddy fields and dry land agricultural have decreased, while non-agricultural land has increased. The food sufficiency of the four food commodities, namely rice, corn, sweet potatoes, and cassava, has fluctuated in recent years. However, the overall condition of food sufficiency from 2009-2017 decreased from 6.48 in 2009 to 5.04 in 2017. Based on the Pearson correlation test results in Pacitan Regency, there was no connection and significant relationship between land use change and food sufficiency. This result can occur due to an increase in population, changes in the cultivation of food crops into woody plants, changes, and a decrease in land productivity.

Using Open Foris Collect Earth in Kyrgyzstan to support greenhouse gas inventory in the land use, land use change, and forestry sector.

The Kyrgyz Republic (Kyrgyzstan) is one of the countries most vulnerable to the adverse effects of climate change in Central Asia. The land use, land use change, and forestry (LULUCF) sector is critical in climate change mitigation in Kyrgyzstan and is integral to national greenhouse gas (GHG) inventories. However, consistent, complete, and updated activity data is required for the LULUCF sector to develop a transparent GHG inventory. Collect Earth (CE), developed by the Food and Agriculture Organization of the United Nations (FAO), is a free, user-friendly, and open-source tool for collecting activity data for the LULUCF sector. CE assists countries in developing GHG inventories by providing consistent and complete land representation. This article reports an estimate of land use and land-use change dynamics in Kyrgyzstan, based on analyzing 13,414 1-hectare (ha) sampling units through an augmented visual interpretation approach using satellite imagery at the very high spatial and temporal resolution available through the Google Earth platform. The results show that in 2019, forests covered 1.36 million ha or 6.83% of the total land with a 6.23% uncertainty. This estimate was 5 to 16% higher than previous estimates, detecting an additional 63,024 to 188,164ha of forestland that had not been reported previously. The new estimates suggest an average increase of 10.4% in the current forestlands of Kyrgyzstan.

Assessing hydrological and water quality responses to dynamic landuse change at watershed scale in Mississippi

The hydrology and water quality modeling in a watershed are affected by land use land cover (LULC) input. This study differs from numerous LULC change studies by introducing multi-year LULC input in a single simulation of Soil and Water Assessment Tool (SWAT) model. The proposed approach highlighted the outperformance of the model with dynamic LULC input (DM) over static LULC input (SM) based on the magnitude and direction of the hydrological responses. The difference between DM and SM outputs allowed for studying effects of historical LULC change. Additionally, agricultural management operation inputs enabled more realistic simulation of runoff, sediments, total nitrogen (TN), and total phosphorus (TP). The SM used static landuse data layers for 2009, and DM used landuse data layers for 2009, 2015, and 2018 to represent changes in LULC distribution over time. The expansion of agricultural land (0.9%) and forest cover (0.5%), as well as the reduction of grassland, water, and barren areas (1.4%), were the significant LULC changes from 2009 to 2018. Even though the expansion of forest cover was identified from 2009 to 2015, a declining trend was observed from 2015 to 2018. The agricultural land cover increased consistently from 2009 to 2018. The expansion of agricultural land increased average annual surface runoff, sediment yield, TN, and TP loads by 1.3%, 5.4%, 5.8%, and 5.9% respectively at watershed scale as determined by DM model simulation results. At sub-watershed scale, agricultural land expansion increased runoff, sediment, TN, and TP loads by up to 5%, 16%, 15%, and 15% respectively whereas, the expansion of forest cover resulted in reduction in same parameters by up to 5%, 15%, 23%, and 26% respectively. In general, the study determined that the integration of dynamic LULC and agricultural operations in SWAT allows a more accurate representation of agricultural watersheds for hydrological and water quality analysis.

Differing taxonomic responses of mosquito vectors to anthropogenic land-use change in Latin America and the Caribbean.

Anthropogenic land-use change, such as deforestation and urban development, can affect the emergence and re-emergence of mosquito-borne diseases, e.g., dengue and malaria, by creating more favourable vector habitats. There has been a limited assessment of how mosquito vectors respond to land-use changes, including differential species responses, and the dynamic nature of these responses. Improved understanding could help design effective disease control strategies. We compiled an extensive dataset of 10,244 Aedes and Anopheles mosquito abundance records across multiple land-use types at 632 sites in Latin America and the Caribbean. Using a Bayesian mixed effects modelling framework to account for between-study differences, we compared spatial differences in the abundance and species richness of mosquitoes across multiple land-use types, including agricultural and urban areas. Overall, we found that mosquito responses to anthropogenic land-use change were highly inconsistent, with pronounced responses observed at the genus- and species levels. There were strong declines in Aedes (-26%) and Anopheles (-35%) species richness in urban areas, however certain species such as Aedes aegypti, thrived in response to anthropogenic disturbance. When abundance records were coupled with remotely sensed forest loss data, we detected a strong positive response of dominant and secondary malaria vectors to recent deforestation. This highlights the importance of the temporal dynamics of land-use change in driving disease risk and the value of large synthetic datasets for understanding changing disease risk with environmental change.

The effect of pretreatment choice on cellulosic ethanol production from sugarcane straw: An insight into environmental impact profile and GHG emissions mitigation potential in Brazil

The exploration of residual lignocellulosic biomass for biofuel production is crucial to achieve significant greenhouse gas (GHG) emissions mitigation within the following decades, translating to diminished agricultural environmental impact and land-use change dynamics. On the other hand, cellulosic ethanol production pathways are typically resource-intensive, in energy and chemical terms, which directly influence its carbon intensity. Pretreatment choice, to this end, is key, since it dictates the overall process performance and yield, and may include crucial flows, under the life-cycle perspective, such as solvents and other chemicals. This work, then, aims to evaluate the effect of pretreatment choice, namely hydrothermal (HT), steam explosion (SE), and alkaline (AK), in the technical and environmental performance of cellulosic ethanol production from sugarcane straw (SCS), extending this analysis to the final GHG emissions mitigation potential for gasoline and fossil-generated electricity substitution, under the Brazilian context in São Paulo. Results show that, while AK provided the highest ethanol yield, this pretreatment option gave the lowest electricity generation surplus, and its sodium hydroxide usage was identified as an important environmental hotspot in most impact categories, which narrowed down its GHG emission mitigation gap for gasoline substitution. HT and SE presented similar ethanol and electricity yields, with SE being the most balanced option in terms of productivity and environmental impact profile. By selecting the SE route, all of the available SCS in São Paulo could be converted into 10% of the Brazilian annual ethanol production, and mitigate 5.4 MtCO2e of gasoline emissions, 15% of the Brazilian Biofuel Policy (RenovaBio) target for 2022.

PREDIKSI KONVERSI LAHAN PERTANIAN BERBASIS ARTIFICIAL NEURAL NETWORK-CELLULAR AUTOMATA (ANN-CA) DI KAWASAN SLEMAN BARAT

Analysis and prediction of land conversion using spatial-temporal data are essential for environmental monitoring and better land use planning and management. The West Sleman area has the potential to experience land use changes due to anthropogenic factors. This study aimed to determine the spatial-temporal dynamics of land use change in 2012-2022 and predict future land use change using the ANN-CA model for 20 years (2022-2042). Analyzed the spatial-temporal dynamics of land use change based on land use data derived from SPOT imagery, then predicted future land use change with the ANN-CA model using the MOLUSCE plugin on QGIS Desktop 2.18.11. The simulation results showed an accuracy of 86.66% and an overall Kappa value of 83% obtained by comparing the actual data in 2022 with the simulated data on land use change in the same year. The irrigated paddy fields decreased by 6.39% (685.22 ha) due to conversion to settlements. The area of residential buildings increased by 4.65% (498.49 ha) during 2012- 2017. Predictions of land use change in 2022-2042 show that the reduction of irrigated paddy fields will continue, and the number of residential buildings tend to increase.

Utilizing Google Earth Data to Assess Spatial-Temporal Land Use Changes around Jatibarang Reservoir, Semarang City

This study aims to assess the spatial-temporal land use changes around Jatibarang Reservoir in Semarang City, Indonesia City from 2012 to 2022, utilizing Google Earth data. The Jatibarang Reservoir and its surrounding areas have been subject to rapid urbanization and land use transformations in recent years, leading to significant environmental and socio-economic implications. The classification accuracy of seven land use categories was validated, demonstrating a high level of accuracy. The analysis reveals a dominance of agricultural land use, with notable shifts observed in forest cover, settlements, and reservoir areas. Population growth, economic development, and policy interventions are identified as the main drivers behind these land use changes. The spatial analysis using Nearest Neighbor Analysis (NNA) indicates a scattered distribution pattern of land use changes, primarily concentrated in the western part of the reservoir area. These land use changes have significant social, economic, and environmental implications, impacting the sustainability of the reservoir and surrounding areas. The study highlights the need for improved data quality, rigorous validation methods, and spatial modeling approaches in future research. By understanding the dynamics of land use changes and their implications, policymakers and stakeholders can make informed decisions for sustainable land management and environmental conservation in similar contexts.

Assessing the role of protected areas in the land-use change dynamics of a biodiversity hotspot.

Although protected areas (PAs) are designed to safeguard natural ecosystems from anthropic modifications, many PAs worldwide are subjected to numerous human-induced impacts. We evaluated whether the establishment of PAs in the Upper Paraná River floodplain region could reduce anthropic landscape changes and whether there is a difference in protection when using different PA restriction categories. We analyzed the overall landscape dynamics using 30years of land-use time series data and evaluated the change intensity via a partial land-use intensity analysis. Despite the increasing landscape anthropization, the PAs seemed to relieve the general change process, protecting natural areas mainly from agricultural expansion. Concerning the degree of use restriction, more restricted protection led to less human-induced changes. Finally, accessing PA effectiveness is a multidisciplinary challenge for researchers; however, this knowledge is crucial to avoid misunderstandings or poorly crafted public policies or decisions that may harm the environment.