A land-use and land-cover modeling strategy to support a national assessment of carbon stocks and fluxes

Information on future land-use and land-cover (LULC) change is needed to analyze the impact of LULC change on ecological processes. The U.S. Geological Survey has produced spatially explicit, thematically detailed LULC projections for the conterminous United States. Four qualitative and quantitative scenarios of LULC change were developed, with characteristics consistent with the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES). The four quantified scenarios (A1B, A2, B1, and B2) served as input to the forecasting scenarios of land-use change (FORE-SCE) model. Four spatially explicit data sets consistent with scenario storylines were produced for the conterminous United States, with annual LULC maps from 1992 through 2100. The future projections are characterized by a loss of natural land covers in most scenarios, with corresponding expansion of anthropogenic land uses. Along with the loss of natural land covers, remaining natural land covers experience increased fragmentation under most scenarios, with only the B2 scenario remaining relatively stable in both the proportion of remaining natural land covers and basic fragmentation measures. Forest stand age was also modeled. By 2100, scenarios and ecoregions with heavy forest cutting had relatively lower mean stand ages compared to those with less forest cutting. Stand ages differed substantially between unprotected and protected forest lands, as well as between different forest classes. The modeled data were compared to the National Land Cover Database (NLCD) and other data sources to assess model characteristics. The consistent, spatially explicit, and thematically detailed LULC projections and the associated forest stand-age data layers have been used to analyze LULC impacts on carbon and greenhouse gas fluxes, biodiversity, climate and weather variability, hydrologic change, and other ecological processes.

Watershed-scale hydrology and soil erosion are the main environmental components that are greatly affected by environmental perturbations such as climate and land use and land cover (LULC) changes. The purpose of this study is to assess the impacts of scenario-based LULC change and climate change on hydrology and sediment at the watershed scale in Rib watershed, Ethiopia, using the empirical land-use change model, dynamic conversion of land use and its effects (Dyna-CLUE), and soil and water assessment tool (SWAT). Regional climate model (RCM) with Special Report on Emission Scenarios (SRES) and Representative Concentration Pathway (RCP) outputs were bias-corrected and future climate from 2025 to 2099 was analyzed to assess climate changes. Analysis of the LULC change indicated that there has been a high increase in cultivated land at the expense of mixed forest and shrublands and a low and gradual increase in plantation and urban lands in the historical periods (1984-2016) and in the predictions (2016-2049). In general, the predicted climate change indicated that there will be a decrease in precipitation in all of the SRES and RCP scenarios except in the Bega (dry) season and an increase in temperature in all of the scenarios. The impact analysis indicated that there might be an increase in runoff, evapotranspiration (ET), sediment yield, and a decrease in lateral flow, groundwater flow, and water yield. The changing climate and LULC result in an increase in soil erosion and changes in surface and groundwater flow, which might have an impact on reducing crop yield, the main source of livelihood in the area. Therefore, short- and long-term watershed-scale resource management activities have to be designed and implemented to minimize erosion and increase groundwater recharge.

Contribution of multi-objective land use optimization to carbon neutrality: A case study of Northwest China

The Mid-Atlantic region (MAR) of the U.S. is subjected to a variety of stressors that affect the headwaters of the major rivers. Some of these stressors are abandoned mine drainage, agriculture, municipal point sources, urban areas, out-of-basin diversions, competing water uses, rapid population growths in the lowlands, alterations in water availability due to climate change and habitat alteration. In addition to these regional stressors, the rapid population growths and energy sources shifting have resulted in changes in land use and land cover (LULC) over the last few decades. The interactive effects of LULC and interannual/long-term climate changes have resulted in water quality changes in the region. The goal of this research is to investigate long-term changes in stream total dissolved solids (TDS) under changing LULC and climate variability in the MAR. Also, this dissertation is intended to generate understanding of how predominant LULC features, interannual climate variability, and their pollution processes interact to influence receiving water conditions. This research consists of three complementary studies. The first study was to investigate the interactive effects of interannual climate variability and LULC on in-stream TDS trends at 27 sites in the north-central Appalachian region over a 20-year period (1990 – 2010). The second analysis was to characterize individual streams susceptibility to LULC changes and its effects on TDS changing rates at 29 monitoring sites in the MAR. The third study was to develop a modeling approach to predict the combined effects of LULC and interannual precipitation on stream TDS concentrations using data of 77 monitoring sites during 2008-2018. In these studies, traditional statistical non-parametric approaches (e.g., Mann Kendall test (MK), Theil-Sen slope estimator), principal component analysis (PCA), and advanced statistical modeling methods (structural equation modeling, SEM, and latent growth modeling, LGM) and geographical information system (GIS) techniques were used. Results of the first study showed varying TDS trends with 16 (60%) of the sites having an increasing trend, 7 (26%) having a decreasing trend, and 4 (14%) with no statistically significant trend during a time of major LULC and climate changes. The relationships between TDS and climate revealed that 55% of the sites had a negative TDS-precipitation (TDS-P) slope; 45% had a positive (TDS-P) slope; 32% of the sites had a negative TDS-T slope and 68% had a positive TDS-temperature slope (TDS-T). Principal component analysis revealed that watersheds with an increasing TDS trend were distributed along the vectors of barren, and agriculture lands

In this study, the impact of change in land use and land cover (LULC) on runoff estimation in the Kidangoor watershed was assessed using the SCS-CN technique. Recent flood-like natural disasters in Kerala are thought to be driven by changes in rainfall patterns and LULC. The accurate calculation of runoff from watersheds is urgently needed. In ArcGIS 10.5, the supervised classification approach is used to classify satellite images from 2000, 2011, 2013, and 2017. Similarly, the Inverse Distance Weighted (IDW) technique is used to produce spatial distribution maps of rainfall for each antecedent moisture condition (AMC). The runoff maps were generated by superimposing the distributed rainfall, LULC, and Hydrological Soil Group (HSG) maps. It was observed that the built-up area expanded by 168% between 2000 and 2017, whereas other classes decreased by 10–23%. However, compared to 2000, both with and without a change in LULC, runoff generation increased by just 31%, and 27% in 2017. The SCS-CN technique for runoff estimation indicates that the change in LULC in the Kidangoor watershed is insignificant. Thus, this study will help land use planners and decision-makers in limiting the potential damage from flooding when it comes to flood management techniques.

Groundwater recharge is significantly influenced by anthropogenic activities, particularly changes in land use and land cover (LULC). These long-term temporal and seasonal LULC changes alter groundwater flow dynamics, necessitating their assessment for sustainable groundwater resource management. This study investigates the effects of LULC changes on groundwater recharge processes in the sub-watershed of the Nira River, Maharashtra, India. Using Google Earth Engine, LULC classifications were generated from Sentinel-2 satellite imagery acquired over a decadal period (2014–2024). A change detection algorithm was employed to decipher the long-term spatio-temporal LULC patterns, complemented by seasonal analysis using LULC maps of wet and dry months. Historical data from government agencies and private entities validated these findings, strengthening the analysis. The results indicate a 4.6% increase in built-up areas and a 5.7% decrease in forest cover over the analysis period. Rainfall data from 2015 to 2024 was correlated with groundwater level records, revealing enhanced recharge in 2024 compared to 2014. This improvement is attributed to increased rainwater harvesting structures observed during the assessment period, contributing significantly to recharge in dug wells. Seasonal LULC variations also influenced recharge dynamics, with the dry season showing higher recharge potential compared to the wet season. These findings provide critical insights into the interplay between LULC changes, groundwater recharge processes, and sustainable water resource management in the study area.Keywords: LULC, impact assessment, Groundwater recharge, Western Deccan Basalt, India

Land use and land cover (LULC) changes through built-up area expansion always increases linearly with land demand as a consequence of population growth and urbanization. Cirebon City is a center for Ciayumajakuning Region that continues to grow and exceeds its administrative boundaries. This phenomenon has led to peri-urban regions which show urban and rural interactions. This study aims to analyze (1) the dynamics of LULC changes using cellular automata (CA), artificial neural network (ANN), and ANN-CA; (2) the influential factors (drivers); and (3) change probability in the period 2030 and 2045 for Cirebon’s peri-urban. We used logistic regression as quantitative approach to analyze the interaction of drivers and LULC changes. The LULC data derived from Landsat series satellite imagery in 1999-2009 and 2009-2019, validation of dynamic spatial model refers to 100 LULC samples. This research shows that LULC changes are dominated by built-up area expansion which causes plantations and agricultural land to decrease. The drivers have a simultaneous effect on LULC changes with r-square of 0.43, where land slope, distance from existing built-up area, distance from CBD, and accessibility are significant triggers. LULC simulation of CA algorithm is the best model than ANN and ANN-CA based on overall accuracy and overall accuracy (0.96, 0.75, 0.73 and 0.95, 0.66, 0.66 respectively), it reveals urban sprawl through the ribbon and compact development. The average probability of built-up area expansion is 0.18 (2030) and 0.19 (2045). If there is no intervention in spatial planning, this phenomenon will decrease productive agricultural lands in Cirebon's peri-urban.

Capturing the scope and trajectory of changes in land use and land cover (LULC) is critical to urban and regional planning, natural resource sustainability and the overall information needs of policy makers. Studies on LULC change are generally conducted within peaceful environments and seldom incorporate areas that are politically volatile. Consequently, the role of civil conflict on LULC change remains elusive. Using a dense time stack of Landsat Thematic Mapper images and a hybrid classification approach, this study analysed LULC changes in Kono District between 1986–1991, 1991–2002 and 2002–2007 with the overarching goal of elucidating deviations from typical changes in LULC caused by Sierra Leone's civil war (1991–2002). Informed by social survey and secondary data, this study engaged the drivers that facilitated LULC changes during war and non-war periods in a series of spatial regression models in exploring the interface between civil conflict and LULC change.

Abstract. The concept of plant functional types (PFTs) is shown to be beneficial in representing the complexity of plant characteristics in land use and climate change studies using regional climate models (RCMs). By representing land use and land cover (LULC) as functional traits, responses and effects of specific plant communities can be directly coupled to the lowest atmospheric layers. To meet the requirements of RCMs for realistic LULC distribution, we developed a PFT dataset for Europe (LANDMATE PFT Version 1.0; http://doi.org/10.26050/WDCC/LM_PFT_LandCov_EUR2015_v1.0, Reinhart et al., 2021b). The dataset is based on the high-resolution European Space Agency Climate Change Initiative (ESA-CCI) land cover dataset and is further improved through the additional use of climate information. Within the LANDMATE – LAND surface Modifications and its feedbacks on local and regional cliMATE – PFT dataset, satellite-based LULC information and climate data are combined to create the representation of the diverse plant communities and their functions in the respective regional ecosystems while keeping the dataset most flexible for application in RCMs. Each LULC class of ESA-CCI is translated into PFT or PFT fractions including climate information by using the Holdridge life zone concept. Through consideration of regional climate data, the resulting PFT map for Europe is regionally customized. A thorough evaluation of the LANDMATE PFT dataset is done using a comprehensive ground truth database over the European continent. The assessment shows that the dominant LULC types, cropland and woodland, are well represented within the dataset, while uncertainties are found for some less represented LULC types. The LANDMATE PFT dataset provides a realistic, high-resolution LULC distribution for implementation in RCMs and is used as a basis for the Land Use and Climate Across Scales (LUCAS) Land Use Change (LUC) dataset which is available for use as LULC change input for RCM experiment set-ups focused on investigating LULC change impact.

Modeling the effect of land use and climate change on water resources and soil erosion in a tropical West African catch-ment (Dano, Burkina Faso) using SHETRAN

ContextThere is an ongoing debate whether local biodiversity is declining and what might drive this change. Changes in land use and land cover (LULC) are suspected to impact local biodiversity. However, there is little evidence for LULC changes beyond the local scale to affect biodiversity across multiple functional groups of species, thus limiting our understanding of the causes of biodiversity change.ObjectivesHere we investigate whether landscape-wide changes in LULC, defined as either trends in or abrupt changes in magnitude of photosynthetic activity, are driving bird diversity change.MethodsLinking 34 year (1984–2017) time series at 2745 breeding bird survey (BBS) routes across the conterminous United States of America with remotely-sensed Landsat imagery, we assessed for each year what proportion of the landscape surrounding each BBS route changed in photosynthetic activity and tested whether such concomitant or preceding landscape-wide changes explained changes in bird diversity, quantified as relative abundance (geometric mean) and assemblage composition (Bray–Curtis index).ResultsWe found that changes in relative abundance was negatively, and assemblage composition positively, correlated with changes in photosynthetic activity within the wider landscape. Furthermore, landscape-wide changes in LULC in preceding years explained on average more variation in bird diversity change than concomitant change. Overall, landscape-wide changes in LULC failed to explain most of the variation in bird diversity change for most BBS routes regardless whether differentiated by functional groups or ecoregions.ConclusionsOur analyses highlight the influence of preceding and concomitant landscape-wide changes in LULC on biodiversity.

Are we losing water mostly due to climate change? This study delves into that question. The main innovation of this research lies in developing a methodology that forecasts future shifts in water resources through the use of nondimensional indicators, such as Landscape Hydric Potential (LHP), caused by climate change scenarios, land use and land cover (LULC) projections, and a combination of both factors. The LHP method draws upon a range of indicators that shape the geosphere at the catchment scale, namely: hydrogeological conditions, soil conditions, climatic conditions, geomorphological conditions, and LULC. The analysis was carried out for 33 catchments located in the Upper Vistula River Basin in East-Central Europe. The study was conducted in the following stages. First, LHP values were calculated for the present conditions. Subsequently, an analysis of anticipated changes in LULC and in climate were conducted for the near and far future. Lastly, simulations were performed to project how LHP might evolve, considering potential changes in climate and LULC over time. The results have shown that under current climatic conditions, mountainous catchments are characterized by higher LHP values than catchments located in highlands or plains. Agricultural areas are projected to experience the largest changes in LULC. Climatic water balance indicate minimal changes, irrespective of time horizon. Our studies conclude that changes in predicted LULC could have a more significant impact on LHP values than the projected climate change.

Changes in land use and land cover (LULC) are one of the changes that are directly affected by human activities which are largely driven by socio-economic factors. Knowing, analyzing and modeling LULC change transformation plays an important role in planning, management and decision making activities. The purpose of this study is to develop LULC projection models using CA-Markov analysis to predict LULC in 2034 in Mamuju Subdistrict as the city centre of Mamuju Regency which has a rapid change in LULC. Inputs used in this research are data on river networks, roads, public facilities, LULC 2009, LULC 2014 and LULC 2019. The results showed that for 25 years (2009-2034) forest dominated land cover in Mamuju Subdistrict and settlement into LULC classes which has increased. In 2034, settlements have an area of 5.88% of the total study area or grow by 4.93% over the period of 2009-2034. The CA-Markov model used in predicting LULC changes was validated and produced a kappa coefficient of 0.969 (96.9%) which showed that the model had successfully predicted LULC changes in the study area.

<p>Over the past few decades, there has been over increasing pressure on land due to population growth, urbanization, agriculture expansion and industrialization. The change in land use and land cover (LULC) pattern are highly dependent on human intervention. Deforestation pattern has started due to growth of suburbs, cities, and industrial land. The alarming rate in change of LULC pattern was on a rising trend since 1990s and has been increasing over time. This study focuses on analyzing the changes in LULC pattern in Dublin, Ireland over the past two decades using remotely sensed LANDSAT satellite imagery data, and quantify the effect of LULC change in streamflow simulation in watershed at Dublin by using rainfall-runoff model. Benefit of using remotely sensed image to investigate LULC changes include availability of high-resolution spatial data at free of cost, images captured at high temporal resolution to monitor the changes in LULC during both seasonal and yearly timescale and readily availability of data. The potential classification of landforms has been done by performing both supervised as well as unsupervised classification. The results obtained from the classified images have been compared to google earth images to understand the accuracy of the image classification. The change in LULC can be characterized by changes in building density and urban/artificial area (build up areas increase due to population growth), changes in vegetation area as well as vegetation health, changes in waterbodies and barren land. Furthermore, a set of indices such as vegetation index, building index, water index and drought index were estimated, and their changes were monitored over time. Results of this analysis can be used to understand the driving factors affecting the changes in LULC and to develop mathematical models to predict future changes in landforms. Soil Water Assessment Tool (SWAT) based rainfall-runoff model were used to simulate the changes in runoff due to the LULC changes in watershed over two decades. The developed framework is highly replicable because of the used LANDSAT data and can be applied to generate essential information for conservation and management of green/forest lands, as well as changes in water availability and water stress in the assessed area.</p>

Abstract. Land-use and land-cover (LUC) changes are a key uncertainty when attributing changes in measured atmospheric CO2 concentration to its sinks and sources and must also be much better understood to determine the possibilities for land-based climate change mitigation, especially in the light of human demand on other land-based resources. On the spatial scale typically used in terrestrial ecosystem models (0.5 or 1°) changes in LUC over time periods of a few years or more can include bidirectional changes on the sub-grid level, such as the parallel expansion and abandonment of agricultural land (e.g. in shifting cultivation) or cropland–grassland conversion (and vice versa). These complex changes between classes within a grid cell have often been neglected in previous studies, and only net changes of land between natural vegetation cover, cropland and pastures accounted for, mainly because of a lack of reliable high-resolution historical information on gross land transitions, in combination with technical limitations within the models themselves. In the present study we applied a state-of-the-art dynamic global vegetation model with a detailed representation of croplands and carbon–nitrogen dynamics to quantify the uncertainty in terrestrial ecosystem carbon stocks and fluxes arising from the choice between net and gross representations of LUC. We used three frequently applied global, one recent global and one recent European LUC datasets, two of which resolve gross land transitions, either in Europe or in certain tropical regions. When considering only net changes, land-use-transition uncertainties (expressed as 1 standard deviation around decadal means of four models) in global carbon emissions from LUC (ELUC) are ±0.19, ±0.66 and ±0.47 Pg C a−1 in the 1980s, 1990s and 2000s, respectively, or between 14 and 39 % of mean ELUC. Carbon stocks at the end of the 20th century vary by ±11 Pg C for vegetation and ±37 Pg C for soil C due to the choice of LUC reconstruction, i.e. around 3 % of the respective C pools. Accounting for sub-grid (gross) land conversions significantly increased the effect of LUC on global and European carbon stocks and fluxes, most noticeably enhancing global cumulative ELUC by 33 Pg C (1750–2014) and entailing a significant reduction in carbon stored in vegetation, although the effect on soil C stocks was limited. Simulations demonstrated that assessments of historical carbon stocks and fluxes are highly uncertain due to the choice of LUC reconstruction and that the consideration of different contrasting LUC reconstructions is needed to account for this uncertainty. The analysis of gross, in addition to net, land-use changes showed that the full complexity of gross land-use changes is required in order to accurately predict the magnitude of LUC change emissions. This introduces technical challenges to process-based models and relies on extensive information regarding historical land-use transitions.