Cover Data Research Articles

Detailed height mapping of trees and buildings (HiTAB) in Chicago and its implications to urban climate studies

Abstract The vertical dimensions of urban morphology, specifically the heights of trees and buildings, exert significant influence on wind flow fields in urban street canyons and the thermal environment of the urban fabric, subsequently affecting the microclimate, noise levels, and air quality. Despite their importance, these critical attributes are less commonly available and rarely utilized in urban climate models compared to planar land use and land cover data. In this study, we explicitly mapped the height of trees and buildings (HiTAB) across the city of Chicago at 1 m spatial resolution using a data fusion approach. This approach integrates high-precision light detection and ranging (LiDAR) cloud point data, building footprint inventory, and multi-band satellite images. Specifically, the digital terrain and surface models were first created from the LiDAR dataset to calculate the height of surface objects, while the rest of the datasets were used to delineate trees and buildings. We validated the derived height information against the existing building database in downtown Chicago and the Meter-scale Urban Land Cover map from the Environmental Protection Agency, respectively. The co-investigation on trees and building heights offers a valuable initiative in the effort to inform urban land surface parameterizations using real-world data. Given their high spatial resolution, the height maps can be adopted in physical-based and data-driven urban models to achieve higher resolution and accuracy while lowering uncertainties. Moreover, our method can be extended to other urban regions, benefiting from the growing availability of high-resolution urban informatics globally. Collectively, these datasets can substantially contribute to future studies on hyper-local weather dynamics, urban heterogeneity, morphology, and planning, providing a more comprehensive understanding of urban environments.

Spatio-temporal analysis and prediction of land use landcover (LULC) change in Wular Lake, Jammu and Kashmir, India.

Landsat land use/land cover (LULC) data analysis to establish freshwater lakes' temporal and spatial distribution can provide a solid foundation for future ecological and environmental policy development to manage ecosystems better. Analysis of changes in LULC is a method that can be used to learn more about direct and indirect human interactions with the environment for sustainability. Neural network technology significantly facilitates mapping between asymmetric and high-dimensional data. This paper presents a methodological advancement that integrates the CA-ANN (cellular automata-artificial neural network) technique with the dynamic characteristics of the water body to forecast forthcoming water levels and their spatial distribution in "Wular Lake." We used remote sensing data from 2001 to 2021 with a 10-year interval to predict spatio-temporal change and LULC simulation. The validation of the calibration of predicted and accurate LULC maps for 2021 yielded a maximum kappa value of 0.86. Over the past three decades, the study region has seen an increase in a net change % in the impervious surface of 22.41% and in agricultural land by 52.02%, while water decreased by 14.12%, trees/forests decreased by 40.77%, shrubs decreased by 11.53%, and aquatic vegetation decreased by 4.14%. Multiple environmental challenges have arisen in the environmentally sustainable Wular Lake in the Kashmir Valley due to the vast land transformation, primarily due to human activities, and have been predominantly negative. The research acknowledges the importance of (LULC) analysis, recognizing it as a fundamental cornerstone for developing future ecological and environmental policy frameworks.

Evaluating MODIS cloud-free snow cover datasets using massive spatial benchmark data in the Tibetan Plateau

Accurate snow cover data is crucial for understanding climate change, managing water resources, and calibrating models. The MODIS (Moderate-resolution Imaging Spectroradiometer) and its cloud-free snow cover datasets are widely used, but they have not been systematically evaluated due to different benchmark data and evaluation parameters. Conventional methods using station observations as a ground truth suffer from underrepresentation and mismatches in temporal and spatial scales. This study established a scale-matched spatial benchmark dataset, compiling from 18,433 Landsat series and 11,172 Sentinel-2 images over two decades, totaling ∼1.86 billion samples and ∼320 million snow samples. We evaluated seven MODIS cloud-free snow cover datasets for seasons, elevation zones, land covers and subregions using this benchmark data. For the clear-sky part, NIEER_MODIS_SCE (MODIS snow cover extent product over China) performs best due to its use of optimal NDSI thresholds suitable for each land use type. This highlights the importance of regional customization in snow mapping algorithms, and it can be further improved in spring, forests and zone 1 by combining it with M*D10A1GL06. For the cloud removed part, one-step integrated spatiotemporal cloud removal datasets perform better than any other approach does. The second-best dataset is obtained from a simple but effective single temporal cloud removal method using nearby time information. For the whole dataset, the best NIEER_MODIS_SCE has an overall accuracy of 0.82 and snow retrieval accuracy of 84.56 %. It performs excellently in most settings but weakest in forests thus requiring more efficient strategies. This research provides new perspectives and methods for objectively assessing MODIS snow cover products and other relevant datasets. These methods can be readily extended to other regions and adapted to future satellite missions. And such findings may guide the selection of more valid snow cover data and the developing of even better snow detecting strategies.

Semi-Arid to Arid Scenario Shift: Is the Cabrobó Desertification Nucleus Becoming Arid?

Monitoring areas susceptible to desertification contributes to the strategic development of regions located in environments of extreme hydric and social vulnerability. Therefore, the objective of this study is to evaluate the process of soil degradation in the Desertification Nucleus of Cabrobó (DNC) over the past three decades using remote sensing techniques. This study used primary climatic data from TerraClimate, geospatial data of land use and land cover (LULC), and vegetation indices (SAVI and LAI) via Google Earth Engine (GEE) from Landsat 5/TM and 8/OLI satellites, and established the aridity index (AI) from 1992 to 2022. The results indicated 10 predominant LULC classes with native vegetation suppression, particularly in agriculture and urbanization. SAVI ranged from −0.84 to 0.90, with high values influenced by La Niña episodes and increased rainfall; conversely, El Niño episodes worsened the rainfall regime in the DNC region. Based on the Standardized Precipitation Index (SPI), it was possible to correlate normal and severe drought events in the DNC with years under the influence of El Niño and La Niña phases. In summary, the AI images indicated that the DNC remained semi-arid and that the transition to an arid region is a cyclical and low-frequency phenomenon, occurring in specific periods and directly influenced by El Niño and La Niña phenomena. The Mann–Kendall analysis showed no increasing trend in AI, with a Tau of −0.01 and a p-value of 0.97. During the analyzed period, there was an increase in Non-Vegetated Areas, which showed a growing trend with a Tau of 0.42 in the Mann–Kendall analysis, representing exposed soil areas. Annual meteorological conditions remained within the climatic pattern of the region, with annual averages of precipitation and actual evapotranspiration (ETa) close to 450 mm and an average temperature of 24 °C, showing changes only during El Niño and La Niña events, and did not show significant increasing or decreasing trends in the Mann–Kendall analysis.

Uncovering the impacts of LUCC on ecological connectivity in suburban open-pit mining concentration areas: A pattern collection of changing relationships between ecological resistance and ecological network elements

The suburban open-pit mining concentration area couples two human activities, urban sprawl and open-pit mining, while destruction of surface vegetation and ecological restoration coexist. This has also resulted in complex land use shifts that significantly affect regional ecological connectivity. However, the impact of such complex Land use/land cover changes (LUCC) on ecological connectivity in this region is not well understood Therefore, this study takes the suburban open-pit mining concentration area of Jinan City as a case study and uses Land use/land cover (LULC) data from the years 2000, 2010, and 2020 to simulate the spatio-temporal evolution of ecological networks (ENs). Subsequently, a pattern collection of changing relationships was proposed that relate changes in ecological resistance to changes in EN elements, addressing the dynamic transmission process from "spatial field" to "spatial structure entity". Based on this approach, GeoDetector is employed to reveal the response extent of EN elements to LUCC. The results indicate: (1) In the past 20 years, the area of construction land has increased by 100 %, while mining land has increased by 69 %. This significant increase in LUCC has led to a notable enhancement in ecological resistance in the study area, driving the generation of 20 % more ecological pinch points and barriers. Additionally, it has resulted in a twofold increase in both the number and length of level 1 ecological corridors. (2) The dominant status and structure of grassland, agriculture land, and woodland remain relatively stable; however, the interflow among them has caused approximately 25 % of the ecological corridors to undergo changes such as disappearance and path alteration. (3) The implementation of ecological protection and restoration has led to a continuous increase in the area of ecological sources, particularly with the mining rehabilitation increasing by 32.7 times compared to 2000, thereby driving gradual improvement in 25 % of the ecological network elements. (4) Although the quantity and length of ecological corridors in the study area remain relatively stable, evaluation results indicate that its spatial structure is gradually deteriorating, with ecological connectivity decreasing by 7.7 %. This study highlights the importance of restoration of ecological processes in sub-urban open-pit mining concentration areas. The study results can provide a scientific basis for ecological restoration and sustainable development in resource-based cities.

Urban impervious cover characterization for rainfall-runoff analysis: Impacts of data availability and resolution

Urban watershed hydrologic and water quality models, and stormwater control measure (SCM) designs, rely on accurate determination of stormwater runoff volume and impervious areas contributing to runoff. This research assesses the impacts of data availability and spatial resolution on two of the main indicators of urban impervious cover: total impervious area (TIA) and directly connected impervious area (DCIA). The research also explores the relationship between TIA, DCIA, and the effective impervious area (EIA), another important indicator of urban impervious cover. Using spatial programming, surface runoff was tracked, from originated to discharged locations, in nine urban catchments (ranging from 16 to 2035 ha) in Minnesota, USA, under various scenarios combining geospatial data types and resolutions. TIA fractions derived from low-resolution, nationally available imperviousness data were not statistically different than those from high-resolution land cover (LC) data modified by “unshading” to remove tree canopies overlaying impervious surfaces. Comparison of DCIA and EIA values indicated that modified LC is preferable for accurate DCIA delineation in ungauged catchments. EIA was 65 % of DCIA under the assumption that all rooftops were connected to sewer systems, while it exceeded DCIA by 34 % assuming all rooftops were unconnected, underscoring the importance of rooftop connectivity information in urban areas. The assumption of 0 % to 40 % rooftop connectivity resulted in comparable DCIA and EIA values in the study catchments. Using roads as the runoff collection system (instead of sewer inlets) did not significantly alter DCIA values at 5 % level, when unshaded LC was used. Given the absence of observed runoff data in most urban areas, DCIA serves as an appropriate imperviousness indicator for determining runoff in ungauged urban watersheds. However, achieving accurate DCIA delineation necessitates high-resolution LC data, emphasizing the need for relevant urban management organizations to develop such data.

Estimating GDP by Fusing Nighttime Light and Land Cover Data

Accurate information on gross domestic product (GDP) is essential for better understanding the dynamics of regional economies and urbanization processes. Satellite based nighttime light datasets can well track GDP in urban areas, however, they are difficult to be used in suburban, rural and sparsely populated areas. Thus, this study explored the potential of GlobeLand30 and relief degree for improving the ability of VIIRS Nighttime Light data of GDP estimation. Firstly, we calculated the Moran’s Index (Moran’s I) to measure spatial auto-correlation of GDP. At provincial level, Moran’s I Index of GDP is 0.14, Z value is 2.29. While at the prefecture city level, it is 0.11 and 14.54, respectively. Then, we compared the results derived from geographically weighted regression (GWR) and OLS models (i.e., R2, root mean square error, corrected Akaike information criteria and residuals). Both models suggest that land cover information can significantly improve GDP estimation performance, and total nighttime light (TNL) is the most important economic indicator for estimating GDP. The coefficients of the GWR model for TNL at the provincial and prefecture levels are 1.75 and 1.19, respectively, which are significantly larger than the coefficients for other factors such as land cover and terrain undulation. In addition, the GWR model performed better than OLS model in GDP estimation at both provincial and prefecture levels, and prefecture-level models can better depict the spatial variation in detail. In provincial-level models, GWR could account for 93% of economic development, while OLS could only reflect 82%. Likewise, in prefecture-level models, R2 of GWR model improved almost 50% compared with that of OLS model.

Assessing receptivity to malaria using case surveillance and forest data in a near-elimination setting in northeast Thailand

BackgroundThailand aimed to eliminate malaria by 2024, and as such is planning for future prevention of re-establishment in malaria free provinces. Understanding the receptivity of local areas to malaria allows the appropriate targeting of interventions. Current approaches to assessing receptivity involve collecting entomological data. Forest coverage is known to be associated with malaria risk, as an environment conducive to both vector breeding and high-risk human behaviours.MethodsGeolocated, anonymized, individual-level surveillance data from 2011 to 2021 from the Thai Division of Vector-Borne Disease (DVBD) was used to calculate incidence and estimated Rc at village level. Forest cover was calculated using raster maps of tree crown cover density and year of forest loss from the publicly available Hansen dataset. Incidence and forest cover were compared graphically and using Spearman’s rho. The current foci classification system was applied to data from the last 5 years (2017–2021) and forest cover for 2021 compared between the classifications. A simple risk score was developed to identify villages with high receptivity.ResultsThere was a non-linear decrease in annual cases by 96.6% (1061 to 36) across the two provinces from 2011 to 2021. Indigenous Annual Parasite Index (API) and approximated Rc were higher in villages in highly forested subdistricts, and with higher forest cover within 5 km. Forest cover was also higher in malaria foci which consistently reported malaria cases each year than those which did not. An Rc > 1 was only reported in villages in subdistricts with > 25% forest cover. When applying a simple risk score using forest cover and recent case history, the classifications were comparable to those of the risk stratification system currently used by the DVBD.ConclusionsThere was a positive association between forest coverage around a village and indigenous malaria cases. Most local transmission was observed in the heavily forested subdistricts on the international borders with Laos and Cambodia, which are where the most receptive villages are located. These areas are at greater risk of importation of malaria due to population mobility and forest-going activities. Combining forest cover and recent case surveillance data with measures of vulnerability may be useful for prediction of malaria recurrence risk.

Ensemble Machine Learning on the Fusion of Sentinel Time Series Imagery with High-Resolution Orthoimagery for Improved Land Use/Land Cover Mapping

In the United States, several land use and land cover (LULC) data sets are available based on satellite data, but these data sets often fail to accurately represent features on the ground. Alternatively, detailed mapping of heterogeneous landscapes for informed decision-making is possible using high spatial resolution orthoimagery from the National Agricultural Imagery Program (NAIP). However, large-area mapping at this resolution remains challenging due to radiometric differences among scenes, landscape heterogeneity, and computational limitations. Various machine learning (ML) techniques have shown promise in improving LULC maps. The primary purposes of this study were to evaluate bagging (Random Forest, RF), boosting (Gradient Boosting Machines [GBM] and extreme gradient boosting [XGB]), and stacking ensemble ML models. We used these techniques on a time series of Sentinel 2A data and NAIP orthoimagery to create a LULC map of a portion of Irion and Tom Green counties in Texas (USA). We created several spectral indices, structural variables, and geometry-based variables, reducing the dimensionality of features generated on Sentinel and NAIP data. We then compared accuracy based on random cross-validation without accounting for spatial autocorrelation and target-oriented cross-validation accounting for spatial structures of the training data set. Comparison of random and target-oriented cross-validation results showed that autocorrelation in the training data offered overestimation ranging from 2% to 3.5%. The XGB-boosted stacking ensemble on-base learners (RF, XGB, and GBM) improved model performance over individual base learners. We show that meta-learners are just as sensitive to overfitting as base models, as these algorithms are not designed to account for spatial information. Finally, we show that the fusion of Sentinel 2A data with NAIP data improves land use/land cover classification using geographic object-based image analysis.

Towards a gapless 1 km fractional snow cover via a data fusion framework

Accurate quantification of snow cover facilitates the prediction of snowmelt runoff, the assessment of freshwater availability, and the analysis of Earth’s energy balance. Existing fractional snow cover (FSC) data, however, often suffer from limitations such as spatial and temporal gaps, compromised accuracy, and coarse spatial resolution. These limitations significantly hinder the ability to monitor snow cover dynamics effectively. To address these formidable challenges, this study introduces a novel data fusion framework specifically designed to generate high-resolution (1 km) daily FSC estimation across vast regions like North America, regardless of weather conditions. It achieved this by effectively integrating the complementary spatiotemporal characteristics of both coarse- and fine-resolution FSC data through a multi-stage processing pipeline. This pipeline incorporates innovative strategies for bias correction, gap filling, and consideration of dynamic characteristics of snow cover, ultimately leading to high accuracy and high spatiotemporal completeness in the fused FSC data. The accuracy of the fused FSC data was thoroughly evaluated over the study period (September 2015 to May 2016), demonstrating excellent consistency with independent datasets, including Landsat-derived FSC (total 24 scenes; RMSE=6.8–18.9 %) and ground-based snow observations (14,350 stations). Notably, the fused data outperforms the widely used Interactive Multi-sensor Snow and Ice Mapping System (IMS) daily snow cover extent data in overall accuracy (0.92 vs. 0.91), F1_score (0.86 vs. 0.83), and Kappa coefficient (0.80 vs. 0.77). Furthermore, the fused FSC data exhibits superior performance in accurately capturing the intricate daily snow cover dynamics compared to IMS data, as confirmed by superior agreement with ground-based observations in four snow-cover phenology metrics. In conclusion, the proposed data fusion framework offers a significant advancement in snow cover monitoring by generating high-accuracy, spatiotemporally complete daily FSC maps that effectively capture the spatial and temporal variability of snow cover. These FSC datasets hold substantial value for climate projections, hydrological studies, and water management at both global and regional scales.

Regional climate response to land cover change in tropical West Africa: a numerical sensitivity experiment with ESA land cover data and advanced WRF-Hydro

Regional climate response to land cover change in tropical West Africa: a numerical sensitivity experiment with ESA land cover data and advanced WRF-Hydro

Identification of Urban Expansion Driving Factors using CA-Markov model: a Case Study Demak and Jepara Regency, Indonesia

Land cover changes are a critical concern in a developing country where rapid urbanization and population growth intersect with environmental dynamics. Understanding the driving forces behind these changes is essential for sustainable development and effective land management. This study analyzes land cover changes in Demak-Jepara regency, Indonesia, over a 20-year period using Landsat data. The objective is to identify the dominant factors driving the increase in built-up areas, considering both natural and human-induced factors. Factors such as road distance, existing build area, and natural features are evaluated. Using ArcGIS and Idrisi Selva's Land Change Modeler, land cover data is processed, and Cellular Automata-Markov analysis is conducted. The study considers a cell size of 30 x 30 meters and a time step of 5 years from 2001 to 2009. Transition persistence analysis identifies significant factors, validated using 2020 land cover data through AUC (Area Under the Curve) and ROC (Relative Operating Characteristic) analysis. The combination of natural and human-induced factors (scenario-C) shows the highest AUC value of 0.9406, indicating better conformity with 2020 land cover. Dominant factors in scenario C include roads, existing built-up areas, river order, and slope gradient. Results reveal that road development and proximity to existing settlements are the primary drivers of land cover changes. Natural factors like river order and slope gradient have a lesser impact, while the coastline has minimal influence. These findings highlight the importance of considering both natural and human-induced factors in land use planning. They provide valuable insights for policymakers and land managers in making informed decisions for sustainable development and land use strategies. Keywords: Land Cover Changes; Driving Factors; Natural Factors; CA-Markov; Sustainable Development

Optimizing land use for climate mitigation using nature based solution (NBS) strategy: a study on afforestation potential and carbon sequestration in Rajasthan, India

Rajasthan faces significant environmental challenges, including the pressing need for effective climate change mitigation strategies. Recognizing afforestation as a vital tool in this endeavor, this study leverages the latest in remote sensing and geospatial analysis to map out the state's afforestation potential. The goal is to assess land suitability across Rajasthan for afforestation projects and evaluate the potential carbon sequestration capabilities of different tree species. This aims to inform sustainable land management strategies that can contribute to climate change mitigation. By integrating satellite imagery, land cover data, terrain analysis, and vegetation indices, the study evaluates environmental factors such as slope, soil moisture, and vegetation health to identify areas optimal for tree planting. A carbon sequestration model was also developed to estimate sequestration rates based on species-specific growth patterns. Findings indicate that approximately 40% of Rajasthan is suitable for afforestation, with potential carbon sequestration rates ranging from 2 to 8 tons per hectare per year. Species such as Azadirachta indica (Neem) and Prosopis cineraria (Khejri) are identified as particularly effective for short-term sequestration, while Phyllanthus emblica (Amla) and Ziziphus mauritiana (Ber) are better suited for long-term carbon capture. This research highlights the importance of targeted afforestation using suitable tree species as a nature-based solution (NBS) for climate change mitigation in Rajasthan. The study offers a data-driven approach to enhancing ecosystem resilience and supports decision-making for climate adaptation in arid regions, highlighting the significant potential of Rajasthan's land for carbon sequestration through afforestation.

Surviving the cold: Assessing long-term outcomes among Korean CKD patients exposed to low perceived temperature during winter

Perceived temperature (PT), which encompasses meteorological factors such as wind speed, cloud cover, and humidity, reflects the actual effect of temperature on the human body. However, limited data exist on the health implications of prolonged exposure to low temperatures during winter in individuals with chronic kidney disease (CKD). We investigated the association between winter PT and long-term outcomes among CKD patients. A total of 32,870 CKD patients from three tertiary hospitals in Seoul were enrolled in this retrospective study (2001–2018). PT was calculated using Staiger's equation, integrating temperature data from 29 automated weather stations across Seoul, along with dew point temperature, wind velocity, and cloud cover data. Kriging interpolation was utilized to estimate PT values at the patients' locations. Overall mortality and major adverse cardiovascular events (MACEs) were assessed using a time-varying Cox proportional hazards model. Additionally, the Cox regression model evaluated PT corresponding to temperature thresholds for cold surge watches or warnings. Over a median follow-up of 6.14 ± 3.96 years, 6147 deaths (18.7%) were recorded. We found that as the average or minimum PT and Ta decreased by 1 °C, the risk of overall mortality significantly increased. In multivariable analyses, the hazard ratio (HR) for the average PT was 1.049 (95% confidence interval [CI] 1.028–1.071), and that for the minimum PT was 1.038 (CI 1.027–1.052). Furthermore, a cold surge warning at a PT of −25.63 °C indicated an HR of 1.837 (CI 1.764–1.914) and a C-index of 0.792. The increased risk of mortality was more pronounced in patients with low or middle socioeconomic statuses. For MACEs, lower average and minimum PT and Ta were associated with an increased risk, following a similar trend to overall mortality, although not all results reached statistical significance. These findings emphasize the importance of targeted public health policies to mitigate risks among vulnerable CKD patients.

Phytosociological study of a Grassland community of Baripada, Mayurbhanj district of Odisha, India: Conservation strategies for floristic diversity

A phytosociological study of the grassland community at Maharaja Purna Chandra Autonomous College (21 º 93’ N; 86 º 76' E) was conducted from July 2022 to July 2023. The study aimed to understand the floristic diversity and propose conservation strategies. A rigorous data collection methodology was implemented using 1 m × 1 m quadrat sampling, and the findings were analyzed and presented using a novel approach. Parameters such as occurrence (+) and non-occurrence (-) of species, frequency, density, abundance, basal area, basal cover, and Important Value Index (IVI) were recorded for 36 species (18 grasses and 18 non-grasses) throughout the research period. The study revealed that certain species, including Chrysopogon acciculatus and Evolvulus nummularius, exhibited high frequency, density, abundance and IVI throughout the research period. Conversely, Eragrostis unioloides and Sida cordifolia showed the lowest IVI during the study. Basal area and basal cover data indicated an inverse relationship between grasses and non-grasses. The total Relative Frequency (RF), Relative Density (RD), and Relative Dominance (RDo.) of the community were consistently around 100 for each month, and the total IVI values were approximately 300 each month. These results underscore the need for effective conservation strategies to preserve the unique floristic diversity of this grassland community. The remarkable phytosociological attributes of this experimental grassland community may be attributed to the topography, soil composition, and climatic conditions of the locality.

Wastewater discharges and urban land cover dominate urban hydrology signals across England and Wales

Urbanisation is an important driver of changes in streamflow. These changes are not uniform across catchments due to the diverse nature of water sources, storage, and pathways in urban river systems. While land cover data are typically used in urban hydrology analyses, other characteristics of urban systems (such as water management practices) are poorly quantified which means that urbanisation impacts on streamflow are often difficult to detect and quantify. Here, we assess urban impacts on streamflow dynamics for 711 catchments across England and Wales. We use the CAMELS-GB dataset, which is a large-sample hydrology dataset containing hydro-meteorological timeseries and catchment attributes characterising climate, geology, water management practices and land cover. We quantify urban impacts on a wide range of streamflow dynamics (flow magnitudes, variability, frequency, and duration) using random forest models. We demonstrate that wastewater discharges from sewage treatment plants and urban land cover dominate urban hydrology signals across England and Wales. Wastewater discharges increase low flows and reduce flashiness in urban catchments. In contrast, urban land cover increases flashiness and frequency of medium and high flow events. We highlight the need to move beyond land cover metrics and include other features of urban river systems in hydrological analyses to quantify current and future drivers of urban streamflow.

Evaluating the effects of changes in land use and assessing the value of ecosystem services in the Cisadane Watershed, Banten Province, Indonesia

Analyzing ecosystem service values (ESV) is crucial for achieving sustainable development. The main objective of this study was to assess the ecosystem services of the Cisadane watershed in Indonesia, with specific goals: (i) examining the spatiotemporal dynamics of ESV using multi-year land use and land cover (LULC) data from 2000 to 2021, (ii) exploring trade-offs and synergies among various ecosystem services, and (iii) investigating the sensitivity of ESV to changes in LULC. The results unveiled a significant decrease in forested areas (21.2%) and rice fields (10.2%), leading to a decline in ESV of $196.37 billion (33.17%) from 2010 to 2021. Throughout the period from 2000 to 2021, interactions between ESV were mainly synergistic. Projected from the baseline year (2021), the decline in ESV is expected to persist, ranging from $24.78 billion to $124.28 million by 2030 and from $45.78 billion to $124.28 million by 2050. The total estimated ecosystem values exhibited an inelastic response in terms of ecosystem value coefficients. The study also emphasizes an inelastic response in total estimated ESV coefficient concerning ecosystem value coefficients. These findings underscore the urgent need for targeted conservation efforts and sustainable land management practices to mitigate the further decline in ecosystem services and safeguard the long-term well-being of the Cisadane watershed and its inhabitants.

Climate dynamics and the effect of topography on snow cover variation in the Indus-Ganges-Brahmaputra river basins

The river basins of the Indus-Ganges-Brahmaputra (IGB) are characterized by rapid population growth, increased water demand, and rapid snow/ice melting, all of which combine to make these catchments especially vulnerable to climate change. Here, we present a comprehensive dataset (2003−2020) for precipitation, temperature and snow cover area (SCA) to investigate the spatio-temporal variability of important climate factors in relation to elevation and across three major sub-basins of the IGB. Improved MODIS (Moderate Resolution Imaging Spectro-radiometer) Terra and Aqua snow cover data are used to estimate regional snow cover, while Climate Hazards Group InfraRed Precipitation with Station Data version 2.0 (CHIRPS-V2.0) is deployed for precipitation, and MODIS Land Surface Temperature (LST) data are used to estimate temperature. The analysis reveals significant changes in precipitation, LST and snow cover throughout the IGB region together with a strong degree of spatial coherence between SCA and regional surface topography (R2 = 0.82).Maximum changes were observed in the Brahmaputra basin at mean annual precipitation (13.9 mm/year) and reduced SCA (155.4 km2/year) over the period studied. Similar trends are also evident, albeit with relatively lower absolute values in the other two basins, whereby the Indus basin evidences a significant reduction in annual SCA, averaging 82.2 km2/year with increased precipitation (3.12 mm/year) and LST. This investigation further highlights the relative influence of climate change factors and regional human activities in determining high-elevation changes in SCA. Most of the middle elevation regions of the Ganges experienced a significant reduction in SCA, with human impact contributing 82% of the observed variation, while climate factors are more prominent in the Indus and Brahmaputra basins. Understanding the dynamics of snow cover and its regional influencing factors is crucial to the more accurate evaluation of water resources and to defining appropriate mitigation and adaptation strategies for the coming decades.

Monitoring Sustainable Development Goal Indicator 15.3.1 on Land Degradation Using SEPAL: Examples, Challenges and Prospects

A third of the world’s ecosystems are considered degraded, and there is an urgent need for protection and restoration to make the planet healthier. The Sustainable Development Goals (SDGs) target 15.3 aims at protecting and restoring the terrestrial ecosystem to achieve a land degradation-neutral world by 2030. Land restoration through inclusive and productive growth is indispensable to promote sustainable development by fostering climate change-resistant, poverty-alleviating, and environmentally protective economic growth. The SDG Indicator 15.3.1 is used to measure progress towards a land degradation-neutral world. Earth observation datasets are the primary data sources for deriving the three sub-indicators of indicator 15.3.1. It requires selecting, querying, and processing a substantial historical archive of data. To reduce the complexities, make the calculation user-friendly, and adapt it to in-country applications, a module on the FAO’s SEPAL platform has been developed in compliance with the UNCCD Good Practice Guidance (GPG v2) to derive the necessary statistics and maps for monitoring and reporting land degradation. The module uses satellite data from Landsat, Sentinel 2, and MODIS sensors for primary productivity assessment, along with other datasets enabling high-resolution to large-scale assessment of land degradation. The use of an in-country land cover transition matrix along with in-country land cover data enables a more accurate assessment of land cover changes over time. Four different case studies from Bangladesh, Nigeria, Uruguay, and Angola are presented to highlight the prospect and challenges of monitoring land degradation using various datasets, including LCML-based national land cover legend and land cover data.

Land cover effects on regenerants density and richness in restoration treatments in the Atlantic Rainforest biome

There is good evidence that the results of a restoration program depend largely on the landscape context. In restoration projects, it is crucial to consider the previous land uses and covers and landscape configuration as they have a significant impact on the entire process, especially seed dispersal and natural regeneration of sites. Thus, the objective of this work was to verify the effect of landscape (i.e., land use) on restoration success of total planting, seed sowing and natural regeneration sites in the Atlantic Forest biome, southeast Brazil. The methodology employed was based on demonstration units of restoration. The focus was on regenerant richness and abundance indicators, along with land use and land cover data (class area) from 2010, 2015, and 2020, obtained from the MapBiomas Project. Generalized linear models and correlation analysis were used for the study. We hypothesize that a landscape with more forest and natural regeneration cover (mosaic of agriculture and pasture) would positively affect regenerants in restored sites, which we only observed for natural regeneration treatment sites. In active restoration treatment sites, mosaic of agriculture and pasture was negatively and farming was positively associated to regenerants. We also found greater density and richness of regenerants in natural regeneration treatment sites than in seed sowing and seedling planting. The influence of the surrounding landscape, particularly mosaic, played a crucial role in this success. In addition, land use cover history, as we observed in classes in the last five and ten years, did explain regenerants in studied sites. Thus, results show that the characterization of landscape context and previous land-use history is essential to understand the limitations to succession and define cost-effective restoration strategies.