Main Land Cover Research Articles

Quantifying Soil Loss in the Brazilian Savanna Ecosystem: Current Rates and Anticipated Impact of Climate Changes

ABSTRACTThe Brazilian Savanna (Cerrado) is the second‐largest South American biome that corresponds to almost two‐third of the national agricultural production. Extensive agricultural‐driven land‐use changes have significantly altered the landscape, causing increased soil erosion. Furthermore, projections of climate change effects on the Cerrado raise concerns about the potential exacerbation of soil loss and its consequences on ecosystem sustainability. This study investigated soil loss for the Cerrado ecosystem by assessing current rates and projecting the potential effects of future climate change. Current soil loss was based on experimental plots (100 m2) during 7 years maintained under typical main land cover in Brazil (sugarcane, pasture, Cerrado, and bare soil). Erosivity, by using the Universal Soil Loss Equation (USLE), was estimated from observations, parameters of erodibility, and land cover. To assess the future soil loss (2100), we used the calibrated USLE equation with yearly erosivity derived from 12 downscaled and bias‐corrected SSP2‐4.5 and SSP5‐8.5 scenarios of CMIP6 climate model projections. Current agricultural practices induce considerable erosion, where sugarcane has 3.4 times higher soil loss as compared with the natural soil cover. Regarding future SSP2‐4.5 and SSP5‐8.5 scenarios (2100), we estimated an increase of 4.9% and 7.6% in soil loss, respectively, for all land covers. The observed soil loss rates underscore the critical importance of implementing sustainable land management practices to mitigate further soil degradation. Climate change may impose additional stress on the Cerrado ecosystem, amplifying the urgency for adaptive measures to safeguard this important biome.

How to measure outcomes in forest restoration? A European review of success and failure indicators

Restoration involves the recovery and repair of environments because environmental damage is not always irreversible, and communities are not infinitely resilient to such harm. When restoration projects are applied to nature, either directly or indirectly these may take the form of ecological, forestry or hydrological restoration, for example. In the current scenario of global climate change and increasing intensity of disturbances the importance of restoration in all types of ecosystems in order to adapt to the new conditions (so called prestoration) is evident. Whatever the objective of the restoration initiative, there is a lack of consensus as regards common indicators to evaluate the success or failure of the different initiatives implemented. In this study, we have carried out an extensive meta-analysis review of scientific papers aiming to evaluate the outcomes of restoration projects. We have done a review and selected 95 studies implemented in Europe. We explored the main pre-restoration land cover in which restoration initiatives have been implemented, the main causes of degradation, the objective of the restoration action and the indicators selected to analyze the success or failure of the action. We identified a total of 84 indicators in the analyzed papers and compared with the ones proposed for forest in the recent Nature Restoration Law. The analysis revealed five indicators commonly used for the evaluation of restoration initiatives (abundance, coverage, density, Ellenberg indicator, and richness), even where the initial objective has not yet been achieved. Our findings underscore both the benefits and challenges associated with a specific set of harmonized indicators for evaluating the success or failure of restoration initiatives.

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).

Vegetation Type Mapping in Southern Patagonia and Its Relationship with Ecosystem Services, Soil Carbon Stock, and Biodiversity

Vegetation Type (VT) mapping using Optical Earth observation data is essential for the management and conservation of natural resources, as well as for the evaluation of the supply of provisioning ecosystem services (ESs), the maintenance of ecosystem functions, and the conservation of biodiversity in anthropized environments. The main objective of the present work was to determine the spatial patterns of VTs related to climatic, topographic, and spectral variables across Santa Cruz province (Southern Patagonia, Argentina) in order to improve our understanding of land use cover at the regional scale. Also, we examined the spatial relationship between VTs and potential biodiversity (PB), ESs, and soil organic content (SOC) across our study region. We sampled 59,285 sites sorted into 19 major categories of land cover with a reliable discrimination level from field measurements. We selected 31 potential predictive environmental dataset covariates, which represent key factors for the spatial distribution of land cover such as climate (four), topography (three), and spectral (24) factors. All covariate maps were generated or uploaded to the Google Earth Engine cloud-based computing platform for subsequent modeling. A total of 270,292 sampling points were used for validation of the obtained classification map. The main land cover area estimates extracted from the map at the regional level identified about 142,085 km2 of grasslands (representing 58.1% of the total area), 38,355 km2 of Mata Negra Matorral thicket (15.7%), and about 25,189 km2 of bare soil (10.3%). From validation, the Overall Accuracy and the Kappa coefficient values for the classification map were 90.40% and 0.87, respectively. Pure and mixed forests presented the maximum SOC (11.3–11.8 kg m−2), followed by peatlands (10.6 kg m−2) and deciduous Nothofagus forests (10.5 kg m−2). The potential biodiversity was higher in some shrublands (64.1% in Mata Verde shrublands and 63.7% in mixed shrublands) and was comparable to those values found for open deciduous forests (Nothofagus antarctica forest with 60.4%). The provision of ESs presented maximum values at pure evergreen forests (56.7%) and minimum values at some shrubland types (Mata Negra Matorral thicket and mixed shrubland) and steppe grasslands (29.7–30.9%). This study has provided an accurate land cover and VT map that provides crucial information for ecological studies, biodiversity conservation, vegetation management and restoration, and regional strategic decision-making.

Remote sensing in the analysis between forest cover and COVID-19 cases in Colombia

This article explores the relationship between forest cover and coronavirus disease 2019 (COVID-19) cases in Colombia using remote sensing techniques and data analysis. The study focuses on the CORINE land cover methodology's five main land cover categories: artificial territory, agricultural territories, forests and semi-natural areas, humid areas, and water surfaces. The research methodology involves several phases of the unified method of analytical solutions for data mining (ASUM-DM). Data on COVID-19 cases and forest cover are collected from the Colombian National Institute of Health and Advanced Land Observation Satellite (ALOS PALSAR), respectively. Land cover data is processed using QGIS software. The results indicate an inverse relationship between forest cover and COVID-19 cases, as evidenced by Pearson's index ρ of -0.439 (p-value <0.012). In addition, a negative correlation is observed between case density and forests and semi-natural areas, one of the land cover categories. The findings of this study suggest that higher forest cover is associated with lower numbers of COVID-19 cases in Colombia. The results could potentially inform government organizations and policymakers in implementing strategies and policies for forest conservation and the inclusion of green areas in densely populated urban areas.

A study on the spatial and temporal dynamics of landscape spatial patterns of different types of rural communities in Taiwan

Climate change and urbanization are the main factors causing landscape changes not only in urban areas but also in rural communities. Rural communities have economic, cultural, social, and significant ecological value. Based on the background of environmental change, this study selects three main classes of rural communities in Taiwan as the research areas. Based on the remote sensing images from 2001 to 2020 and the survey of land use status, the land transfer matrix and landscape index method were used to analyze the landscape spatial pattern changes and driving forces of rural communities. The results revealed that (1) the land use changes of the four different types of rural communities have both common features. The growth of construction land and transportation land area is a common phenomenon, and the growth rate matches the type of rural community. Forest land or farmland is the main land cover. From the perspective of land transfer analysis, it is the mutual conversion and loss of agricultural land, forest land and other land. (2) There are differences in the landscape pattern changes of the three main classes of rural communities. The production community (Dalian) pattern is progressing steadily. The ecological community (Gangbian) pattern is progressing slowly. The process of living community pattern varies greatly, while the progress of village experience type (Shuangtan) pattern is slow. The comprehensive wealth type (Seshui) pattern is developing unevenly and at a fast pace. (3) The driving force of land use change mainly comes from the policy drive of the public sector, the spatial planning system, and community development activities. This study fills in the gaps in the development of landscape spatial patterns of different types of rural communities, and provides a reference for the formulation of regulations on the spatial planning system of rural communities in Taiwan.

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.

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.

Land cover flows and land use intensity in the three decades of the post-communist Czechia: Changing trends and driving forces

The land system faces many pressures from the provision of biomass resources and space to the economy. The need to understand land use and cover changes and its drivers is of high importance. This work presents an innovative approach by applying a transdisciplinary approach combining the methods of spatial analysis Land Cover Flows with the methods from the concept of socio-economic metabolism, Material and Energy Flow Accounting, Human Appropriation of Net Primary Production (HANPP) and Final Energy Return on Investment (FEROI). Our main aim is to identify the main land use changes and land cover flows, link them to the underlying socio-economic processes and interpret them in a historical context. Our results show that the overall land use intensity is growing although the positive trends of growing grasslands and forests started after the collapse of communism. The growing intensity of agricultural production with increasing suburbanization reversed these trends. Until the 2000s the HANPP decreased but at the end of the period increased from 55% in 2012 to 70% in 2018. Volumes of the extraction of agricultural biomass are growing while the area of agricultural land has decreased. FEROI grew and stabilized to around 1.0 in the last period (2012–2018) comparable to the value found in the year 2001. The suburbanization rates peaked after the year 2000 at 250 m2/km2/yr.

Spatial heterogeneity analysis of matching degree between endangered plant diversity and ecosystem services in Xishuangbanna.

Biodiversity and ecosystem services (ESs) are closely linked. Human activities have caused critical damage to the habitat and ecosystem function of organisms, leading to decline in global biodiversity and ecosystem services. To ensure sustainable development of local ecological environments, it is critical to analyze the spatial matching degree of biodiversity and ESs and identify ecologically vulnerable areas. Taking Xishuangbanna, southern China, as an example, we constructed a pixel-scale matching degree index to analyze the spatial matching degree of endangered plant diversity (EPD) and four ESs and classified the matching degree into low-low, low-high, high-low, and high-high four types. The results revealed a mismatch relationship of EPD and ESs in more than 70% of areas. Under the influence of altitude and land use/land cover (LULC) type, the matching degree of EPD and ESs showed obvious spatial heterogeneity. In low-altitude areas in the south of Xishuangbanna, EPD and ESs mainly showed mismatch, while high-altitude areas in the west had a better match. Natural forest was the main land cover in which EPD and ESs showed high-high match and its areal proportion was much larger than that of rubber plantation, tea plantation, and cropland. Our findings also stress the need to concentrate conservation efforts on areas exhibiting a low-low match relationship, indicative of potential ecological vulnerability. The pixel-scale spatial matching degree analysis framework developed in this study for EPD and ESs provides high-resolution maps with 30 m × 30 m pixel size, which can support the implementation of ecological protection measures and policy formulation, and has a wide range of applicability. This study provides valuable insights for the sustainable management of biodiversity and ESs, contributing to the strengthening of local ecological environment protection.

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.

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.

Land cover modelling for tropical forest vulnerability prediction in Kalimantan, Indonesia

This study aimed to estimate the vulnerability of tropical forest in Kalimantan, Indonesia, from simulated land cover changes using the Weighted Normalised Likelihood–Markov Chain model (WNL-MC) for the period 2018–2050 under a forest conservation and a no-conservation scenario. Predictions of future vulnerability and forest change were based on land cover maps for 2010 and 2014 developed using the integration of Landsat, ALOS PALSAR, and Sentinel-1 data. The four main land cover classes in the region were oil palm and rubber plantations, native forests, and non-forested areas. The performance of the WNL–MC model was evaluated by comparing the simulated and actual land cover maps for 2018 and validating them against high-resolution images. Kappa accuracy for the simulated maps was >85%, and overall accuracy was >90%. Under the scenario of no forest conservation, native forest showed approximately 50% loss over the next 30 years, while the forest conservation scenario showed a slowing of current deforestation by 18% from 2018 to 2030 and by 5.95% from 2030 to 2050. Native forest outside forest conservation zones was most vulnerable to conversion to other land uses, particularly in lowland areas close to settlements and roads. Estimations of native forest loss, particularly under the no-conservation scenario, emphasise the need for policies to preserve and conserve remaining native forest areas.

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.

Interactions between Climate and Land Cover Change over West Africa

Climate–land interaction over West Africa has often been assessed using climate simulations, although the model-based approach suffers from the limitations of climate models for the region. In this paper, an alternative method based on the analysis of historical land cover data and standardized climatic indices is used to investigate climate–land interactions, in order to establish climatic conditions and their corresponding land cover area changes. The annual variation in land cover area changes and climatic changes are first estimated separately and then linked using various spatiotemporal scales. The results show that incidences of land cover change result from abrupt changes in climatic conditions. Interannual changes of −1.0–1.0 °C, 0–1.5 °C, and −0.5–0.5 °C, and up to ±50 mm changes in precipitation and climatic water balance, lead to 45,039–52,133 km2, 20,935–22,127 km2, and approximately 32,000 km2 changes, respectively, while a ±0.5 °C and ±20 mm change represents normal climate conditions with changes below 20,000 km2. Conversely, conversions of cropland, forest, grassland, and shrubland are the main land cover change types affecting the climate. The results offer a basis for the re-evaluation of land cover change and climate information used in regional climate models simulating land–climate interactions over West Africa.

Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment

Technological innovations and advanced multidisciplinary research increase the demand for multisensor data fusion in Earth observations. Such fusion has great potential, especially in the remote sensing field. One sensor is often insufficient in analyzing urban environments to obtain comprehensive results. Inspired by the capabilities of hyperspectral and Light Detection and Ranging (LiDAR) data in multisensor data fusion at the feature level, we present a novel approach to the multitemporal analysis of urban land cover in a case study in Høvik, Norway. Our generic workflow is based on bitemporal datasets; however, it is designed to include datasets from other years. Our framework extracts representative endmembers in an unsupervised way, retrieves abundance maps fed into segmentation algorithms, and detects the main urban land cover classes by implementing 2D ResU-Net for segmentation without parameter regularizations and with effective optimization. Such segmentation optimization is based on updating initial features and providing them for a second iteration of segmentation. We compared segmentation optimization models with and without data augmentation, achieving up to 11% better accuracy after segmentation optimization. In addition, a stable spectral library is automatically generated for each land cover class, allowing local database extension. The main product of the multitemporal analysis is a map update, effectively detecting detailed changes in land cover classes.