Terrestrial carbon sequestration and storage

<p>The land use and land cover (LULC) change induces hydrologic variability in a catchment and studying this variability is central to efficient water management practice in a catchment. The assessment of the alteration in hydrological processes due to LULC change and its influence on overall river ecosystem functioning is even more pertinent to developing nations that face the issue of water scarcity and pollution. In this work, we investigate the influence of the LULC change over a period of ~40 years (1970-2013) on the variability of natural or virgin flow in the Ramganga river, a major tributary of the Ganga river, India. For LULC change data, object-based image classification was performed on high-resolution satellite imageries acquired for the Ramganga river basin – CORONA (1970) and LISS IV (2013) images. The natural or virgin flows (i.e., the flow in the river without regulation practices such as construction of dams or barrages) were estimated by performing hydrological modeling using the Soil and Water Assessment Tool (SWAT). Initially, the SWAT model was set up, calibrated, and validated for the present flow scenario (i.e. with all management practices present) using LULC data of the year 2013. Natural flows were derived by removing all interventions and keeping agricultural practices only rain-fed. Next, keeping all parameters unchanged, the LULC data of the year 2013 was replaced by the LULC data of the year 1970. This enabled us to study the effects of LULC change on river hydrology between the period 1970-2013. The model showed good agreement between the observed and simulated flows with R<sup>2</sup> values of 0.82 for the calibration period (2002-2014) and 0.68 for the validation period (1990-1999). The Nash-Sutcliffe efficiency values were 0.81 and 0.66 for calibration and validation periods respectively. The comparison of LULC data between the study period (1970 and 2013) reveals that land cover classes of agriculture, built-up, mixed forest, barren land, shrubs and bushes, and water areas were altered by nearly 6%, 102%, -7%, -59%, -75%, and -2% respectively (‘-’ sign indicates decrement in the land cover area). The influence of this LULC change was evident in the results from the hydrological model. For the years 2002-2013 (calibration period), the natural flows estimated using the LULC map of 2013 at the basin outlet were observed to be higher by 3-12% compared to flows estimated using the LULC input of 1970. The estimates of mean monthly flows for the years 2002-2013 at the basin outlet reveal that while the natural flows estimated using the LULC map of 2013 were higher compared to flow estimates using the LULC map of 1970 for most of the months, the flows during the dry months (May-July) were observed to be lower for the former compared to the latter. Our work provides valuable insights into hydrological variability in a major sub-basin of the Ganga river induced due to LULC changes and we advocate that alterations associated with LULC must be incorporated into water management strategies.</p>

Assessment and prediction of carbon storage based on land use/land cover dynamics in the coastal area of Shandong Province

Human‐induced land use and land cover (LUC) changes threaten the ecosystem services of the vulnerable tropical afro‐alpine vegetation. Several LUC change studies are available for the Ethiopian highlands, but relatively little is known about LUC change in the afro‐alpine zones. In this study, LUC changes between 1964 and 2012 were mapped for the afro‐alpine zone of Lib Amba Mountain, part of the Abune Yosef Mountains in North Ethiopia. Historical LUC was derived from georeferenced aerial photographs of 1964 and 1982, and the present LUC (2012) from Bing Map satellite imagery. Based on these successive LUC maps a time‐depth map, LUC proportions, LUC transition matrices and LUC change trajectories were calculated. Two main phases of LUC change could be distinguished linked to the neo‐Boserupian perspective. (i) Between 1964 and 1982, there were large‐scale deforestation and general degradation of the vegetation above 3500 m, in a period of low population pressure; (ii) Between 1982 and 2012, an intensification of land use prevailed accompanied with a slight regeneration of the vegetation and the Erica arborea L. forest, under increased population pressure. Depth interviews indicated that local and governmental land management measures are very important for the protection against vegetation depletion and soil degradation. Quick recovery of the forest on Lib Amba provides confidence that degraded afro‐alpine areas would benefit in a short time from complete protection, given the vicinity of remaining patches of afro‐alpine vegetation. Management interventions are thus vital to restore the important ecosystem services of the afro‐alpine vegetation belt. Copyright © 2015 John Wiley & Sons, Ltd.

A regional modeling system was applied with inputs from global climate and chemistry models to quantify the effects of global change on future biogenic emissions and their impacts on ozone and biogenic secondary organic aerosols (BSOA) in the US. Biogenic emissions in the future are influenced by projected changes in global and regional climates and by variations in future land use and land cover (LULC). The modeling system was applied for five summer months for the present-day case (1990–1999, Case 1) and three future cases covering 2045–2054. Individual future cases were: present-day LULC (Case 2); projected-future LULC (Case 3); and future LULC with designated regions of tree planting for carbon sequestration (Case 4). Results showed changing future meteorology with present-day LULC (Case 2) increased average isoprene and monoterpene emission rates by 26% and 20% due to higher temperature and solar insolation. However when LULC was changed together with climate (Case 3), predicted isoprene and monoterpene emissions decreased by 52% and 31%, respectively, due primarily to projected cropland expansion. The reduction was less, at 31% and 14% respectively, when future LULC changes were accompanied by regions of tree planting (Case 4). Despite the large decrease in biogenic emission, future average daily maximum 8-h (DM8H) ozone was found to increase between +8 ppbv and +10 ppbv due to high future anthropogenic emissions and global chemistry conditions. Among the future cases, changing LULC resulted in spatially varying future ozone differences of −5 ppbv to +5 ppbv when compared with present-day case. Future BSOA changed directly with the estimated monoterpene emissions. BSOA increased by 8% with current LULC (Case 2) but decreased by 45%–28% due to future LULC changes. Overall, the results demonstrated that on a regional basis, changes in LULC can offset temperature driven increases in biogenic emissions, and, thus, LULC projection is an important factor to consider in the study of future regional air quality.

<p>Changes in land use and land cover (LULC) significantly impact ecosystem services (ES), often leading to land degradation and disrupting natural balance. This study examines how LULC changes have influenced total ecosystem services (TES) in Citarum Watershed over the past decade. Specifically, researchers analyze (1) the shifts in LULC and key ES components—water yield (WY), soil conservation (SC), and carbon storage (CS)—from 2010 to 2020, (2) the spatial relationship between land use intensity (LUI) and ES, and (3) the synchronization and distribution patterns of LUI and TES using a coupling coordination degree (CCD) model. The findings reveal significant LULC changes between 2010 and 2020, with bare/shrubland and agricultural areas expanding by 88.37% and 2.25%, while forest land and lakes declined by 0.78% and 0.09%. These transformations affected ES values, as WY and CS decreased by 15.01% and 4.98%, whereas SC increased by 12.03%. Overall, TES declined by 7.54%, with the steepest reduction (17.70%) observed in the downstream region. The coupling coordination analysis highlights an imbalance between LUI and TES, with 65 to 68% of sub-districts classified as imbalanced. These results underscore the urgent need for integrated land-use planning strategies to restore ecosystem balance and promote sustainability in Citarum Watershed.</p>

We applied an integrated approach to investigate the impacts of land use and land cover (LULC) changes on hydrology at different scales in the Loess Plateau of China. Hydrological modeling was conducted for the LULC maps from remote sensing images at two times in the Upper Fenhe River watershed using the SWAT model. The main LULC changes in this watershed from 1995 to 2010 were the transformation of farmland into forests, grassland, and built-up land. The simulation results showed that forested land contributed more than any other LULC class to water yield, but built-up land had most impact due to small initial loss and infiltration. At basin scale, a comparison of the simulated hydrological components of two LULC maps showed that there were slight increases in average annual potential evapotranspiration, actual evapotranspiration, and water yield, but soil water decreased, between the two intervals. In subbasins, obvious LULC changes did not have clear impacts on hydrology, and the impacts may be affected by precipitation conditions. By linking a hydrological model to remote sensing image analysis, our approach of quantifying the impacts of LULC changes on hydrology at different scales provide quantitative information for stakeholders in making decisions for land and water resource management.

The change detection and land use and land cover (LULC) maps are more important powerful forces behind numerous ecological systems and fallow land. The current research focuses on demarcating the spatiotemporal LULC changes, NDVI and change detections maps. These effects directly affect the ecosystem, land resources, cropping pattern and agriculture. LULC assessment and surveillance are essential for long-term planning and sustainable use of natural resources. However, we have developed the soft computing machine learning algorithm for mapping land use and land cover based on the Google earth engine (GEE) platform and change detection mapping done by SAGA GIS software. It is significantly used for ecological safety and planning under various climate variations. To accurately describe the land use and land cover classes with changes are identified in the area. This area exclusively uses the multitemporal Landsat-5 (30 m) and Sentinel-2 (10 m) imageries in LULC mapping. The GEE is a cloud-computing platform with the prevailing classification ability of random forest (RF) models to make five-year interval LULC maps for 2010, 2015 and 2020. To unique multiple RF models established as a classifier in the algorithm created by JavaScript and GEE. SAGA GIS has provided the best platform for detecting changes in land use and land cover classes. NDVI maps are created based on the cloud-based platform. These maps value ranges between −0.68 to −0.15, 0.76 to −0.29 and 0.66 to −0.11 in 2010, 2015 and 2020. Experimental outcomes indicate five classes such as water bodies, built up, barren, cropland and fallow land during 2010, 2015 and 2020. The overall accuracy of User and Producer for 2010, 2015 and 2019 years in between 86.23%, 88.34%, 85.53% and 92.51%, 94.34% and 91.54%, respectively. We have observed that (2010, 2015 − 2020) agriculture and built-up land increased by 1040.76 ha, 1246.32 ha, 1500.93 ha and 34.96 ha, 37.08 ha, 42.58 ha, respectively. Other side degraded land, fallow land, waterbodies areas (953.19 ha, 679.23 ha, 937.24 ha and 1385.73 ha, 1513.53 ha, 991.08 ha and 32.85 ha, 21.33 ha, 25.66 ha) are increased during the year of 2010, 2015 and 2020, respectively. While results have been done by GEE cloud platform and remote sensing data, this developed algorithm easily classified the land use maps from Landsat-5 and Sentinel-2 TM imagery in the machine learning approach. The determined 30-m and 10-m three-year LULC maps are made-up to deliver vital data on the changes, monitoring and understanding of which types of LULC classes and changes have occupied a place in the Rahuri area.

Land use and land cover (LULC) changes are associated with notable environmental concerns, such as alterations in land surface temperature, loss of vegetation and the impact on carbon sequestration, especially within urban settings. Urban planners worldwide are investigating these impacts. A study utilizing remote sensing and geographic information system techniques examined changes in land surface temperature (LST), normalized difference vegetation index (NDVI) and carbon sequestration in Mysuru City from 1992 to 2022. The results showed a 2.39% decrease in vegetation cover and a substantial 36.97% increase in urban areas, leading to elevated LST by an average of 4°C. Carbon storage has significantly declined to 25Mgha-1 in 2022 from 56Mgha-1 in 1992. The spatial extent of zero-level carbon storage has steadily diminished over the years. The analysis indicates a negative relationship between LST and carbon storage, with a weak correlation explained by a coefficient of determination (R2) value of 0.063 in 1992 and 0.024 in 2022. Conversely, a positive relationship exists between the NDVI and carbon storage, suggesting that effective strategies such as afforestation, reforestation and urban greening practices can enhance carbon sequestration capacity and mitigate climate change impacts in rapidly growing cities. The results of this study offer valuable insights for city planners and policymakers, aiding in the identification of areas experiencing increased LST and diminishing natural landscapes. Furthermore, the study can serve as a foundation for identifying urban hotspots, cold spots and areas conducive to urban thermal comfort. The results of this study can be enhanced by using a higher spatial resolution satellite dataset, thus providing a prospect for future research.

The extent of coastal rice paddy agricultural land is vulnerable to land use and land cover (LULC) changes to non-agricultural uses due to land degradation, one of which is caused by salinity. This study aims to detect and project LULC changes up to 2031, particularly in coastal rice paddy areas affected by salinity, by comparing LULC in 2017, 2019, and 2021. Sentinel-2 Imagery is used for LULC classification, with recordings selected during the generative phase of rice growth to obtain the most optimal rice paddy area. There are six LULC classifications: water, wetland, low-medium-high vegetation cover, and built-up area. To understand the impact of salinity on crops, several vegetation indices (VIs) such as NDVI, SAVI, EVI, and ARVI are used. The LULC changes classified according to VIs are compared with the MOLUSCE plugin based on artificial neural networkmultilayer perceptron (ANN-MLP) and Cellular Automata (CA). The comparison of VIs results shows that NDVI is better at describing LULC changes due to the influence of salinity, with a kappa value of 0.63 and a Correctness of 72.565. The LULC projection using CA in all VIs indicates that wetland areas are more likely to convert into water bodies, suggesting that high salinity land tends to be unproductive for rice paddies, making it prone to conversion.

This study addresses the significant factors contributing to warming during the 20th century, namely Greenhouse Gases (GHG) and Land Use (LU), emphasizing the need for localized hydrological impact assessments. Recognizing the limitations of Global Climate Models (GCMs) in predicting local-scale phenomena, the research employs a downscaling approach for hydrological impact studies. Observed datasets and downscaled GCM data are utilized to analyze temperature, precipitation, and potential evapotranspiration (PET) trends. The study integrates downscaled GCM data from the Coupled Model Inter-comparison Project 6 (CMIP6) to project future climate scenarios under Representative Concentration Pathways (RCPs) 4.5 and 8.5. Runoff data from three stations within the Jhelum Basin is collected and analyzed over various time scales, providing a comprehensive understanding of historical and future hydrological trends. Future climate projections are corrected using the Daily Bias Correction (DBC) method). The study then employs the Soil & Water Assessment Tool (SWAT) model, given its suitability for hydrological studies with limited data availability. SWAT is calibrated and validated using SWAT CUP, incorporating observed river discharge. The impact assessment on runoff considers different climate change and land use change scenarios. Future Land Use and Land Cover (LULC) predictions are made for 2025 to 2100, and the model is rerun to analyze the combined impact of changing climate and LULC on runoff. The study aims to achieve a robust understanding of future water resource dynamics for runoff generation; the integrated assessment of climate and land use impact on the hydrological dynamics of the Jhelum Basin uncovers substantial shifts in runoff patterns. The combination of changing climate conditions and evolving Land Use/Land Cover (LULC) scenarios reveals intricate interactions, influencing the basin's hydrological response. Future projections highlight the nuanced interplay between climate scenarios and LULC changes, offering valuable insights into the complex dynamics of water resource management. These results provide essential information for policymakers and decision-makers, guiding the formulation of adaptive strategies to address the evolving challenges in runoff generation within the Jhelum Basin. The research explores runoff responses to LULC and climate change through scenario-based setups. By quantitatively analyzing the effects on runoff and peak discharge across different periods, the study provides valuable insights for policymakers and decision-makers in water resources. This integrated assessment contributes to a more informed and sustainable approach to water resource management in the Jhelum Basin amidst changing climatic and land use conditions.

Impact of land use and land cover changes on temperature trends over India

A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery

Land use and land cover (LULC) change analyses are critical for the sustainable planning and management of natural resources in the face of rapid population growth across the globe. It is believed that LULC changes cause severe environmental challenges such as climate change, biodiversity loss, pollution, alteration to the physical and chemical properties of the soil as well as the destruction of the ozone layer. The main objective of the study was to assess the LULC changes at Hova Farm from 1992 to 2011 using geospatial technologies. Three Landsat images for 1992, 2001, and EMT+ for 2011 were used. The Landsat images had a resolution of 30m by 30m. Five LULC classes of woodland, wooded grassland, cultivated land, bushland and water body were created using the supervised classification maximum likelihood in ENVI 5.0. Field observation and measurements were also used to validate remotely sensed data. The accuracy assessment for the classified maps for 1992, 2001 and 2011 was 88.74%, 86, 67% and 87% respectively. The results indicated that the greatest LULC changes occurred between 1992 and 2001 and was attributed to the fast-track land reform programme and illegal mining activities on the farm. The study recommends the creation of a LULC database for the periodic monitoring and sustainable management of natural resources at both local and national levels in Zimbabwean.

The main objective of this research was to evaluate land use and land cover (LULC) change in Battambang province of Cambodia over the last two decades. The LULC maps for 1998, 2003, 2008, 2013 and 2018 were produced from Landsat satellite imagery using the supervised classification technique with the maximum likelihood algorithm. Each map consisted of seven LULC classes: built-up area, water feature, grassland, shrubland, agricultural land, barren land and forest cover. The overall accuracies of the LULC maps were 93%, 82%, 94%, 93% and 83% for 1998, 2003, 2008, 2013 and 2018, respectively. The LULC change results showed a significant increase in agricultural land, and a large decrease in forest cover. Most of the changes in both LULC types occurred during 2003–2008. Overall, agricultural land, shrubland, water features, built-up areas and barren land increased by 287,600 hectares, 58,600 hectares, 8300 hectares, 4600 hectares and 1300 hectares, respectively, while forest cover and grassland decreased by 284,500 hectares and 76,000 hectares respectively. The rate of LULC changes in the upland areas were higher than those in the lowland areas of the province. The main drivers of LULC change identified over the period of study were policy, legal framework and projects to improve economy, population growth, infrastructure development, economic growth, rising land prices, and climate and environmental change. Landmine clearance projects and land concessions resulted in a transition from forest cover and shrubland to agricultural land. Population and economic growth not only resulted in an increase of built-up area, but also led to increasing demand for agricultural land and rising land prices, which triggered the changes of other LULC types. This research provides a long-term and detailed analysis of LULC change together with its drivers, which is useful for decision-makers to make and implement better policies for sustainable land management.

Identifying the mechanism through which changes in land use and land cover (LULC) modulate hydrological flows is vital for water resource planning and management. To examine the impact of LULC change on the hydrology of the Gaborone Dam catchment within the upper Limpopo basin, where Notwane river is the major river within the catchment, three LULC maps for the years 1997, 2008, and 2017 were established based on a mosaic of Landsat 5 for 1997 and 2008 and Landsat 8 for 2017. The 10 m-resolution Version 200 ESA World Land Cover Map for 2021 is used as a ground truth to train the random forest (RF) classifier to identify land cover classes from Landsat 8 imageries of 2021 using the Google Earth Engine (GEE) Python API. The overall accuracy/kappa coefficient of the RF classifier is 0.99/0.99 for the training and 0.73/0.68 for the validation data sets, which indicate excellent and substantial agreements with the ground truth, respectively. With this confidence in the LULC classification, the impact of LULC change on the hydrological flow within the catchment was estimated by employing the Soil and Water Assessment Tool (SWAT) and indicator of hydrological alteration (IHA). The SWAT model calibration and validation were first performed, and the ability of the model to capture the observed stream flow was found to be good. The LULC maps from Landsat images during the 1997–2017 period show a decrease in forests and shrubland in contrast to an increase in pasture land. The expansion of pasture and cropland and the reduction in forests and shrubland led to a decline in the amount of evapotranspiration and groundwater recharge. Furthermore, the LULC change also caused a reduction in low flow during dry periods and an increase in high flow during the rainy season. The findings clearly demonstrate that LULC changes can affect the water table by altering soil water recharge capacity. The study highlighted the importance of LULC for catchment water resource management through land use planning to regulate the water level in the Gaborone Dam against the impact of climate change and growing water demands by the city of Gaborone due to population growth.