Main Land Cover Types Research Articles

Vegetation canopy height shapes bats’ occupancy: a remote sensing approach

ABSTRACT Anthropogenic activities have significantly altered land cover on a global scale. These changes often have a negative effect on biodiversity limiting the distribution of species. The extent of the effect on species’ distribution depends on the landscape composition and configuration at a local and landscape level. To better understand this effect on a large scale, we evaluated how land cover and vegetation structure shape bat species’ occurrence while considering species’ imperfect detection. We hypothesize that intensification of anthropogenic activities in agriculture, for example, reduces heterogeneity of land cover and vegetation structure, and thereby, limits bat occurrence. To investigate this, we conducted acoustic bat sampling across 59 locations in southern Portugal, each with three spatial replicates. We derived fine-scale vegetation structural metrics by combining spaceborne LiDAR (GEDI) and synthetic aperture radar data (Sentinel-1 and ALOS/PALSAR-2). Additionally, we included land cover metrics and high-resolution climate data from CHELSA. Our findings revealed an important relationship between bat species’ occupancy and vegetation structure, particularly with vegetation canopy height. Moreover, forest and shrubland proportions were the main land cover types influencing bat species responses. All species’ best-ranking occupancy models included at least one climatic variable (temperature, humidity, or potential evapotranspiration), demonstrating the importance of climate when predicting bat distribution. Our acoustic surveys had a species’ detection probability varying from 0.19 to 0.86, and it was influenced by night conditions. These findings underscore the importance of modeling imperfect detection, especially for highly vagile and elusive organisms like bats. Our results demonstrate the effectiveness of using vegetation and landscape metrics derived from high-resolution remote sensing data to model species distribution in the context of biodiversity monitoring and conservation.

Assessment of the Requirements of Natural Resources Using the Community Around Khow Noi-Khow Pradoo Non-Hunting Area of Phisanulok Province, Thailand

This study aimed to assess natural resource needs in the Khow Noi-Khow Pradoo non-hunting area (KNKPNHA) to understand the importance of using these natural resources. The assessment was based on local resource funds combined with the quality standards set by environmental agencies. The methodology involved collecting data through group interviews with academics and local stakeholders, along with individual interviews with villagers living in communities around KNKPNHA. Semi-structured interviews, check-lists, questionnaires, and the analysis of conceptual framing were used. KNKPNHA covers an area of 13516.8 hectares. The main land cover types in the area are mixed deciduous forest (major) and deciduous forest (minor). The primary occupation of villagers in KNKPNHA is the collection of wild edible plants from the forest, and is the next most important occupation is tourist services. The villagers can be classified into three categories: (1) those who hold land and utilize the forest; (2) distant has influence on understanding of space application regulations and services; and (3) those whose household income is related to their condition. It was found that villagers in KNKPNHA rely on wild edible plants from the forest. However, the results of the study show that perceptions of the importance of collecting fuel and herbal medicine from the forest is decreasing. Future research should investigate resource use behavior, including conservation management, with the participation of locals in the KNKPNHA and help communities to monitor and safeguard resources alongside government personnel.

Characterizing the Regional Differences in Carbon Dioxide Concentration Based on Satellite Observations in the Beijing-Tianjin-Hebei Region during 2015–2021

The regional differences in carbon dioxide (CO2) variations from the Orbiting Carbon Observatory-2 (OCO-2) over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 2015 to 2021 are analyzed in this study. This study shows an annual increase and a seasonal cycle; the CO2 annual growth rate was about 2.63 ppm year−1, with the highest value being in spring and the lowest in summer. The spatial distribution is unbalanced, regional differences are prominent, and the CO2 concentration is lower in the north of the Jing-Jin-Ji region (like Zhangjiakou, Chengde, and Qinhuangdao). Land-type structures and population economy distributions are the key factors affecting CO2 concentration. By analyzing the land-type structures over Jing-Jin-Ji in 2020, we find that cropland, woodland, and grassland (CWG) are the main land cover types in Jing-Jin-Ji; the proportion of these three types is about 83.3%. The woodland areas in Zhangjiakou, Chengde, and Qinhuangdao account for about 65% of the total woodland areas in Jing-Jin-Ji; meanwhile, the grassland areas in these three regions account for 62% of the total grassland areas in Jing-Jin-Ji. CO2 concentration variation shows a high negative correlation with CWG land areas (coefficient of determination (R2) > 0.76). The regions with lower population and GDP secondary industry (SI) density also have lower CO2 concentration (like Zhangjiakou, Chengde, and Qinhuangdao), and the regions with higher population and GDP SI density also have higher CO2 concentration (like the southeast of Jing-Jin-Jin).

Mapping spatiotemporal soil moisture in highly heterogeneous agricultural landscapes using mobile dual‐spectra cosmic‐ray neutron sensing

AbstractAccurate large‐scale soil moisture (SM) maps are crucial for catchment‐scale hydrological models used for water resource management and warning systems for droughts, floods, and wildfires. SM can be mapped by mobile cosmic‐ray neutron (CRN) systems of moderated detectors at homogeneous landscapes of similar soil and vegetation. In this study, we present a new approach for mobile CRN detection to perform to its full potential, where CRN measurements can also be converted to SM at heterogeneous landscapes. The approach is based solely on thermal and epithermal neutron datasets obtained from mobile dual‐spectra CRN detection, combined with theoretical developments using a particle transport model. For each measurement point, the land cover type is identified using the thermal‐to‐epithermal (T/E) ratio, and the relevant neutron‐count‐to‐soil‐moisture conversion function is estimated from CRN stations located at the main land cover types in the catchment. With this approach, the requirement of collecting 100+ soil samples for each point along the survey route is omitted. We use this T/E‐dependent approach to obtain SM maps from 12 CRN surveys and compare it with a simple approach where only the conversion function from the agricultural site is used. SM by the simple approach is comparable to the estimates of the agricultural stations of a capacitance sensor network, while the estimates of the T/E‐dependent approach also compare well with the heathland and forest stations. With accurate SM estimates for all landcover types, the average error is reduced from 0.089 to 0.038 when comparing CRN SM with space‐borne Soil Moisture Active Passive Mission estimates.

Exploring Land-Cover Types and Their Changes in the Open-Pit Mining Area of Ordos City Using Sentinel-2 Imagery

Exploring the land-cover types and their changes in open-pit mining areas is of great significance to the ecological restoration of Ordos City and its sustainable development. Therefore, in this study, the temporal features, spectral features, and the derived features from intra-annual images combined with the random forest method were used to determine the land-cover types and their changes in the open-pit mining area of Ordos City in 2019 and 2022. The results show that the land-cover types in the open-pit mining area of Ordos City are construction land, bare land, water, vegetation, and coal. The main land-cover types in the western open-pit mining area of Ordos City are different from those in the eastern open-pit mining area. The main land-cover type changes in the open-pit mining area from 2019 to 2022 are the conversion of bare land to vegetation and the conversion of coal to bare land, as well as the conversion of vegetation to bare land. This study provides the possibility of dynamic monitoring for the realization of green mine planning in Ordos City.

Fine-scale characterization of irrigated and rainfed croplands at national scale using multi-source data, random forest, and deep learning algorithms

Knowledge of the extent and distribution of irrigated and rainfed croplands is critical in providing the necessary baseline data for enhancing agricultural efficiency and making informed policy decisions. Accurately identifying and mapping irrigated and rainfed croplands can hasten the attainment of Sustainable Development Goals (SDGs) 1 and 2, aimed at reducing poverty and hunger, respectively. However, traditional methods employed to identify and map cropland areas are expensive and require substantial labour, particularly in extensive environments. As a result, this study presents a comprehensive and spatially explicit methodological framework for identifying and mapping national-scale irrigated and rainfed croplands in South Africa. This framework leverages low-cost earth observation technologies (Sentinel-2 MSI) and employs highly accurate classification algorithms, namely Deep Learning Neural Network (DNN) and Random Forest (RF). The proposed methodology strategically integrates data from multiple sources, including public repositories (e.g., cropland data, evapotranspiration), ongoing research (e.g., land cover maps), and field data, to enhance the accuracy and reliability of the results. The methodology begins by employing a robust random forest model to classify the study area into distinct land cover types. Leveraging the power of a deep learning neural network (DNN), the method accurately distinguishes between irrigated and rainfed croplands in South Africa. The random forest model achieved a notable classification accuracy of 0.77 when identifying the main land-use and land cover types. Meanwhile, the deep learning neural network (DNN) model achieved an accuracy of 0.71 in differentiating rainfed and irrigated croplands at a national scale. These results highlight the effectiveness of the proposed methodology in providing baseline information relevant to crop monitoring, yield forecasting, and understanding agricultural food supply systems. Furthermore, the proposed methodology has the potential to offer timely and accurate information on cropland areas and their extent which could assist in implementing targeted interventions for optimising agricultural productivity. With its potential to be upscaled to other sub-Saharan countries, this methodology enriches agricultural decision-making and plays a vital role in bolstering food security and advancing the attainment of SDGs.

Application of a two source energy balance model coupled with satellite based soil moisture and thermal infrared data

Satellite retrievals of thermal infrared data from the Earth’s surface when combined with the surface energy balance equation are widely used to monitor land surface evapotranspiration (ET). Unfortunately thermal infrared based models using this approach have a systematic positive bias with ET during soil water stress conditions. The Two-source Energy Balance model using information on Soil Moisture as another key constraint (TSEB-SM) improves model performance and utility. Here, high resolution satellite soil moisture data from SMAP (Soil Moisture Active Passive) were used to provide soil water boundary conditions for the TSEB-SM model. This SMAP driven TSEB-SM approach was applied in the Heihe river basin in China across a wide range of soil moisture conditions using MODIS thermal infrared land surface temperature (LST) data. The ET estimates from TSEB-SM were validated using flux tower measurements over the main land cover types in the study area and compared with the results from the original TSEB model. The comparison showed that the TSEB-SM approach reduced the RMSE error in latent heat flux estimates by 5 % to 20 % at satellite overpass time, and TSEB-SM outperformed the original TSEB model in daily ET estimates (RMSE values of 1.0 mm/d versus 1.5 mm/d, respectively) across all validation sites. The magnitude of the TSEB-SM improvements were larger early and late in the growing season and in arid areas, consistent with soil moisture improving estimates of ET particularly under times of higher water stress. The use of SMAP satellite soil moisture data with the TSEB-SM model increased the reliability of ET estimation and provides an improved capacity for continuos monitoring of drought effects on the global water cycle.

Deforestation and Its Effect on Surface Albedo and Weather Patterns

Deforestation is an important environmental problem and a key promoter of regional climate change through modifying the surface albedo. The objective of this research was to characterize the impact of deforestation and land use changes on surface albedo (α) and climate patterns in a tropical highland region of Mexico, between the years 2014 and 2021. The main land cover types are coniferous forests (CF), oak and gallery woodlands (OGW), and annual agriculture (AA), which represent more than 88% of the regional territory. We used 2014 and 2021 Landsat 8 OLI images with topographic and atmospheric correction in order to develop an inventory of albedo values for each land cover type in both time scenarios. Albedo images were generated by using the equation proposed by Liang in 2001, which is based on the reflectance of the bands 2, 3, 4, 5, and 7. Differences in albedo values were calculated between the years 2014 and 2021, and those differences were correlated with variations in climate parameters, for which we used climate data derived from the WRF model. In addition, the different land use changes found were classified in terms of triggers for increasing or decreasing surface albedo. We used the Mann–Whitney U Test to compare the 2021 − 2014 climatic deviations in two samples: Sample A, which included sites without albedo change in 2021; and Sample B, including sites with albedo change in 2021. Results showed that between 2014 and 2021, at least 38 events of land use change or deforestation occurred, with albedo increments between 1 and 11%, which triggered an average increment of 2.16% (p < 0.01; Mann–Whitney U Test) of the regional surface albedo in comparison to the 2014 scenario. In this period, the albedo for CF, OGW, and AA also increased significantly (p < 0.001; Mann–Whitney U Test) by +79, +12, and +9%, respectively. In addition, the regional albedo increment was found to be significant and negatively correlated (p < 0.01 Spearman’s coefficient) with relative humidity (RH), maximum temperature (Tmax), and minimum temperature (Tmin), and correlated (p < 0.01) positively with diurnal temperature range (DTR). The Mann–Whitney U Test revealed that 2021 climatic variations in Sample B sites are statistically different (p < 0.05) to 2021 climatic variations in Sample A sites, which demonstrates that albedo changes are linked to a decrease in minimum temperature and relative humidity and an increase in DTR. Conversion of CF and OGW into AA, perennial agriculture (PA), or grassland (GR) always yielded an albedo increment, whilst the conversion of AA to irrigation agriculture (IA) or PA triggered a decrease in albedo, and finally, the pass from GR or AA to protected agriculture (PA) caused albedo to increase or decrease, depending on the greenhouse covers materials. Reducing deforestation of CF and OGW, conversion of AA or GR into PA, and selecting adequate greenhouse covers could help to mitigate regional climate change.

Spatiotemporal and Driving Factors of Land-Cover Change in the Heilongjiang (Amur) River Basin

Monitoring land-use and land-cover change (LUCC) is extremely important in the sustainable development and management of terrestrial ecosystems. Taking the Heilongjiang (Amur) River Basin as the study area, this study aimed to identify the spatiotemporal characteristics of land cover at the entire basin and at the country levels of the three countries using the land-use change index, frequency statistics and land-cover change degree. The results showed that: (1) The LULC types were mainly forest land and grassland (accounting for nearly 83% in total) from 2001 to 2019. The main land-cover types in China, Russia and Mongolia were forest land, forest land and grassland, respectively. (2) The area of urban land, cropland and wetland increased significantly from 2001 to 2019, while the area of forest land and bare land decreased during this time. In general, the ecological environment has greatly improved over the last 19 years, although the relevant ecological restoration still needs to be further implemented. The findings can provide a scientific basis for ecological protection and sustainable development in the Heilongjiang (Amur) River Basin. The approaches here are also applicable to land-cover change research in other similar regions.

İzmir'de kentleşmenin neden olduğu kent ekosistemi ve yerel iklim değişikliklerinin uzun süreli KIA oluşumu açısından STG yöntemiyle değerlendirilmesi

Even if urbanization offers various opportunities to people living in todays world. It also comes with some side effects such as worsening climate conditions by creating thermal pollution due to certain urban activities, sectoral urban designs and consequent patterns in cities. In local sense, the old climatic conditions beforete the change because of urbanization in rural areas can be called natural when they are compared with new conditions deteriorated by widespread urbanization. Thus, thermal pollution changes city’s local climate over time and negatively affects city’s resilience. Here in this research, it is determined themperature related local climate variation caused by specific city activities in the city of Izmir by analysing time series thermal data distribution over the entire city over a certain period of time and for this analyse even a novel approach is introduced and suggested which is a Simulated Single Image (SSI) method based on Simulated Single Data (SSD) statistical analyze. The method uses not only trend or average values of time series data as being as usual but it uses both and also standart deviation of the data to support a single output from the time series data analyse. Thus, outputs were obtained as single images from the the LANDSAT time series data to represent where generally Urban Hot Spots (UHS) appear and Urban Heat Islands (UHI) develop in the city. Stereo representation of the study region is also used to visually examine the topographical effect on UHI distribution in the city. Izmir which is the third mostly populated city of Turkey located on the Izmir Gulf of Egean Sea is chosen as study area and the study clearly demonstrated that industrial regions and roads with large surfaces, bare lands with sparse bushes, empty or sparse grassy urban lands and more significantly the urban land parts faced to certain directions are the main urban land cover and structure types contributing UHSs to appear and UHI developments in the city.

The effects of flash drought on the terrestrial ecosystem in Korea

Typified by rapid intensification in a short time, flash droughts (FDs) have attracted increasing attention because of their effects on water resources and ecosystems. Water use efficiency (WUE) is a key indicator for understanding the interaction between the carbon dynamic and water cycle. However, the effects of FDs on terrestrial ecosystems in Korea remain mostly unknown. Therefore, we identified FD events in Korea using the soil moisture (SM)-based rate of intensification (RI) and the evapotranspiration (ET)-based Standardized Evaporative Stress Ratio (SESR) with flux tower data. Continuous satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) were used to examine spatiotemporal patterns and trends in FD-related factors, Potential ET (PET), Actual ET (AET), Gross Primary Productivity (GPP), Evaporative Stress Ratio (ESR), and WUE from 2001 to 2020. The frequency, duration, and intensity of FDs in Korea were investigated and responses in WUE to FDs in different landcover types were examined. The SESR and RI had a similar performance in capturing FD events in flux tower site data. At the regional scale, a lower ESR was mainly observed in the western and southern parts of the study area, indicating progressively drier climate conditions. The mean frequency percentage and number of Korean FD events in the past 20 years were 61.3% and 16, respectively. In addition, more FD events occurred in the northern and eastern regions of the study area, where the main landcover type is forest, and northeastern Korea suffered from long FD events (over 30 days). FDs caused a positive response of WUE in most of the study area. However, the change in WUE after FD events varied by FD severity and landcover type. The changes in WUE after moderate FD events were largely driven by the change in GPP, and the highest sensitivity of WUE to FD intensity was found in croplands. These findings highlight the characteristics and effects of FDs on WUE across landcover types, which will be helpful in managing potential environmental risks in Korea.

Toward a better understanding of uncertainty for satellite precipitation products over complex terrain with sparse rain gauge data

Satellite precipitation product quality is critical for various meteorological and hydrological applications. Products are subjected to more uncertainties in sparsely-gauged regions than in densely-gauged regions due to insufficient error correction. However, ground-truth-based validation cannot accurately quantify the spatially distributed uncertainty of satellite precipitation products over complex terrain with sparse rain gauge data. In this study, we explored a gauge-independent method, called the Three-Cornered Hat (TCH), to overcome the quantification issue and better understand the uncertainty of satellite precipitation products in such situations. Firstly, the reliability of the TCH method was explored over a topographically complex area. Then, the performance of the Soil Moisture TO RAIN-Advanced SCATterometer (SM2RAIN-ASCAT) and Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) in the Chuan-Yu region was evaluated using TCH, with the help of the China Merged Precipitation Analysis (CMPA) precipitation product as the third input. We found that: (1) The high consistency between error standard deviation (ESD) estimated by TCH and root mean square error (RMSE) estimated by ground-truth-based validation demonstrated that TCH is reliable for evaluating precipitation datasets over complex terrain; (2) The ESDs of SM2RAIN-ASCAT and IMERG had different spatial distributions, and the quality of IMERG was overall higher than that of SM2RAIN-ASCAT over a mountainous area; (3) IMERG performed better than SM2RAIN-ASCAT both at low (under 3000 m) and high elevations (over 3000 m) in most years, as well as for most main land cover types (woody savannas, mixed forests, and grasslands) in the study area. This method and these findings provide a new insight for better use of satellite precipitation products over complex terrain.

Spatiotemporal evolution of land cover changes and landscape ecological risk assessment in the Yellow River Basin, 2015–2020

The Yellow River Basin (YRB), which has faced severe ecological issues since ancient times, is one of the largest and most difficult-to-govern basins in the world. Recently, all provincial governments within the basin have individually enacted a series of measures to protect the Yellow River; however, the lack of central governance has inhibited efforts. Since 2019, the government has comprehensively managed the YRB, improving the governance to unprecedented levels; however, evaluations of the YRB's overall ecological status remain lacking. Using high-resolution data from 2015 to 2020, this study illustrated major land cover transitions, evaluated the correlated overall ecological status of the YRB via the landscape ecological risk index, and analyzed the relationship between risk and landscape structure. The results showed that the (1) main land cover types in the YRB in 2020 are farmland (17.58%), forestland (31.96%), and grassland (41.42%), with urban land accounting for 4.21%. Some social factors were significantly related to changes in major land cover types (e.g., from 2015 to 2020, forest and urban lands have increased by 2.27% and 10.71%, grassland and farmland decreased by 2.58% and 0.63%, respectively). (2) Landscape ecological risk improved, albeit with fluctuations (high in the northwest, low in the southeast). (3) Ecological restoration and governance were imbalanced since no obvious changes were observed in the western source region of the Qinghai Province (Yellow River). (4) Finally, positive impacts of artificial re-greening showed slight lags as the detected improvements in NDVI were not recorded for approximately 2 years. These results can facilitate environmental protection and improve planning policies.

Accuracy Assessment of Unsupervised Land Use and Land Cover Classification Using Remote Sensing and Geographical Information Systems

A significant tool for determining how accurate a categorization product is the accuracy assessment. Remote Sensing is one of the essential tools for compiling land use and land cover maps through image classification. The availability of high-quality Landsat imagery and secondary data, an accurate classification technique, and the user's knowledge and competence with the procedures essential to the image classification process. Assess the satellite image classification suitability for further mapping and analysis through accuracy assessments. This paper examined land use and land cover classification using unsupervised classification and extracted NDBI and NDVI further to support main land use and land cover types in the area. The accuracy of classifications was assessed using an error matrix and Kappa statistics. Land use and land cover, NDBI, and NDVI classification accuracies are almost perfect, further verified by the Kappa statistics tool. An excellent unsupervised classification of land use and land cover classes was generated. Accuracy assessment evaluation is one of the most significant tools for determining a classification product's accuracy. The confusion error matrix and Kappa coefficient were particularly useful in calculating accuracy assessment. Typically, accuracy measures of the unsupervised classification show a moderate accuracy level. This study observed almost perfect agreement in all types of accuracy measures. This study is an important source of information that planners and decision-makers may utilize to plan the environment sustainably.

Temporal and spatial changes of soil erosion under land use and land cover change based on Chinese soil loss equation in the typical watershed on the Loess Plateau

AbstractLand use and land cover change (LULCC) directly affect the temporal and spatial change of soil erosion. As a typical governance watershed in the hilly and gully area of the Loess Plateau, the Jiuyuangou watershed has experienced significant LULCC in the past 10 years due to conversion of farmland to forests, economic construction, and cropland abandonment. However, the evolution process of soil erosion change and LULCC in the watershed is unclear, as is the relationship between the two. This study used satellite images to extract information on LULCC in the watershed and the Chinese soil loss equation (CSLE) model to evaluate the temporal and spatial evolution of soil erosion in the watershed from 2010 to 2020. The main results showed that (1) the continuous vegetation restoration project in the watershed reduced soil erosion from 2010 to 2015; however, the increased frequency of extreme rainfall events after 2015 reduced its impact. The annual average soil erosion modulus decreased from 10.85 t ha−1 year−1 in 2010 to 8.03 t ha−1 year−1 in 2015 but then increased to 10.57 t ha−1 year−1 in 2020; (2) the main land use and land cover (LULC) type in the Jiuyuangou watershed is grassland, accounting for 62% of the total area, followed by forestland, cropland, buildings, and water. Cropland has the largest multi‐year average soil erosion modulus, followed by grassland and buildings, with forestland having the smallest; (3) significant spatial correlations occurred between soil erosion change and LULCC for common ‘no change’ and common ‘gain’ in the settlements, roads, and areas near the human influences with good soil and water conservation, but not other regions due to the influence of climatic factors (heavy rain events). Thus, we should repair terraces, control dams in the watershed, and actively conserve water and soil. This study provides a scientific reference for planning and managing water and soil conservation and ecological environment construction in the watershed.

Characterizing the Long-Term Landscape Dynamics of a Typical Cloudy Mountainous Area in Northwest Yunnan, China

Detailed knowledge of landscape dynamics is crucial for many applications, from resource management to ecosystem service assessments. However, identifying the spatial distribution of the landscape using optical remote sensing techniques is difficult in mountainous areas, primarily due to cloud cover and topographic relief. Our study uses stable classification samples from mountainous areas to investigate an integrated approach that addresses large volumes of cloud-cover data (with associated data gaps) and extracts landscape time series (LTS) with a high time–frequency resolution. We applied this approach to map LTS in a typical cloudy mountainous area (Erhai watershed in northwestern Yunnan, China) using dense Landsat stacks, and then we also used the classified results to investigate the spatial–temporal landscape changes in the study area at biennial intervals. The overall accuracy of the landscape classification ranged from 81.75% to 88.18%. The results showed highly dynamic processes in the landscape throughout the study period. Forest was the main land cover type, covering approximately 39.19% to 41.68% of the total study area. Alpine meadow showed fluctuating trends, with a net loss of 11.22% and an annual reduction rate of −0.4%. Shrub cover increased by 1.26%, and water bodies showed a small decrease in area, resulting in an overall net change of −0.03%. Built-up land and farmland areas continued to expand, and their annual growth rates were 1.52% and 1.06%, respectively. Bare land showed the highest loss, with a net change of 228.97 km2. In the Erhai watershed, all the landscape classes changed or transitioned into other classes, and a substantial decrease in bare land occurred. The biennial LTS maps allow us to fully understand the spatially and temporally complex change processes occurring in landscape classes; these changes would not be observable at coarse temporal intervals (e.g., 5–10 years). Our study highlights the importance of increasing the temporal resolution in landscape change studies to support sustainable land resource management strategies and integrate landscape planning for environmental conservation.

Spatiotemporal Dynamics of Land Cover and Their Driving Forces in the Yellow River Basin since 1990

The national strategy for ecological protection and high-quality development is raising the ecological security protection to an unprecedented level in the Yellow River Basin (YRB) of China. Due to the explicitly analyzed land cover changes under climate change and rapid urbanization in the YRB area since 1990, land cover dynamic degree index, transfer matrix, and geo-detector method were used to explicate land cover changes and their key driving factors, based on the spatial data of land cover from 1990 to 2020. The results show that grasslands, croplands, and forests are the main land cover types, accounting for 48.37%, 25.05%, and 13.50%, respectively, of the total area in the YRB area. Grassland, cropland, and cropland are the major land cover type, accounting for 61.49%, 37.13%, and 66.33%, respectively, in the upstream, midstream, and downstream of the YRB area. Built-up land has showed a continual increasing trend, and its dynamic degree was up to 3.38% between 2010 and 2020. Population density was a key factor for land cover change, with an average contribution rate of 0.264; then, elevation and temperature also expressed an important role to drive the land cover change in the YRB area during the period from 1990 to 2020.

Soil organic carbon stocks in the high mountain permafrost zone of the semi-arid Central Andes (Cordillera Frontal, Argentina)

This study presents the first detailed soil organic carbon (SOC) inventory for a high mountain permafrost zone in the semi-arid Central Andes of South America. We describe plant cover and soil profiles at 31 sites representing the main land cover and landform types in the Veguitas catchment (Cordillera Frontal, Argentina), which ranges in elevation from c. 3000 to 5500 m. The vegetated area with soil development is largely confined to altitudes of < 3650 m and represents only 8.2% of the total catchment area. Mean SOC 0–100 cm storage for the vegetated portion of the catchment is 3.62 kg C m−2, which is reduced to 0.33 kg C m−2 if we consider negligible SOC stocks in the extensive bare ground and glaciated areas at higher elevations. Hotspots of SOC storage are wet meadow areas, with peat deposits up to 102 cm deep and a maximum observed total SOC storage of 53.07 kg C m−2. These wet meadow areas, however, occupy only 0.11% of the total catchment area and their contribution to mean SOC storage is limited. Among soils at well-drained sites, highest mean SOC 0–100 cm storage is found on backslope positions of moraines that predate the Last Glacial Maximum (6.87 kg C m−2). Only 2% of all SOC stocks in the catchment are found in permafrost terrain and none are located in the permafrost layer itself. The main ecoclimatic control on SOC storage is plant cover, with vegetation limits being sensitive to ambient temperature. Projected increases in temperatures will not remobilize any frozen SOC stocks but will likely result in an upward shift of the upper vegetation belt with soil development creating new areas of phytomass carbon and SOC storage. The area is expected to represent a net C sink and thus a negative feedback on future global warming.

Modifying the SWAT Model to Simulate Eco-Hydrological Processes in an Arid Grassland Dominated Watershed

Grasslands are the main land cover type and one of the most important ecosystems in arid and alpine endorheic basins. The vegetation coverage of grasslands is spatially heterogeneous in arid and alpine areas and it may lead to variations in water allocation. The Soil and Water Assessment Tool (SWAT) is one of the most widely used semi-distributed catchment-scale eco-hydrological models. The leaf area index (LAI) is one of the vegetation coverage indexes and is incorporated in the SWAT model. However, in SWAT, the LAI accumulation is controlled by heat, and neglects other relevant factors such as precipitation and terrain. To address the drawbacks of the SWAT in simulating vegetation coverage and plant patterns, several studies have focused on improving LAI estimation. However, they still have been limited to arid and alpine grasslands with different vegetation coverages. In this study, we modified the SWAT model using remotely sensed LAI data with high temporal and spatial resolution. We used this to better simulate eco-hydrological processes in grassland basins with different vegetation coverages in the upper reaches of the Bayin River Basin. Results showed that for the original SWAT model, the simulated LAI was homogeneous within each land use/cover type, whereas the remotely sensed LAI was spatially heterogeneous and better captured the vegetation coverage of the entire basin. The proper estimation of the LAI was reflected in the improved simulation of the monthly streamflow and sediment yield at the basin outlet and the monthly ET. These findings indicate that the modified SWAT could better simulate hydrological processes in arid and alpine grasslands with different vegetation coverages.

Landsat time series reveal forest loss and woody encroachment in the Ngorongoro Conservation Area, Tanzania

AbstractThe Ngorongoro Conservation Area (NCA) of Tanzania, is globally significant for biodiversity conservation due to the presence of iconic fauna, and, since 1959 has been managed as a unique multiple land‐use areas to mutually benefit wildlife and indigenous residents. Understating vegetation dynamics and ongoing land cover change processes in protected areas is important to protect biodiversity and ensure sustainable development. However, land cover changes in savannahs are especially difficult, as changes are often long‐term and subtle. Here, we demonstrate a Landsat‐based monitoring strategy incorporating (i) regression‐based unmixing for the accurate mapping of the fraction of the different land cover types, and (ii) a combination of linear regression and the BFAST trend break analysis technique for mapping and quantifying land cover changes. Using Google Earth Pro and the EnMap‐Box software, the fractional cover of the main land cover types of the NCA were accurately mapped for the first time, namely bareland, bushland, cropland, forest, grassland, montane heath, shrubland, water and woodland. Our results show that the main changes occurring in the NCA are the degradation of upland forests into bushland: we exemplify this with a case study in the Lerai Forest; and found declines in grassland and co‐incident increases in shrubland in the Serengeti Plains, suggesting woody encroachment. These changes threaten the wellbeing of livestock, the livelihoods of resident pastoralists and of the wildlife dependent on these grazing areas. Some of the land cover changes may be occurring naturally and caused by herbivory, rainfall patterns and vegetation succession, but many are linked to human activity, specifically, management policies, tourism development and the increase in human population and livestock. Our study provides for the first time much needed and highly accurate information on long‐term land cover changes in the NCA that can support the sustainable management and conservation of this unique UNESCO World Heritage Site.