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On account of the uneven level of ecological and environmental protection in the countries along the Belt and Road, infrastructure construction and investment cooperation around natural resource-intensive industries such as steel, energy, and nonferrous metals can easily induce serious ecological problems. It is necessary and practical to provide enterprises with timely and effective ecological circumstantial information of Belt and Road and pilot the establishment of the Belt and Road infrastructure construction activities with appropriate ecological risk prevention and control mechanisms. In this study, we researched the ecological risk level distribution of the countries along the Belt and Road by using ESA global land cover data and ecological risk index mode of land use. Kuwait, one of the most representative countries along the Belt and Road, was selected to analyze its spatial distribution of ecological risk by using ordinary Kriging interpolation. The results show several aspects. There existed high-ranking ecological risks in the following countries: Saudi Arabia in the Middle East, Turkmenistan in the Central Asian, Mongolia in East Asia and Pakistan in South Asia and so on; the medium ecological risks appear in the following countries: Ukraine in Eastern Europe, India, Bangladesh in South Asian, Kazakhstan and Kyrgyzstan in Central Asia the low ecological risk countries mainly include Russia, most Eastern European countries represented by Poland, and most Southeast Asian countries represented by Thailand. In the case, Kuwait's risk level is high in urban areas, medium in desert areas, and low along the Persian Gulf, showing a layered and striped distribution. Most areas in Kuwait are medium ecological risk zones with a relatively small area of high ecological risk and low ecological risk areas clustered in the eastern part of Kuwait. To effectively lower the ecological risk levels of countries along B&R, we suggest establishing B&R ecological risk prevention and control mechanisms while ecological conservation technologies are also worth noting.

Land use and land cover (LULC) changes strongly affect local hydrology and sediment yields. The current study focused on a basin in the Brazilian Amazon and had the following three objectives: (1) to perform an effective diagnosis of flow and sediment yield, (2) to evaluate the impacts of LULC changes over the last 40 years on the hydro-sedimentological variables, and (3) to investigate the impacts of the possible trends or breaking points in the flow, surface runoff, and sediment yield series. The Soil and Water Assessment Tool (SWAT) model validation showed that the simulated results were consistent with the data measured in the dynamic reproduction of flow seasonality. Furthermore, changes in LULC altered surface runoff, sediment yield, and flow according to the Mann-Kendall and Pettitt non-parametric tests. It was also observed that the sub-basins in which pastureland is predominant are more susceptible to increased surface runoff and sediment yield. According to trend and homogeneity tests, these sub-basins had break points in the time series of these variables. On the other hand, in the sub-basins whose land cover is predominantly forest, the time series is homogeneous and trendless. The current study highlighted the impacts that 40 years of LULC changes in an Amazonian basin had on hydro-sedimentological variables, which, in turn, can play an important role in changing the hydrological cycle, consequently these changes can also impact diverse human activities in the region, such as agribusiness, livestock, energy production, food security, and public water supply. Thus, it is concluded that the SWAT model is a good estimator of hydro-sedimentological processes in Amazonian basins and can be used by decision makers in the management of water and environmental resources.

Change detection is the amount of changes that can guide to more concrete understandings into essential method concerning land cover, land usage and ecological variations. This paper deals with an intelligent methodology to optimize the solution out of the solution space that improves the efficiency of the change detection process. To support the theme, an integrated semi-supervised method is designed with a focus on image fusion, semi supervised clustering and binary swarm based optimization. A new approach of handling fusion using sparse coding is referred to expand the amount of information. Using the extracted information, change detection process is carried out by a constrained clustering technique to provide a solution reflecting the level of changes occurred in the region of investigation. To improve the accuracy by proposing a global optimum solution, still the result is refined through binary swarm based optimization process and hence the results are accelerated towards the increased level of accuracy followed by which the change map is reconstructed to show case changes prominently. To determine the accurateness of the proposed methodology quantitative and qualitative analysis has been done with different datasets. The proposed method has been evaluated with existing techniques such as k-means, AKM, FCM, ECKM and ASCC to show the efficiency and proved to be the preeminent change detection methodology compared to the state-of-art methods.

Forest cover requires large scale and frequent monitoring as an indicator of biodiversity and progress towards United Nations and World Bank Sustainable Development Goal 15. Measuring change in forest cover over time is an essential task in order to track and preserve quality habitats for species around the world. Due to the prohibitive expense and impracticality of mass field data collection to monitor forest cover at regular intervals, satellite images are a key data source for monitoring forest cover globally. A challenge of working with satellite images is missing data due to clouds. Existing methods for interpolating the missing data based on past images, such as compositing, are effective for stable land cover but can be inaccurate for dynamic and substantially changing landscapes. Here we present an adaptation of our recent stochastic spatial random forest (SS‐RF) method, which combines observed data from a prior image and modelled estimates of the current image to produce interpolated land cover values and associated probabilities of those values. Results show our SS‐RF method accurately detected simulated land cover change under both clear felling (0.83 average overall accuracy) and tree thinning (0.85 average overall accuracy). Our method detected forest cover change substantially more accurately than compositing, offering 39% and 12% increases in average overall accuracy for clear felling and tree thinning simulations respectively. However, when natural fluctuation occurs and there is minimal change in land cover, compositing has equivalent or more accurate performance than our method. Overall we find that our SS‐RF method produces accurate estimates under a range of simulated forest clearing scenarios and has a more accurate and robust performance than compositing when modelling noticeably changing landscapes.

This paper aims to determine and classify the land cover and assess the impact of agriculture expansion on the land surface temperature (LST) and normalized difference vegetation index (NDVI) in El-Beheira Governorate, Egypt. To achieve these objectives, the multi-temporal Landsat 5 and 8 satellites images for nine different dates were obtained every five years during 1985–2020. Four land cover categorize of agriculture, urban, desert and water lands were distinguished and mapped using the supervised maximum likelihood classification technique in ArcGIS Desktop 10.6. The created land cover maps illustrate a significant expansion in agriculture from 4212 to 7523 km2 (nearly 473 km2 every five years). The desert area decreased from 5417 to 1817 km2 (about 514 km2 every five years) during 1985–2020. The latitudinal variation for LST and NDVI demonstrated that they have a strong negative correlation (R 91%) across all selected dates. The highest positive values for NDVI corresponded to the lowest values for LST over agricultural lands and reversed over desert areas. Where, the relative impacts of agriculture, desert, urban and water on LST (45.1, 50, 4.8 and 0.1% respectively) reveals that the vegetation and desert represents the most significant land cover categorize that determinantes the spatial and temporal distributions of NDVI and LST.

Methane (CH4) emissions from the boreal and arctic region are globally significant and highly sensitive to climate change. There is currently a wide range in estimates of high-latitude annual CH4 fluxes, where estimates based on land cover inventories and empirical CH4 flux data or process models (bottom-up approaches) generally are greater than atmospheric inversions (top-down approaches). A limitation of bottom-up approaches has been the lack of harmonization between inventories of site-level CH4 flux data and the land cover classes present in high-latitude spatial datasets. Here we present a comprehensive dataset of small-scale, surface CH4 flux data from 540 terrestrial sites (wetland and non-wetland) and 1247 aquatic sites (lakes and ponds), compiled from 189 studies. The Boreal–Arctic Wetland and Lake Methane Dataset (BAWLD-CH4) was constructed in parallel with a compatible land cover dataset, sharing the same land cover classes to enable refined bottom-up assessments. BAWLD-CH4 includes information on site-level CH4 fluxes but also on study design (measurement method, timing, and frequency) and site characteristics (vegetation, climate, hydrology, soil, and sediment types, permafrost conditions, lake size and depth, and our determination of land cover class). The different land cover classes had distinct CH4 fluxes, resulting from definitions that were either based on or co-varied with key environmental controls. Fluxes of CH4 from terrestrial ecosystems were primarily influenced by water table position, soil temperature, and vegetation composition, while CH4 fluxes from aquatic ecosystems were primarily influenced by water temperature, lake size, and lake genesis. Models could explain more of the between-site variability in CH4 fluxes for terrestrial than aquatic ecosystems, likely due to both less precise assessments of lake CH4 fluxes and fewer consistently reported lake site characteristics. Analysis of BAWLD-CH4 identified both land cover classes and regions within the boreal and arctic domain, where future studies should be focused, alongside methodological approaches. Overall, BAWLD-CH4 provides a comprehensive dataset of CH4 emissions from high-latitude ecosystems that are useful for identifying research opportunities, for comparison against new field data, and model parameterization or validation. BAWLD-CH4 can be downloaded from https://doi.org/10.18739/A2DN3ZX1R (Kuhn et al., 2021).

As a specialised branch of archaeology requiring specific field and laboratory methodologies, the contributions of archaeobotany have often been overlooked by the ecological research community. Developments in the fields of botany, chemistry, and ancient DNA analyses have greatly increased the potential for archaeobotany to contribute to topical questions relating to the Anthropocene and landscape transformations. We review the role of archaeobotany in identifying and describing past arable land use. Analytical techniques are illustrated with examples at both local and regional scales, demonstrating how archaeobotany can provide unique details of the wide array of past subsistence and land-use strategies. These data and their potential should be better recognised as important information that could underpin models seeking to evaluate or predict the effects of socioenvironmental interactions.

Modelling future change to land use and land cover is done as part of many local and global scenario environmental assessments. Nevertheless, there are still considerable challenges related to simulating land-use responses to climate change. Mostly, climate change is considered by changing the temperature and precipitation, affecting the spatial distribution and productivity of future land use and land cover as result of differential changes in growing conditions. Other climate change effects, such as changes in the water resources needed to support future cropland expansion and intensification, are often neglected. In this study, we demonstrate how including different types of responses to climate change influences the simulation of future changes to land use and land cover, and land management. We study the influence of including different climate change effects in land system modeling step by step. The results show that land system models need to include numerous simultaneous climate change effects, particularly when looking at adaptation options such as implementing irrigation. Otherwise, there is a risk of biased impact estimates leading either to under- or overestimation of the consequences of land use change, including land degradation. Spatial land system models therefore need to be developed accounting for a multitude of climate change impacts, uncertainties related to climate data, and an assessment of the sensitivity of the outcomes toward the decisions of modellers on representing climate change impacts.

As a result of the development of cities and inclination towards urbanization, natural areas decreased while urban areas increased. In this respect, determination of impermeable surfaces is important for the problems covering; effects of urbanization on natural environment, global environmental variation, urban atmospheric process, human activities and the effects of urbanization on the environment.Remote sensing images are used to examine and classify land cover/uses. Traditional classification methods are mainly divided into supervised classification and unsupervised classification. The aim of this study is to classify land cover/use and to state temporal change using the Support Vector Machines (SVM) approach, which is a supervised classification method. Arnavutkoy district of Istanbul was chosen as the study area for land use and change detection analysis. Landsat 5/7/8 satellite images of Arnavutkoy district were obtained and SVM process was applied to obtain these images. Firstly, four classes were created for each image: urban areas, vegetation, bare soils and wetlands SVM was applied and accuracy analysis was performed to the images classes of which were created before. CAD software and GIS software were used for image processing. The classification accuracy for SVM was found to be 98.66%, 98.31%, 98.95%, 97.99%, 96.37%, 97.90% (from 1995 to 2019). In addition, ROC analysis was used for comparison of accuracy analysis. As a result of this study, land cover/use change of Arnavutkoy district in the last 20 years has been determined. The urban area of the district was 40.99 〖"km" 〗^"2" in 1995 and 93.76 〖"km" 〗^"2" in 2019. In addition, the impact of the Europe's largest airport on land cover / use has been examined. The results showed that the accuracy of using SVM to classify land use/cover is high. Therefore, it has been proposed that this algorithm is used as an optimal classifier for land use/cover maps.

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Over the past half-century, the risk of urban flooding in Dar es Salaam has increased due to changes in land cover coupled with climatic changes. This paper aimed to quantify the impacts of climate and land-cover changes on the magnitudes and frequencies of flood runoffs in urban Dar es Salaam, Tanzania. A calibrated and validated SWAT rainfall-runoff model was used to generate flood hydrographs for the period 1969–2050 using historical rainfall data and projected rainfall based on the CORDEX-Africa regional climate model. Results showed that climate change has a greater impact on change in peak flows than land-cover change when the two are treated separately in theory. It was observed that, in the past, the probability of occurrence of urban flooding in the study area was likely to be increased up to 1.5-fold by climate change relative to land-cover change. In the future, this figure is estimated to decrease to 1.1-fold. The coupled effects of climate and land-cover changes cause a much bigger impact on change in peak flows than any separate scenario; this scenario represents the actual scenario on the ground. From the combined effects of climate and land-cover changes, the magnitudes of mean peak flows were determined to increase between 34.4 and 58.6% in the future relative to the past. However, the change in peak flows from combined effects of climate and land-cover changes will decrease by 36.3% in the future relative to the past; owing to the lesser variations in climate and land-cover changes in the future compared with those of the past. HIGHLIGHTS Investigate temporal variability of urban flood runoffs from the impacts of climate and land-cover changes.; Compare past and future peak flow trend magnitudes based on CORDEX-Africa RCM under RCP4.5.; Investigate changes in the historical and future intensities of urban flood runoffs.; Illustrate the probabilistic impacts of climate and land-cover changes on the recurrence intervals of urban floods.

In this paper, the effect of land use and land cover and the impact of urbanisation on respirable particulate matter (RSPM), sulfur oxide (SO x ) and nitrogen oxide (NO x ) of the Hubli-Dharwad, a Tier II city in India, are correlated based on the trends in air quality observed from 2006 to 2013, population from 1990 to 2010, the number of vehicles between the periods of 2004 and 2013 and urbanisation between the periods of 1975 and 2009. It has been found that urbanisation has increased threefold from 92 km2 in 1975 to 271 km2 in 2009 and the corresponding decrease in agricultural area was from 368.22 to 123.43 km2. The RSPM in the study region is increasing at a rate of 8.9% per year. The study shows that vehicular pollutants are the major cause of air pollution, followed by industries, with the highest RSPM value of 128 μg/m3 at traffic junctions in the Hubli-Dharwad region in 2013. Based on the trend analysis, the air quality atlas predicted for 2030 shows that the RSPM level in the air will reach 150 μg/m3, well above the national ambient air quality standards, and will have serious consequences on human health if proper strategies are not undertaken. Owing to the unique geographical setting of Hubli-Dharwad, its future urbanisation will be in a narrow area, which may lead to a severe air pollution problem that needs immediate attention to provide a safe environment.

Despite several flood databases available in the United States, there is a benefit to combine and reconcile these diverse data sources into a comprehensive flood database with a unified common format and easy public access in order to facilitate flood-related research and applications. Typically, floods are reported by specialists or media according to their socioeconomic impacts. Recently, data-driven analysis can reconstruct flood events based on in situ and/or remote-sensing data. Lately, with the increasing engagement of citizen scientists, there is the potential to enhance flood reporting in near-real time. The central objective of this study is to integrate information from seven popular multi-sourced flood databases into a comprehensive flood database in the United States, made readily available to the public in a common data format. Natural language processing, geocoding, and harmonizing processing steps are undertaken to facilitate such development. In total, there are 698 507 flood records in the United States from 1900 to the present, which highlights the longest and most comprehensive recording of flooding across the country. The database features event locations, durations, date/times, socioeconomic impacts (e.g., fatalities and economic damages), and geographic information (e.g., elevation, slope, contributing area, and land cover types retrieved from ancillary data for given flood locations). Finally, this study utilizes the flood database to analyze flood seasonality within major basins and socioeconomic impacts over time. It is anticipated that thus far the most comprehensive yet unified database can support a variety of flood-related research, such as a validation resource for hydrologic or hydraulic simulations, hydroclimatic studies concerning spatiotemporal patterns of floods, and flood susceptibility analysis for vulnerable geophysical locations. The dataset is publicly available with the following DOI: https://doi.org/10.5281/zenodo.4547036 (Li, 2020).

Context While land use change is the main driver of biodiversity loss, most biodiversity assessments either ignore it or use a simple land cover representation. Land cover representations lack the representation of land use and landscape characteristics relevant to biodiversity modeling. Objectives We developed a comprehensive and high-resolution representation of European land systems on a 1-km2 grid integrating important land use and landscape characteristics. Methods Combining the recent data on land cover and land use intensities, we applied an expert-based hierarchical classification approach and identified land systems that are common in Europe and meaningful for studying biodiversity. We tested the benefits of using this map as compared to land cover information to predict the distribution of bird species having different vulnerability to landscape and land use change. Results Next to landscapes dominated by one land cover, mosaic landscapes cover 14.5% of European terrestrial surface. When using the land system map, species distribution models demonstrate substantially higher predictive ability (up to 19% higher) as compared to models based on land cover maps. Our map consistently contributes more to the spatial distribution of the tested species than the use of land cover data (3.9 to 39.1% higher). Conclusions A land systems classification including essential aspects of landscape and land management into a consistent classification can improve upon traditional land cover maps in large-scale biodiversity assessment. The classification balances data availability at continental scale with vital information needs for various ecological studies.

Soil moisture (SM) estimation using coarse resolution satellite data, like Scatsat-1 scatterometer data (2 km spatial resolution), is still challenging due to various land cover classes present within a single pixel. To estimate SM from coarse resolution data, there is a necessity to study the contribution of different land covers to the total backscatter signal of the single pixel. The estimation of SM is very much dependent on the accuracy of the backscatter signal from the soil component. Therefore, this paper proposes a modified water cloud model to compute the soil-backscattering signal by correcting the backscatter contribution due to urban and vegetation fraction cover. The backscattering signal from the soil surface is then used to retrieve SM. The retrieved SM is compared with the ground truth SM using the error metrics such as root mean square error (RMSE), mean bias (b), correlation coefficient (r), and average relative error (ARE). The proposed model is tested in three conditions: first, when only vegetation correction is applied, RMSE, mean bias, r, and ARE are found to be 0.086 m3/m3, 0.70 m3/m3, 0.534, and 0.24, respectively, and results show an overestimation in SM. Second, when both vegetation and urban correction are applied using the actual fraction, the accuracy is improved with an RMSE, mean bias, and ARE of 0.071 m3/m3 , 0.056 m3/m3, and 0.20, respectively. However, in the third condition, when the urban fraction cover is considered twice the actual value, the RMSE and mean bias are found to be 0.22 m3/m3 and −0.10 m3/m3, respectively, and results show an underestimation in SM.

To understand the spatial–temporal pattern of climate and land cover (CLC) change effects on hydrology, we used three land cover change (LCC) coupled scenarios to estimate the changes in streamflow metrics in the Clackamas River Watershed in Oregon for the 2050s (2040–2069) and the 2080s (2070–2099). Coupled scenarios, which were split into individual and combined simulations such as climate change (CC), LCC, CLC change, and daily streamflow were simulated in the Soil and Water Assessment Tool. The interannual variability of streamflow was higher in the lower urbanized area than the upper forested region. The watershed runoff was projected to be more sensitive to CC than LCC. Under the CLC scenario, the top 10% peak flow and the 7-day low flow are expected to increase (2–19%) and decrease (+9 to −20 cm s), respectively, in both future periods. The center timing of runoff in the year is projected to shift 2–3 weeks earlier in response to warming temperature and more winter precipitation falling as rain. High streamflow variability in our findings suggests that uncertainties can stem from both climate models and hydrologic model parameters, calling for more adaptive water resource management in the watershed. HIGHLIGHTS Tightly coupled CLC change scenarios were used to model flow in the Soil and Water Assessment Tool.; Snow-influenced, forested watershed is more sensitive to CC than LCC.; Hydrologic variability is higher in the urban, agricultural part than the forested part.; Top 10% flow is projected to increase, while low flow is projected to decline.; Warming will shift the center timing of flow volume earlier from mid-May to late-April.

Impervious surface mapping is of great importance in urban studies. Impervious surfaces are major components of urban infrastructures and their expansion represents urban development. These surfaces mainly include built-up areas and streets; they are composed of various materials and found in diverse sizes and shapes. Impervious surface detection is challenging due to the confusion of these surfaces with other land cover classes. These confusions are not constant over different seasons, as seasonality affects the target’s responses. This study particularly focused on the seasonal effect on impervious surface detection using Sentinel-1 and Sentinel-2 images to find the optimum season. The study area is the city of Sanandaj, in the west of Iran. All processes have been executed on the Google Earth Engine as it provides a platform to access and process the satellite images. To exclude the effect of the classification algorithm on the obtained results, three commonly used classifiers have been compared; i.e., maximum likelihood, support vector machine, and neural network. The results show that spring is the best season to delineate impervious surfaces from remaining land covers, while the use of winter images does not provide acceptable results. Sentinel-2 results outperform Sentinel-1. Variation in topography and high sensitivity of SAR responses to moisture and volume structure hinder the application of Sentinel-1 images in the heterogeneous urban area. The built-up class has higher producer accuracy as compared to the street class. There was considerable confusion between the street and bare soil classes in both Sentinel-1 and Sentinel-2 images.

Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally.

Insect pollinators provide an important ecosystem service by improving agricultural productivity. However, their populations have been declining in recent years due to excessive use of synthetic pesticides, climate and land use/land cover (LULC) changes. Climate and LULC changes have resulted in land fragmentation and consequently pollinator habitat loss. To conserve pollinators, there is a need for sustainable agricultural practices such as integrated pest and pollinator management (IPPM), which is a holistic landscape management approach that minimizes pesticides use while conserving pollinator abundance and diversity. This study aimed to use earth observation (EO) data to characterize landscape dynamics in terms of vegetation productivity to guide the implementation of IPPM interventions in an avocado production system in Murang'a (Kenya). Specifically, we utilized Sentinel-2 (S-2)-derived normalized difference vegetation index (NDVI) as a proxy for vegetation productivity to assess IPPM implementation sites. The NDVI was calculated using multi-date S-2 data acquired during the dry and wet seasons and categorized into three vegetation productivity classes - low, medium, and high - using a K-means unsupervised clustering method. We also collected socio-economic baseline data from 410 farmers with a focus on their perception to implement one of four avocado pest management and pollination options: (1) IPPM, (2) integrated pest management (IPM), (3) pollinator supplementation (P), and (4) no intervention (control). The three landscape vegetation productivity classes were then linked with the four farmer preferences with regards to the implementation options. Criteria based on the distances among the sites for implementing the different four options were set for farmer selection and the experiment was replicated three times in each vegetation productivity class (i.e. in total 12 farmers in each class). The results showed that the K-means method was successful in characterizing the landscape vegetation productivity with an overall accuracy of 86.2%. One the other hand, we successfully selected the 36 (12 in each of the 3 vegetation productivity classes) out of 410 farmers who met our distance-based criteria and participated in the implementation of one of the four technology options (i.e. IPPM, IPM, P, and control). In conclusion, NDVI proved to be a vital proxy for assessing the landscape dynamics as it provided a robust view of vegetation productivity patterns, which enabled a well-representative distribution of the four pest management and pollination options across the landscape. Overall, the study shows the utility of integrating EO and socio-economic data in selecting sites for implementing agro-technologies at a landscape scale.

Context Afforestation is one of the major drivers of land cover change in the world. In spite of its multiple applications, forest generation has been fostered during the last century to achieve few specific objectives such as timber production or catchment erosion protection. However, new afforestation programs are required to meet multiple objectives such as carbon sequestration, reducing environmental risks and enhance biodiversity conservation to reach sustainability. Objectives Assessing the general efficiency of past large-scale afforestation programs on the provision of ecosystem services (ES) and other socio-ecological attributes. We also determined whether this efficiency depended on the type of ecosystem used for comparison. Methods We carried out a semi-quantitative meta-analysis from 75 studies analyzing the effects of 128 afforestation actions developed in one of the EU countries with larger areas of afforested land during the twentieth century. Results Spanish afforestation policy improved timber provision and carbon sequestration. However, our results show that afforestation programs did not enhance regulating ES when compared to natural land plots, but only when compared to highly degraded areas prior to restoration actions. Conclusions Large-scale afforestations have difficulties in assuring the provisioning of regulating ES and the conservation of biodiversity when only productivity criteria are prioritized. Our results suggest the need for an afforestation agenda that embraces more comprehensive landscape management approaches that allow reducing undesired ES trade-offs while getting multiple objectives from different ecosystems.

Using an unmanned aerial vehicle (UAV) paired with image semantic segmentation to classify land cover within natural vegetation can promote the development of forest and grassland field. Semantic segmentation normally excels in medical and building classification, but its usefulness in mixed forest-grassland ecosystems in semi-arid to semi-humid climates is unknown. This study proposes a new semantic segmentation network of LResU-net in which residual convolution unit (RCU) and loop convolution unit (LCU) are added to the U-net framework to classify images of different land covers generated by UAV high resolution. The selected model enhanced classification accuracy by increasing gradient mapping via RCU and modifying the size of convolution layers via LCU as well as reducing convolution kernels. To achieve this objective, a group of orthophotos were taken at an altitude of 260 m for testing in a natural forest-grassland ecosystem of Keyouqianqi, Inner Mongolia, China, and compared the results with those of three other network models (U-net, ResU-net and LU-net). The results show that both the highest kappa coefficient (0.86) and the highest overall accuracy (93.7%) resulted from LResU-net, and the value of most land covers provided by the producer’s and user’s accuracy generated in LResU-net exceeded 0.85. The pixel-area ratio approach was used to calculate the real areas of 10 different land covers where grasslands were 67.3%. The analysis of the effect of RCU and LCU on the model training performance indicates that the time of each epoch was shortened from U-net (358 s) to LResU-net (282 s). In addition, in order to classify areas that are not distinguishable, unclassified areas were defined and their impact on classification. LResU-net generated significantly more accurate results than the other three models and was regarded as the most appropriate approach to classify land cover in mixed forest-grassland ecosystems.

Context Open data policies and accessible computation platforms allow efficient extraction of information from remote sensing data for landscape research. Landscape ecology is strongly influenced by remote sensing, and the value of fine resolution temporal information for characterising landscapes is under-explored. Objectives We highlighted the importance of temporal information extracted from remote sensing data gathered over a period of time for landscape research. A case study approach was used to show how time-series information can benefit the mapping of land cover and landscape elements in a heterogeneous landscape dominated by agricultural land use. Methods We constructed four composite images of the study area, each incorporating different levels of temporal information. The images either represent a single date or summarise temporal information into single values as the median of spectral bands or vegetation indices. Random forest and k-means clustering methods were used to classify the images. Results The overall accuracy of the landscape classifications ranged between 0.3 to 0.8, increasing substantially when including temporal information, for mapping both land cover and small landscape elements. Using temporal information and a RF-based classification it was generally possible to map crop and forest types. The size of landscape elements was overestimated, although the clustering model predicted elements close to their true size and complexity. Conclusions The approach highlights the importance of temporal resolution for landscape ecology research. The easy-to-implement methodology offers an opportunity for landscape ecologists to increase the accuracy of landscape mapping and identify ecologically important landscape elements that might otherwise be missed.

Urbanization leads to the construction of various urban infrastructures in the city area for residency, transportation, industry, and other purposes, which causes major land use change. Consequently, it substantially affects Land Surface Temperature (LST) by unbalancing the surface energy budget. Higher LST in city areas decreases human thermal comfort for the city dwellers and affects the urban environment and ecosystem. Therefore, a comprehensive investigation is needed to evaluate the impact of land use change on the LST. Remote Sensing (RS) and Geographic Information System (GIS) techniques were used for the detailed investigation. RS data for the years 1993, 2007 and 2020 during summer (March–May) in Dhaka city were used to prepare land cover maps, analyze LST, generate hazard maps and relate the land cover change with LST by using GIS. The results show that the built-up area in Dhaka city increased by 67% from 1993 to 2020 by replacing lowland mainly, followed by vegetation, bare soil and water bodies. LSTs found in the study area were ranged from 23.26 to 39.94 °C, 23.69 to 43.35 °C and 24.44 to 44.58 °C for the years 1993, 2007 and 2020, respectively. The increases of spatially distributed maximum and mean LST were found 4.62 °C and 6.43 °C, respectively, for the study period of 27 years while the change in minimum LST was not substantial. LST increased by around 0.24 °C per year and human thermal discomfort shifted from moderate to strong heat stress for the total study period due to the increase of built-up and bare lands. This study also shows that normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were negatively correlated with LST while normalized difference built-up Index (NDBI) and normalized difference built-up Index (NDBAI) were positively correlated with LST. The methodology developed in this study can be adapted to other cities around the globe.

Low-lying coastal cities are widely acknowledged as the most densely populated places of urban settlement; they are also more vulnerable to risks resulting from intensive land use and land cover change, human activities, global climate change, and the rising sea levels. This study aims to predict how urban growth is affected by sea level rise (SLR) in the Australian context. We develop an urban cellular automata model incorporating urban planning policies as potential drivers or constraints of urban growth under different SLR scenarios and adaption strategies. Drawing on data capturing the socioeconomic and environmental factors in South East Queensland, Australia, our model is positioned to address one core research question: how does SLR affect future urban growth and human resettlement? Results show that urban growth in coastal regions varies depending on the extent to which the sea level rises and is affected by a combination of factors relating to urban planning and human adaptation strategies. Our study demonstrates the complexity of urban growth in coastal regions and the nuanced outcomes under different adaptation strategies in the context of rising sea levels.

Background Fragmentation and deforestation are one of the greatest threats to forests, and these processes are of even more concern in the tropics, where the seasonal dry forest is possibly one of the most threatened ecosystems with the least remaining surface area. Methods The deforestation and fragmentation patterns that had occurred in Ecuadorian seasonal dry forests between 1990 and 2018 were verified, while geographic information systems and land cover shapes provided by the Ecuadorian Ministry of the Environment were employed to classify and evaluate three types of seasonal dry forests: deciduous, semi-deciduous, and transition. The study area was tessellated into 10 km2 hexagons, in which six fragmentation parameters were measured: number of patches, mean patch size, median patch size, total edge, edge density and reticular fragmentation index (RFI). The RFI was also measured both outside and inside protected natural areas (unprotected, national protected areas and protected forest). Moreover, the areas with the best and worst conservation status, connectivity and risk of disappearance values were identified by means of a Getis-Ord Gi* statistical analysis. Results The deforestation of seasonal dry forests affected 27.04% of the original surface area still remaining in 1990, with an annual deforestation rate of − 1.12% between 1990 and 2018. The RFI has increased by 11.61% as a result of the fact that small fragments of forest have tended to disappear, while the large fragments have been fragmented into smaller ones. The semi-deciduous forest had the highest levels of fragmentation in 2018. The three categories of protection had significantly different levels of fragmentation, with lower RFI values in national protected areas and greater values in protected forests. Conclusions The seasonal dry forest is fragmenting, deforesting and disappearing in some areas. An increased protection and conservation of the Ecuadorian seasonal dry forest is, therefore, necessary owing to the fact that not all protection measures have been effective.

Environmental science is increasingly reliant on remotely sensed observations of the Earth's surface and atmosphere. Observations from polar-orbiting satellites have long supported investigations on land cover change, ecosystem productivity, hydrology, climate, the impacts of disturbance, and more and are critical for extrapolating (upscaling) ground-based measurements to larger areas. However, the limited temporal frequency at which polar-orbiting satellites observe the Earth limits our understanding of rapidly evolving ecosystem processes, especially in areas with frequent cloud cover. Geostationary satellites have observed the Earth's surface and atmosphere at high temporal frequency for decades, and their imagers now have spectral resolutions in the visible and near-infrared regions that are comparable to commonly used polar-orbiting sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imaging Radiometer Suite (VIIRS), or Landsat. These advances extend applications of geostationary Earth observations from weather monitoring to multiple disciplines in ecology and environmental science. We review a number of existing applications that use data from geostationary platforms and present upcoming opportunities for observing key ecosystem properties using high-frequency observations from the Advanced Baseline Imagers (ABI) on the Geostationary Operational Environmental Satellites (GOES), which routinely observe the Western Hemisphere every 5–15 min. Many of the existing applications in environmental science from ABI are focused on estimating land surface temperature, solar radiation, evapotranspiration, and biomass burning emissions along with detecting rapid drought development and wildfire. Ongoing work in estimating vegetation properties and phenology from other geostationary platforms demonstrates the potential to expand ABI observations to estimate vegetation greenness, moisture, and productivity at a high temporal frequency across the Western Hemisphere. Finally, we present emerging opportunities to address the relatively coarse resolution of ABI observations through multisensor fusion to resolve landscape heterogeneity and to leverage observations from ABI to study the carbon cycle and ecosystem function at unprecedented temporal frequency.

In this study, we tested the impact of a revised set of soil, vegetation and land cover parameters on the performance of three different state-of-the-art land surface models (LSMs) within the NASA Land Information System (LIS). The impact of this revision was tested over the South American Dry Chaco, an ecoregion characterized by deforestation and forest degradation since the 1980s. Most large-scale LSMs may lack the ability to correctly represent the ongoing deforestation processes in this region, because most LSMs use climatological vegetation indices and static land cover information. The default LIS parameters were revised with (i) improved soil parameters, (ii) satellite-based interannually varying vegetation indices (leaf area index and green vegetation fraction) instead of climatological vegetation indices, and (iii) yearly land cover information instead of static land cover. A relative comparison in terms of water budget components and “efficiency space” for various baseline and revised experiments showed that large regional and long-term differences in the simulated water budget partitioning relate to different LSM structures, whereas smaller local differences resulted from updated soil, vegetation and land cover parameters. Furthermore, the different LSM structures redistributed water differently in response to these parameter updates. A time-series comparison of the simulations to independent satellite-based estimates of evapotranspiration and brightness temperature (Tb) showed that no LSM setup significantly outperformed another for the entire region and that not all LSM simulations improved with updated parameter values. However, the revised soil parameters generally reduced the bias between simulated surface soil moisture and pixel-scale in situ observations and the bias between simulated Tb and regional Soil Moisture Ocean Salinity (SMOS) observations. Our results suggest that the different hydrological responses of various LSMs to vegetation changes may need further attention to gain benefits from vegetation data assimilation.

Context Urban-rural gradients are useful tools when examining the influence of human disturbances on ecological, social and coupled systems, yet the most commonly used gradient definitions are based on single broad measures such as housing density or percent forest cover that fail to capture landscape patterns important for conservation. Objectives We present an approach to defining urban–rural gradients that integrates multiple landscape pattern metrics related to ecosystem processes important for natural resources and wildlife sustainability. Methods We develop a set of land cover composition and configuration metrics and then use them as inputs to a cluster analysis process that, in addition to grouping towns with similar attributes, identifies exemplar towns for each group. We compare the outcome of the cluster-based urban-rural gradient typology to outcomes for four commonly-used rule-based typologies and discuss implications for resource management and conservation. Results The resulting cluster-based typology defines five town types (urban, suburban, exurban, rural, and agricultural) and notably identifies a bifurcation along the gradient distinguishing among rural forested and agricultural towns. Landscape patterns (e.g., core and islet forests) influence where individual towns fall along the gradient. Designations of town type differ substantially among the five different typologies, particularly along the middle of the gradient. Conclusions Understanding where a town occurs along the urban-rural gradient could aid local decision-makers in prioritizing and balancing between development and conservation scenarios. Variations in outcomes among the different urban-rural gradient typologies raise concerns that broad-measure classifications do not adequately account for important landscape patterns. We suggest future urban-rural gradient studies utilize more robust classification approaches.

Habitat evaluation is essential for managing wildlife populations and formulating conservation policies. With the rise of innovative powerful statistical techniques in partnership with Remote Sensing, GIS and GPS techniques, spatially explicit species distribution modeling (SDM) has rapidly grown in conservation biology. These models can help us to study habitat suitability at the scale of the species range, and are particularly useful for examining the overlapping habitat between sympatric species. Species presence points collected through field GPS observations, in conjunction with 13 different topographic, vegetation related, anthropogenic, and bioclimatic variables, as well as a land cover map with seven classification categories created by support vector machine (SVM) were used to implement Maxent and GARP ecological niche models. With the resulting ecological niche models, the suitable habitat for asiatic black bear (Ursus thibetanus) and red panda (Ailurus fulgens) in Nepal Makalu Barun National Park (MBNP) was predicted. All of the predictor variables were extracted from freely available remote sensing and publicly shared government data resources. The modeled results were validated by using an independent dataset. Analysis of the regularized training gain showed that the three most important environmental variables for habitat suitability were distance to settlement, elevation, and mean annual temperature. The habitat suitability modeling accuracy, characterized by the mean area under curve, was moderate for both species when GARP was used (0.791 for black bear and 0.786 for red panda), but was moderate for black bear (0.857), and high for red panda (0.920) when Maxent was used. The suitable habitat estimated by Maxent for black bear and red panda was 716 km2 and 343 km2 respectively, while the suitable area determined by GARP was 1074 km2 and 714 km2 respectively. Maxent predicted that the overlapping area was 83% of the red panda habitat and 40% of the black bear habitat, while GARP estimated 88% of the red panda habitat and 58% of the black bear habitat overlapped. The results of land cover exhibited that barren land covered the highest percentage of area in MBNP (36.0%) followed by forest (32.6%). Of the suitable habitat, both models indicated forest as the most preferred land cover for both species (63.7% for black bear and 61.6% for red panda from Maxent; 59.9% black bear and 58.8% for red panda from GARP). Maxent outperformed GARP in terms of habitat suitability modeling. The black bear showed higher habitat selectivity than red panda. We suggest that proper management should be given to the overlapping habitats in the buffer zone. For remote and inaccessible regions, the proposed methods are promising tools for wildlife management and conservation, deserving further popularization.

Weather-related power outages affect millions of utility customers every year. Predicting storm outages with lead times of up to five days could help utilities to allocate crews and resources and devise cost-effective restoration plans that meet the strict time and efficiency requirements imposed by regulatory authorities. In this study, we construct a numerical experiment to evaluate how weather parameter uncertainty, based on weather forecasts with one to five days of lead time, propagates into outage prediction error. We apply a machine-learning-based outage prediction model on storm-caused outage events that occurred between 2016 and 2019 in the northeastern United States. The model predictions, fed by weather analysis and other environmental parameters including land cover, tree canopy, vegetation characteristics, and utility infrastructure variables exhibited a mean absolute percentage error of 38%, Nash–Sutcliffe efficiency of 0.54, and normalized centered root mean square error of 68%. Our numerical experiment demonstrated that uncertainties of precipitation and wind-gust variables play a significant role in the outage prediction uncertainty while sustained wind and temperature parameters play a less important role. We showed that, while the overall weather forecast uncertainty increases gradually with lead time, the corresponding outage prediction uncertainty exhibited a lower dependence on lead times up to 3 days and a stepwise increase in the four- and five-day lead times.

Land-use and land-cover (LULC) change as a result of rapid urban expansion cause land surface temperature (LST) variations. The study aims to analyze urban LULC change and its impact on the seasonal, spatial and temporal Surface Urban Heat Islands (SUHI) of Addis Ababa city and its surrounding from 1987 to 2019 using Landsat images. The result indicates that the Impervious Surface (IS) of Addis Ababa city and the surroundings have expanded from 81.49 km2 in 1987 to 591.85 km2 in 2019 with a 6.2% rate of change. On the other hand, vegetation cover which has a high thermal cooling effect has been degraded from 217.66 km2 in 1987 to 157.8 km2 in 2019. The spatial pattern of LST increased from northern highlands towards southern lowlands. The mean temperature for January and February 1987 was 26.22 °C and 27.76 °C, respectively. On 25 January 2002, the study area exhibited the mean LST of 28.25 °C, whereas on 26 February 2002, the mean temperature was increased to 31.26 °C. On 16 January 2019, the mean LST was 31.14 °C, whereas on 1 February declined to 30.62 °C. The study area exhibited a high mean LST on 21 March 2019 which was 36.1 °C, whereas on 15 October 2019 the mean LST was 25.41 °C. The study shows that very high LST exhibited on fallow land (27.78, 30.39 and 33.38 °C), crop (26.5, 28.66 and 30.83 °C), grassland (26.52, 28.53 and 31.15 °C) and IS (26.58, 28.38 and 31.39 °C) while low LST found on vegetation cover (22.76, 21.64 and 24.44 °C in 1987, 2002 and 2019, respectively). The mean LST has a positive correlation with fraction of IS (R2 = 0.5152, 0.5855, 0.7184), CL (R2 = 0.716, 0.6294, 0.7089), FL (R2 = 0.6373, 0.6138, 0.8667) and GL (R2 = 0.6513, 0.6073, 0.6442) while negative with VC (R2 = 0.6295, 0.5601, 0.6357 in 1987, 2002 and 2019, respectively). The mean LST that exhibited in Z1 and Z2 was 25.47 and 26.91 °C, 26.06 and 28.88 °C, and 29.43 and 32.33 °C in 1987, 2002, and 2019, respectively. The IS declined when moving from the center to the peripheral area and the mean LST increased towards the rural area along urban–rural zones (URZs). In the north–south direction along URZs, the minimum and maximum mean LST in January increased from 15.83 °C in 1987 to 18.57 °C in 2019 and 33.64 °C in 1987 to 35.58 °C in 2019, respectively. Besides, the minimum and maximum mean LST for February in 1987 and 2019 was 17.57 °C and 18.97 °C and 32.82 °C and 35.21 °C, respectively. The minimum mean temperature also increased from 18.24 °C to 23.4 °C, and maximum mean LST from 32.84 °C to 41.9 °C from October to March 2019. Along east–west direction, the minimum and maximum mean LST in January was found 18.48 °C and 30.8 °C in 1987 while it was increased to 23.66 °C and 35.69 °C in 2019, respectively. The minimum and maximum mean LST in February was also increased from 20.17 to 23.57 °C and 30.81 to 34.91 °C from 1987 to 2019, respectively. Along northeast–southwest direction, the minimum mean LST in January 1987 was 16.17 °C and increased to 18.16 °C in 2019, whereas the maximum mean temperature increased from 32.1 °C in 1987 to 36.06 °C in 2019. The minimum mean LST in February 1987 was 19.43 °C and decreased to 18.5 ℃ in 2019, whereas the maximum value raised from 32.07 °C in 1987 to 36.85 °C in 2019. The pattern of LST decreased when moving to URZ60 to URZ90 in the northwest direction of the city center and increased city center to URZ47 and city center to URZ165 in the southeast direction. The study revealed that SUHII was highly concentrated in the urban area than the peripheral area. Therefore, to reduce SUHI and to have a sound environment, vegetation cover with dense trees canopy and greenery areas covered with grasses and trees are very important for the city and the surrounding.

The study sought to assess the impacts of land use and land cover (LULC) changes on two wetland systems (Makuleke and Nylsvley Nature Reserve) in the Limpopo Transfrontier River Basin (LTRB) in Sou...

The discontinuous permafrost zone is undergoing rapid transformation as a result of unprecedented permafrost thaw brought on by circumpolar climate warming. Rapid warming over recent decades has significantly decreased the area underlain by permafrost in peatland complexes. It has catalysed extensive landscape transitions in the Taiga Plains of northwestern Canada, transforming forest-dominated landscapes to those that are wetland dominated. However, the advanced stages of this landscape transition, and the hydrological and thermal mechanisms and feedbacks governing these environments, are unclear. This study explores the current trajectory of land cover change across a 300 000 km2 region of northwestern Canada's discontinuous permafrost zone by presenting a north–south space-for-time substitution that capitalizes on the region's 600 km latitudinal span. We combine extensive geomatics data across the Taiga Plains with ground-based hydrometeorological measurements collected in the Scotty Creek basin, Northwest Territories, Canada, which is located in the medial latitudes of the Taiga Plains and is undergoing rapid landscape change. These data are used to inform a new conceptual framework of landscape evolution that accounts for the observed patterns of permafrost thaw-induced land cover change and provides a basis for predicting future changes. Permafrost thaw-induced changes in hydrology promote partial drainage and drying of collapse scar wetlands, leading to areas of afforestation forming treed wetlands without underlying permafrost. Across the north–south latitudinal gradient spanning the Taiga Plains, relatively undisturbed forested plateau–wetland complexes dominate the region's higher latitudes, forest–wetland patchwork are most prevalent at the medial latitudes, and forested peatlands are increasingly present across lower latitudes. This trend reflects the progression of wetland transition occurring locally in the plateau–wetland complexes of the Scotty Creek basin and informs our understanding of the anticipated trajectory of change in the discontinuous permafrost zone.

Spatio-temporal changes in land use land cover (LULC) have been relevant factors in causing the changes in Urban Heat Island (UHI) pattern across rural and urban areas all over the world. Studies conducted have shown that the relation between LULC on scale of the UHI can be an important factor assessing the condition not only for a country but for environment of a city also. Over the years it is reflected in health of vegetation and urbanization pattern of cities. As the thermal remote sensing has been evolved, the measurement of the temperature through satellite products has become possible. Thermal data derived through remote sensing gives us birds-eye-view to see how the thermal data varies in the entire city. In this study such relations are shown over Ahmedabad city of India for the period of 2007 to 2020 using Landsat series satellite data. Land Surface Temperature (LST) is calculated using Google Earth Engine Platform Surface Brightness Temperature for Landsat data and using Radiative Transfer Equation for Landsat data. LST is correlated with land use land cover mainly Built-up, Vegetation, Barren land, Water & Other and corresponding Land Use and Land Cover respectively, and it is found that LST is positively related with all indices except for Normalize Difference Vegetation Index (NDVI) with strong negative correlation and R 2 of 0.51.

Landslide hazard zonation plays an important role in safe and viable infrastructure development, urbanization, land use, and environmental planning. The Shafe and Baso catchments are found in the Gamo highland which has been highly degraded by erosion and landslides thereby affecting the lives of the local people. In recent decades, recurrent landslide incidences were frequently occurring in this Highland region of Ethiopia in almost every rainy season. This demands landslide hazard zonation in the study area in order to alleviate the problems associated with these landslides. The main objectives of this study are to identify the spatiotemporal landslide distribution of the area; evaluate the landslide influencing factors and prepare the landslide hazard map. In the present study, lithology, groundwater conditions, distance to faults, morphometric factors (slope, aspect and curvature), and land use/land cover were considered as landslide predisposing/influencing factors while precipitation was a triggering factor. All these factor maps and landslide inventory maps were integrated using ArcGIS 10.4 environment. For data analysis, the principle of logistic regression was applied in a statistical package for social sciences. The result from this statistical analysis showed that the landslide influencing factors like distance to fault, distance to stream, groundwater zones, lithological units and aspect have revealed the highest contribution to landslide occurrence as they showed greater than a unit odds ratio. The resulting landslide hazard map was divided into five classes: very low (13.48%), low (28.67%), moderate (31.62%), high (18%), and very high (8.2%) hazard zones which was then validated using the goodness of fit techniques and receiver operating characteristic curve with an accuracy of 85.4. The high and very high landslide hazard zones should be avoided from further infrastructure and settlement planning unless proper and cost-effective landslide mitigation measures are implemented.

Purpose The University of Michigan (U-M) is planning its course toward carbon neutrality. A key component in U-M carbon accounting is the calculation of carbon sinks via estimation of carbon storage and biosequestration on U-M landholdings. Here, this paper aims to compare multiple remote sensing methods across U-M natural lands and urban campuses to determine the accurate and efficient protocol for land assessment and ecosystem service valuation that other institutions may scale as relevant. Design/methodology/approach This paper tested three remote sensing methods to determine land use and land cover (LULC), namely, unsupervised classification, supervised classification and supervised classification incorporating delineated wetlands. Using confusion matrices, this paper tested remote sensing approaches to ground-truthed data, the paper obtained via field-based vegetation surveys across a subset of U-M landholdings. Findings In natural areas, supervised classification incorporating delineated wetlands was the most accurate and efficient approach. In urban settings, maps incorporating institutional knowledge and campus tree surveys better estimated LULC. Using LULC and literature-based carbon data, this paper estimated that U-M lands store 1.37–3.68 million metric tons of carbon and sequester 45,000–86,000 Mt CO2e/yr, valued at $2.2m–$4.3m annually ($50/metric ton, social cost of carbon). Originality/value This paper compared methods to identify an efficient and accurate remote sensing methodology to identify LULC and estimate carbon storage, biosequestration rates and economic values of ecosystem services provided.

Sloping farmlands dominate much of Vietnam’s northern borderlands with China. Here, ethnic minority farmers have relied on their traditional ecological knowledge for centuries to fashion sustainable semi-subsistence livelihoods as best they can. With a rapidly increasing agrarian transition, these farmers must now juggle the agro-ecological limits of their farmlands with new state agricultural policies, growing market integration, and increasing extreme weather events. Despite about 60 percent of Vietnam’s landmass comprising slopes greater than 15°, there is sparse information regarding how best to support sustainable livelihood approaches in such regions. Yet, an understanding of current crop choices, agricultural limits, and farmer decision-making processes in such locales is vital for relevant, slope-related policy suggestions to be formulated. In this paper, we take a mixed methods approach, combining land-use and land cover (LULC) change mapping with qualitative interviews and observations, to investigate the interactions among sloping lands, LULC change, and local livelihoods in a remote, mountainous commune in northern Vietnam’s borderlands. We analyze LULC maps for Bản Phố commune, Lao Cai province, which contains 13 ethnic minority Hmong villages and has a fairly typical upland topography with three-quarters of the land sloped over 15°. Focusing on three main findings from our LULC analysis we then determine the drivers and livelihood consequences of an increase of ‘shrubs’ on sloped land, specific pockets of conversion to ‘bare soils’, and an increase in particular urban areas. We find that state afforestation policies, lowland demand for ‘authentic upland alcohols’, and officials keen to raise the status of a nearby town, all factor into the challenges and opportunities farmers now face.

The understanding of land-use and land-cover change (LUCC) is crucial for the study of global climate change and the terrestrial ecological environment. Sandy land is an important component of the ...

Spatial–temporal changes of land surface parameters (land cover change, net primary production, and vegetation phenology) affect the characteristics of atmospheric dust. This phenomenon subsequently will influence the climate parameters (radiative forcing, temperature, cloud, and precipitation). This research uses time-series data (2005–2015) from Regional Climate Model (RegCM) dust module simulation, WRF-Chem, meteorological data, aerosol optical depth, soil moisture, MODIS-based vegetation phenology, and net primary production to investigate the impacts of land surface parameter change on dust emission and the influence of dust on radiative forcing, heat fluxes, cloud formation, and the amount of precipitation over Southwest Asia. As the findings suggest, the southern regions of the Arabian Peninsula, southeastern Iraq, southwestern Iran, northern Arabian Peninsula, and southeastern Iran are subject to the most potent regional concentration of atmospheric dust. Our results revealed: (i) 12% of the land cover classes in southeastern Iraq and southwestern Iran were transformed into barren lands from 2005 to 2015 and subsequently these areas experienced the highest rate of dust formation, (ii) land surface changes and their consequences for dust formation have affected dust radiative forcing up to 10%, (iii) net-radiative forcing of dust always induces a state of cooling temperatures at surface, (iv) longwave forcing of dust causes a rise in heating impacts at top-of-atmosphere (TOA), so, dust aerosols have a cooling effect at the surface and heating effect at TOA, and (v) dust seems to reinforce cloud cover and cloud perceptible water, while reducing precipitation over Southwest Asia.

Adopting a low spatial resolution remote sensing imagery to get an accurate estimation of Land Use Land Cover is a difficult task to perform. Image fusion plays a big role to map the Land Use Land Cover. Therefore, This study aims to find out a refining method for the Land Use Land Cover estimating using these steps; (1) applying a three pan-sharpening fusion approaches to combine panchromatic imagery that has high spatial resolution with multispectral imagery that has low spatial resolution, (2) employing five pixel-based classifier approaches on multispectral imagery and fused images; artificial neural net, support vector machine, parallelepiped, Mahalanobis distance and spectral angle mapper, (3) make a statistical comparison between image classification results. The Landsat-8 image was adopted for this research. There are twenty Land Use Land Cover thematic maps were generated in this study. A suitable and reliable Land Use Land Cover method was presented based on the most accurate results. The results validation was performed by adopting a confusion matrix method. A comparison made between the images classification results of multispectral imagery and all fused images levels. It proved the Land Use Land Cover map produced by Gram–Schmidt Pan-sharpening and classified by support vector machine method has the most accurate result among all other multispectral imagery and fused images that classified by the other classifiers, it has an overall accuracy about (99.85%) and a kappa coefficient of about (0.98). However, the spectral angle mapper algorithm has the lowest accuracy compared to all other adopted methods, with overall accuracy of 53.41% and the kappa coefficient of about 0.48. The proposed procedure is useful in the industry and academic side for estimating purposes. In addition, it is also a good tool for analysts and researchers, who could interest to extend the technique to employ different datasets and regions. Validerad;2021;Nivå 2;2021-12-03 (johcin)

Modeling of large rainfall events plays an important role in water resources and floodplain management. Rainfall is resulted from complex interactions between climate factors (air moisture, temperature, wind speed, etc.) and land surface (topography, soil, land cover, etc.). Therefore, deriving accurate areal rainfall is not only relied on atmospheric boundary conditions, but also on the reliability and availability of soils, topography, and vegetation data. Consequently, uncertainties in both atmospheric and land surface conditions contributes to rainfall model errors. In this study, a blended technique combining dynamical and statistical downscaling has been explored. The proposed downscaling approach uses input provided from three different global reanalysis data sets including ERA-Interim, ERA20C, and CFSR. These reanalysis atmospheric data are hybridly downscaled by means of the Weather Research and Forecasting (WRF) model, which is followed by the application of an artificial neural network (ANN) model to further downscale the WRF output to a finer resolution over the studied region. The proposed technique has been applied to the third largest river basin in Vietnam, the Sai Gon–Dong Nai Rivers Basin; and the calibration and validation show the simulation results agreed well with observation data. Results of this study suggest that the proposed approach can improve the accuracy of simulated data, as it merges model simulations with observations over the modeled region. Another highlight of this approach is inexpensive computational demand on both computation times and output storage.

Among the biogenic volatile organic compounds (BVOCs) emitted by plant foliage, isoprene is by far the most important in terms of both global emission and atmospheric impact. It is highly reactive in the air, and its degradation favours the generation of ozone (in the presence of NOx) and secondary organic aerosols. A critical aspect of BVOC emission modelling is the representation of land use and land cover (LULC). The current emission inventories are usually based on land cover maps that are either modelled and dynamic or satellite-based and static. In this study, we use the state-of-the-art Model of Emissions of Gases and Aerosols from Nature (MEGAN) model coupled with the canopy model MOHYCAN (Model for Hydrocarbon emissions by the CANopy) to generate and evaluate emission inventories relying on satellite-based LULC maps at annual time steps. To this purpose, we first intercompare the distribution and evolution (2001–2016) of tree coverage from three global satellite-based datasets, MODerate resolution Imaging Spectroradiometer (MODIS), ESA Climate Change Initiative Land Cover (ESA CCI-LC), and the Global Forest Watch (GFW), and from national inventories. Substantial differences are found between the datasets; e.g. the global areal coverage of trees ranges from 30 to 50×106 km2, with trends spanning from −0.26 to +0.03 % yr−1 between 2001 and 2016. At the national level, the increasing trends in forest cover reported by some national inventories (in particular for the US) are contradicted by all remotely sensed datasets. To a great extent, these discrepancies stem from the plurality of definitions of forest used. According to some local censuses, clear cut areas and seedling or young trees are classified as forest, while satellite-based mappings of trees rely on a minimum height. Three inventories of isoprene emissions are generated, differing only in their LULC datasets used as input: (i) the static distribution of the stand-alone version of MEGAN, (ii) the time-dependent MODIS land cover dataset, and (iii) the MODIS dataset modified to match the tree cover distribution from the GFW database. The mean annual isoprene emissions (350–520 Tg yr−1) span a wide range due to differences in tree distributions, especially in isoprene-rich regions. The impact of LULC changes is a mitigating effect ranging from 0.04 to 0.33 % yr−1 on the positive trends (0.94 % yr−1) mainly driven by temperature and solar radiation. This study highlights the uncertainty in spatial distributions of and temporal variability in isoprene associated with remotely sensed LULC datasets. The interannual variability in the emissions is evaluated against spaceborne observations of formaldehyde (HCHO), a major isoprene oxidation product, through simulations using the global chemistry transport model (CTM) IMAGESv2. A high correlation (R > 0.8) is found between the observed and simulated interannual variability in HCHO columns in most forested regions. The implementation of LULC change has little impact on this correlation due to the dominance of meteorology as a driver of short-term interannual variability. Nevertheless, the simulation accounting for the large tree cover declines of the GFW database over several regions, notably Indonesia and Mato Grosso in Brazil, provides the best agreement with the HCHO column trends observed by the Ozone Monitoring Instrument (OMI). Overall, our study indicates that the continuous tree cover fields at fine resolution provided by the GFW database are our preferred choice for constraining LULC (in combination with discrete LULC maps such as those of MODIS) in biogenic isoprene emission models.

The need for conserving water rises as the resource becomes scarcer. The most essential step in order to manage this resource is to estimate watershed runoff. Runoff and rainfall are the main two sources that maintain ground water and recharge it. Hydrologists in the Kurdistan Region face the challenge of unavailability of data (most watersheds in the area are ungauged) or inaccurate data. The surface runoff of the study area was simulated via soil conservation service-curve number (SCS-CN) technique integrated into geographical information system (GIS). Surface runoff and curve number maps were made using the GIS created and processed data from land use/land cover (LU/LC), daily rainfall, hydrologic soil group and slope thematic maps. The results showed that the urbanization of the Sulaymaniyah watershed has increased the impermeability of the land by 40.9% for the period from 1999 to 2019. The runoff depth was 40.2% higher in 2019 as compared to 1999 due to the increase of the impermeable surface area. Research has also revealed a growth in built-up areas with a decrease of coverage in vegetation, resulting in a greater depth of surface runoff in urban catchment areas.

Estimating future temporal patterns of Surface Urban Heat Islands (SUHIs) on multiple time scales is an ongoing research endeavor. Among these time scales, estimation of next-day SUHIs is of special significance to urban residents, yet we currently lack a simple but efficient approach for making such estimations. In the present study, we propose a statistical strategy for estimating next-day nighttime SUHIs, based on incorporating various SUHI controls into a support vector machine regression (SVR) model. The majority of both the surface controls (including factors related to land cover and solar radiation) and meteorological controls (including temperature fluctuations, relative humidity, accumulated precipitation, wind speed, aerosol optical depth, and soil moisture) that have previously been found to account for daily SUHI variations were used as estimators, and we provide estimations for both the overall SUHI intensity (SUHII) and pixel-by-pixel Gaussian-based LSTs over 59 Chinese megacities. For the overall SUHII, the mean absolute error (MAE) is 0.67 K on average, and the mean absolute percentage error (MAPE) is no more than 25% for more than 90% of the cities. For the pixel-by-pixel LSTs, the associated MAE is less than 2.0 K in most scenarios. In addition, the contribution from each selected estimator to SUHII estimation is assessed comprehensively. Among all the estimators, the contribution from relative humidity is the greatest, followed by rural surface temperature and surface air temperature. Moreover, for nearly 78% of the cities, the estimators related to day-to-day SUHI variations make a larger contribution than those related to intra-annual SUHI variations. We conclude that our simple yet effective statistical approach for estimating next-day SUHIs can potentially help urban residents to better adapt to urban heat stress.

Large fractions of human-altered lands are working landscapes where people and nature interact to balance social, economic, and ecological needs. Achieving these sustainability goals requires tracking human footprints and landscape disturbance at fine scales over time—an effort facilitated by remote sensing but still under development. Here, we report a satellite time-series analysis approach to detecting fine-scale human disturbances in an Ohio watershed dominated by forests and pastures but with diverse small-scale industrial activities such as hydraulic fracturing (HF) and surface mining. We leveraged Google Earth Engine to stack decades of Landsat images and explored the effectiveness of a fuzzy change detection algorithm called the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST) to capture fine-scale disturbances. BEAST is an ensemble method, capable of estimating changepoints probabilistically and identifying sub-pixel disturbances. We found the algorithm can successfully capture the patterns and timings of small-scale disturbances, such as grazing, agriculture management, coal mining, HF, and right-of-ways for gas and power lines, many of which were not captured in the annual land cover maps from Cropland Data Layers—one of the most widely used classification-based land dynamics products in the US. For example, BEAST could detect the initial HF wellpad construction within 60 days of the registered drilling dates on 88.2% of the sites. The wellpad footprints were small, disturbing only 0.24% of the watershed in area, which was dwarfed by other activities (e.g., right-of-ways of utility transmission lines). Together, these known activities have disturbed 9.7% of the watershed from the year 2000 to 2017 with evergeen forests being the most affected land cover. This study provides empirical evidence on the effectiveness and reliability of BEAST for changepoint detection as well as its capability to detect disturbances from satellite images at sub-pixel levels and also documents the value of Google Earth Engine and satellite time-series imaging for monitoring human activities in complex working landscapes.

The study of land cover and use changes is important for sustainability, given that they are part of the global changes affecting our environment. In order to understand the mechanisms determining them, land cover and use changes must be studied based on their associated transitional dynamics. The present research aims to look at the most recent land cover and use changes from Romania, chosen as an example for post-socialists transitions. The methodology was based on applying geo-spatial analyses to data on land cover and use changes and natural protected areas. The results indicate that the most prominent transitional dynamics are deforestations and abandonment of cropland, rooted in the post-socialist property restitution. Furthermore, the study identified transitional dynamics indicating the human pressure within the natural protected areas, including urbanization, development of agriculture, and deforestation. Although subject to limitations bound to the use of CORINE data, the findings are important for understanding the environmental impact of socio-economic drivers, and provide additional evidence for the fact that emerging economies tend to sacrifice the environment during the economic crises.

The Middle East is an area characterized by strong gradients in meteorological conditions and varied land cover. As a result, some of the hottest places in the world as well as very cold areas can be found in this region. The objective of this paper is to quantitatively assess land surface temperature (LST) variations in the countries of the Middle East to estimate the trends and determine whether relationships suggesting the impact of climate change can be observed. Spatiotemporal variability of LST was investigated through the analysis of MODIS Terra LST images (MOD11A2) from 2001 to 2018. The LST differences in both daytime series and seasonal and international means were assessed. The analyses showed that in spring (MAM), about 20,000 km2 of the study area has an LST higher than 50 °C, whereas 3700 km2 has LST lower than 0 °C. In summer (JJA), about 1 million km2 has LST higher than 50 °C. In the fall (SON), about 5 million km2 of the study area has a land surface temperature higher than 30 °C, with about 11 km2 hotter than 50 °C, whereas 320 km2 has LST lower than 0 °C. In winter (DJF), the hottest countries are Yemen, Oman, and Saudi Arabia, with 44.7, 41.8, and 39.8 °C, respectively, whereas the lowest LST values were recorded in Turkey, Iran, and Iraq, with −19.5, −18.1, and −10.5 °C, respectively. An upward trend in the minimum and a downward trend in the maximum value of the winter LST suggest that winters in the Middle East countries such as Iran, Israel, and Jordan have become milder during the considered period. Negative trends in the spring LST in Bahrain and Oman and the summer LST in Bahrain and Qatar suggest that these seasons in those countries became colder during the study period.

In this paper, we present the Catchments Attributes for Brazil (CABra), which is a large-sample dataset for Brazilian catchments that includes long-term data (30 years) for 735 catchments in eight main catchment attribute classes (climate, streamflow, groundwater, geology, soil, topography, land cover, and hydrologic disturbance). We have collected and synthesized data from multiple sources (ground stations, remote sensing, and gridded datasets). To prepare the dataset, we delineated all the catchments using the Multi-Error-Removed Improved-Terrain Digital Elevation Model (MERIT DEM) and the coordinates of the streamflow stations provided by the Brazilian Water Agency, where only the stations with 30 years (1980–2010) of data and less than 10 % of missing records were included. Catchment areas range from 9 to 4 800 000 km2, and the mean daily streamflow varies from 0.02 to 9 mm d−1. Several signatures and indices were calculated based on the climate and streamflow data. Additionally, our dataset includes boundary shapefiles, geographic coordinates, and drainage area for each catchment, aside from more than 100 attributes within the attribute classes. The collection and processing methods are discussed, along with the limitations for each of our multiple data sources. CABra intends to improve the hydrology-related data collection in Brazil and pave the way for a better understanding of different hydrologic drivers related to climate, landscape, and hydrology, which is particularly important in Brazil, having continental-scale river basins and widely heterogeneous landscape characteristics. In addition to benefitting catchment hydrology investigations, CABra will expand the exploration of novel hydrologic hypotheses and thereby advance our understanding of Brazilian catchments' behavior. The dataset is freely available at https://doi.org/10.5281/zenodo.4070146 and https://thecabradataset.shinyapps.io/CABra/ (last access: 7 June 2021).

The type of land use and land cover plays a decisive role in land surface temperature (LST). As cities are composed of varied covers, including vegetation, built-up areas, buildings, roads and areas without vegetation, understanding LST patterns in complex urban spaces is becoming increasingly important. The present study investigated the relationship between LST and albedo, NDVI, NDWI, NDBI and NDBaI in the period between 1994 and 2017. Images from Thematic Mapper (TM) and Thermal Infrared Sensor (TIRS) onboard the Landsat 5 and 8 satellites, respectively, were used in the study. The images were processed, resampled (spatial resolution of 120 m) in the environment of the QGIS 3.0 software and, finally, centroids were extracted resulting in a total of 1252 points. A classical regression (CR) model was applied to the variables, followed by spatial autoregressive (SARM) and spatial error (SEM) models, and the results were compared using accuracy indices. The results showed that the highest correlation coefficient was found between albedo and NDBaI (r = 0.88). The relationship between albedo and LST (r = 0.7) was also positive and significant at р < 0.05. The global Moran's I index showed spatial dependence and non-stationarity of the LST (I = 0.44). The SEM presented the best accuracy metrics (AIC = 3307.15 and R2 = 0.65) for the metropolitan region of Belém, explaining considerably more variations in the relationship between explanatory factors and LST when compared to conventional CR models. Resumo O tipo e uso cobertura do solo desempenham papel decisivo para a temperatura da superfície continental (LST). Como as cidades são compostas por coberturas variadas, incluindo vegetação, áreas construídas, prédios, estradas e áreas desprovidas de vegetação, compreender os padrões da LST no complexo espaço urbano se faz cada vez mais importante. O presente estudo investigou a relação da LST com o albedo, NDVI, NDWI, NDBI e NDBaI no período de 1994 e 2017. Foram utilizadas imagens dos sensores Thematic Mapper (TM) e Thermal Infrared Sensor (TIRS) a bordo dos satélites Landsat 5 e 8, respectivamente. As imagens foram processadas, reamostradas (resolução espacial de 120 m) no ambiente do software QGIS 3.0 e, por fim, foram extraídos centroides com um total de 1252 pontos. O modelo de regressão clássica (RC) foi aplicado às variáveis seguidas por modelos espaciais autoregressivos e de erro espacial (MEAR e MEE) e os resultados foram comparados a partir de índices de acurácia. Os resultados mostram que o maior coeficiente de correlação existe foi verificado entre albedo e NDBaI (r = 0,88). A relação entre o albedo e a LST, com r = 0,7, também é positiva e significativa ao nível de (р < 0,05). A partir do índice I de Moran Global verificou-se a dependência espacial e não estacionariedade da LST (I = 0,44). O modelo MEE apresentou as melhores métricas de acurácia (AIC = 3307,15 e R2 = 0,65) explicando consideravelmente mais variações na relação dos fatores explicativos e da LST para a região metropolitana de Belém, quando comparados aos modelos convencionais de RC.

The spatial extent and distribution of soil erosion due to land cover and climate change is the central theme of our research. We have provide first spatial distribution wise soil erosion map at small scale, considering empirical observations and challenging the previous work in terms of average annual soil erosion in the entire country. The map reveals that the importance of climate and land cover change has been played an important role in controlling fertile soil erosion at finer scale, as well as the overwhelming effect of highly dense population induced activities across the country. Based on the projected soil loss estimation, study have estimated that nearly 9.88% land surface eroded top soil at 80 Mg ha-1 y-1, and the areas of land surface become 13.14% in 2100 year unless human activities and climate change controlled.