Spatial Distribution of Soil Organic Carbon and Total Nitrogen in a Ramsar Wetland, Dafeng Milu National Nature Reserve

The invasion and expansion of Spartina alterniflora in coastal salt marsh wetlands have greatly affected the material cycle of the ecosystem. A total of 372 topsoil samples were collected from 124 sites representing two land-cover types by implementing an unprecedented high sampling density study in the Dafeng Milu National Nature Reserve. Classical statistics and geostatistics were used to quantify soil organic carbon (SOC) and total nitrogen (TN) spatial distribution. Redundancy analysis (RDA) was used to detect correlations between environmental factors, SOC, and TN. The results showed that SOC and TN have moderate variability. The spatial distributions of SOC and TN were similar, and the highest values were observed in the southwest of the study area. In different land cover types, the SOC and TN in the vegetation coverage areas with Spartina alterniflora as the dominant species were significantly higher than those in bare land. RDA showed that TN and aboveground biomass significantly affected the spatial distribution of SOC, while SOC and AGB dominated the spatial distribution of TN.

Quantifying the Spatiotemporal Variation of Evapotranspiration of Different Land Cover Types and the Contribution of Its Associated Factors in the Xiliao River Plain

Evapotranspiration (ET) is a vital constituent of the hydrologic cycle. Researching changes in ET is necessary for understanding variability in the hydrologic cycle. Although some studies have clarified the changes and influencing factors of ET on a regional or global scale, these variables are still unclear for different land cover types due to the range of possible water evaporation mechanisms and conditions. In this study, we first investigated spatiotemporal trends of ET in different land cover types in the Xiliao River Plain from 2000 to 2019. The correlation between meteorological, NDVI, groundwater depth, and topographic factors and ET was compared through spatial superposition analysis. We then applied the ridge regression model to calculate the contribution rate of each influencing factor to ET for different land cover types. The results revealed that ET in the Xiliao River Plain has shown a continuously increasing trend, most significantly in cropland (CRO). The correlation between ET and influencing factors differed considerably for different land cover types, even showing an opposite result between regions with and without vegetation. Only precipitation (PRCP) and NDVI had a positive impact on ET in all land cover types. In addition, we found that vegetation can deepen the limited depth of land absorbing groundwater, and the influence of topographic conditions may be mainly reflected in the water condition difference caused by surface runoff. The ridge regression model eliminates multicollinearity among influencing factors; R2 in all land cover types was over 0.6, indicating that it could be used to effectively quantify the contribution of various influencing factors to ET. According to the results of our model calculations, NDVI had the greatest impact on ET in grass (GRA), cropland (CRO), paddy (PAD), forest (FOR), and swamp (SWA), while PRCP was the main influencing factor in bare land (BAR) and sand (SAN). These findings imply that we should apply targeted measures for water resources management in different land cover types. This study emphasizes the importance of comprehensively considering differences among various hydrologic cycles according to land cover type in order to assess the contributions of influencing factors to ET.

An estimation of the evapotranspiration of typical steppe areas using Landsat images and the METRIC model

Remote sensing excels in estimating regional evapotranspiration (ET). However, most remote sensing energy balance models require researchers to subjectively extract the characteristic parameters of the dry and wet limits of the underlying surfaces. The regional ET accuracy is affected by wrong determined ideal pixels. This study used Landsat images and the METRIC model to evaluate the effects of different dry and wet pixel combinations on the ET in the typical steppe areas. The ET spatiotemporal changes of the different land cover types were discussed. The results show that the surface temperature and leaf area index could determine the dry and wet limits recognition schemes in grassland areas. The water vapor flux data of an eddy covariance system verified that the relative error between the ETd,METRIC and ETd,GES of eight DOYs (day of the year) was 18.8% on average. The ETMETRIC values of the crop growth season and the ETIMS of eight silage maize irrigation monitoring stations were found to have a relative error of 11.1% on average. The spatial distribution of the ET of the different land cover types in the study area was as follows: ETwater > ETarable land > ETforest land > ETunutilized land > ETgrassland > ETurban land.

Spatial Representativeness of Gross Primary Productivity from Carbon Flux Sites in the Heihe River Basin, China

Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 1500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R2 = 0.56, RMSE = 3.07 g C m−2 d−1) than at field scale (R2 = 0.51, RMSE = 3.34 g C m−2 d−1), and the same situation occurred in the validation of high-resolution downscaled GPP (R2 = 0.85, RMSE = 1.34 g C m−2 d−1 when validated at footprint scale; R2 = 0.82, RMSE = 1.47 g C m−2 d−1 when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale.

Stretching the Habitat Envelope: Insectivorous Bat Guilds Can Use Rubber Plantations, but Need Understorey Vegetation and Forest Buffers

Effects of land-cover change on insectivorous bat activity can be negative, neutral or positive, depending on foraging strategies of bats. In tropical agroforestry systems with high bat diversity, these effects can be complex to assess. We investigated foraging habitat use by three insectivorous bat guilds in forests and rubber plantations in the southern Western Ghats of India. Specifically, we monitored acoustic activity of bats in relation to (1) land-cover types and vegetation structure, and (2) plantation management practices. We hypothesized that activity of open-space aerial (OSA) and edge-space aerial (ESA) bat guilds would not differ; but narrow-space, flutter-detecting (NSFD) bat guild activity would be higher, in structurally heterogeneous forest habitats than monoculture rubber plantations. We found that bat activity of all guilds was highest in areas with high forest cover and lowest in rubber plantations. Higher bat activity was associated with understorey vegetation in forests and plantations, which was expected for NSFD bats, but was a surprise finding for OSA and ESA bats. Within land-cover types, open areas and edge-habitats had higher OSA and ESA activity respectively, while NSFD bats completely avoided open habitats. In terms of management practices, intensively managed rubber plantations with regular removal of understorey vegetation had the lowest bat activity for all guilds. Intensive management can undermine potential ecosystem services of insectivorous bats (e.g., insect pest-control in rubber plantations and surrounding agro-ecosystems), and magnify threats to bats from human disturbances. Low-intensity management and maintenance of forest buffers around plantations can enable persistence of insectivorous bats in tropical forest-plantation landscapes.

Urban Heat and Urban Cool Islands of Different Land Use Types in Cuiabá, Brazil

Urban heat islands (UHIs), urban cool islands (UCIs), and their varying effects due to land use/land cover types and the local climate were investigated from 2014 to 2015 in three urban zones located in Cuiabá city, Brazil, during hot-humid, and hot-dry periods. All the urban zones were analysed for land use/land cover type, local climate, and rate of warming and cooling based on the difference in air temperature (ΔT) between the urban zones and the rural zone located outside the urban perimeter. The annual UHI effect in all the urban zones exhibited varying intensities during the day, with the highest daytime intensity recorded after the sunrise. The duration of UHI effect varied with land use/land cover type; a consequence of high built-up density, verticalization, waterproof surface, and other peculiarities of urban areas. In the urban zones with high built-up density, the duration of UHI effect was observed for up to 24 h, while in the urban areas with low built-up density, the maximum duration of UHI effect was 8 h. On an average, during the daytime, the urban zone with approximately 70% of vegetation cover and water bodies recorded a UCI value of approximately –8 °C, whereas the urban zones with approximately 80% waterproof surface and bare land recorded a UCI value close to +2 °C during the hot-dry and hot-humid periods. The results indicate that land use and land cover types directly influence UHI intensity.

Assessing the Potential of Solar PV Installation based on Urban Land Cover Analysis

This paper presents a technical assessment of solar photovoltaic (PV) installation potential in urban areas based on its urban land cover type, using a case study of Indonesian cities including Yogyakarta, Kupang and Tomohon. The assessment was performed using a free online application to assess the urban land cover types, i-Tree Canopy. This application can be used to identify and distinguish urban land cover types such as building rooftop, vegetation, grass, soil, road and water, which then can be used to assess the suitable area for Solar PV installation. Additionally, solar photovoltaic power output data from Global Solar Atlas is used to calculate potential energy production from PV installations in each city. The result shows that in an urbanised city such as Yogyakarta, the most suitable PV installation is in building rooftops. Meanwhile, Kupang and Tomohon have higher potential for ground-mounted PV installation in bare ground or grass. The approach and result of this study could be used for planners and policymakers to determine city-scale solar PV installation planning to maximise solar energy production. It can also be used to calculate the solar energy estimation using free online applications, which is easy to use and more accessible for stakeholders.

Optimization of Snow-Related Parameters in Noah Land Surface Model (v3.4.1) Using Micro-Genetic Algorithm (v1.7a)

The snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas the LSMs evaluate the snow cover fraction, snow albedo, and snow depth through interaction with atmospheric conditions. When the initially-developed empirical parameters are local or inadequate, we need to optimize the parameter sets for a certain region. In this study, we seek for the optimal parameter values in the snow-related processes – snow cover fraction, snow albedo, and snow depth – of the Noah LSM, for South Korea, using the micro-genetic algorithm and the in-situ surface observations and remotely-sensed satellite data. Snow data from surface observation stations representing five land cover types – deciduous broadleaf forest, mixed forest, woody savanna, cropland, and urban and built-up lands – are used to optimize five snow-related parameters that calculate the snow cover fraction, maximum snow albedo of fresh snow, and the fresh snow density associated with the snow depth. Another parameter, reflecting the dependence of snow cover fraction on the land cover types, is also optimized. Optimization of these six snow-related parameters has led to improvement in the root-mean squared errors by 17.0 %, 8.2 %, and 5.6 % on snow depth, snow cover fraction, and snow albedo, respectively. In terms of the mean bias, the underestimation problems of snow depth and overestimation problems of snow albedo have been alleviated through optimization of parameters calculating the fresh snow by about 45.1 % and 32.6 %, respectively.

Sensitivity of Convection-Permitting Regional Climate Simulations to Changes in Land Cover Input Data: Role of Land Surface Characteristics for Temperature and Climate Extremes

Characterization of climate uncertainties due to different land cover maps in regional climate models is essential for adaptation strategies. The spatiotemporal heterogeneity in surface characteristics is considered to play a key role in terrestrial surface processes. Here, we quantified the sensitivity of model results to changes in land cover input data (GlobCover 2009, GLC 2000, CCI, and ECOCLIMAP) in the regional climate model (RCM) COSMO-CLM (v5.0_clm16). We investigated land cover changes due to the retrieval year, number, fraction and spatial distribution of land cover classes by performing convection-permitting simulations driven by ERA5 reanalysis data over Germany from 2002 to 2011. The role of the surface parameters on the surface turbulent fluxes and temperature is examined, which is related to the land cover classes. The bias of the annual temperature cycle of all the simulations compared with observations is larger than the differences between simulations. The latter is well within the uncertainty of the observations. The land cover class fractional differences are small among the land cover maps. However, some land cover types, such as croplands and urban areas, have greatly changed over the years. These distribution changes can be seen in the temperature differences. Simulations based on the CCI retrieved in 2000 and 2015 revealed no accreditable difference in the climate variables as the land cover changes that occurred between these years are marginal, and thus, the influence is small over Germany. Increasing the land cover types as in ECOCLIMAP leads to higher temperature variability. The largest differences among the simulations occur in maximum temperature and from spring to autumn, which is the main vegetation period. The temperature differences seen among the simulations relate to changes in the leaf area index, plant coverage, roughness length, latent and sensible heat fluxes due to differences in land cover types. The vegetation fraction was the main parameter affecting the seasonal evolution of the latent heat fluxes based on linear regression analysis, followed by roughness length and leaf area index. If the same natural vegetation (e.g. forest) or pasture grid cells changed into urban types in another land cover map, daily maximum temperatures increased accordingly. Similarly, differences in climate extreme indices are strongest for any land cover type change to urban areas. The uncertainties in regional temperature due to different land cover datasets were overall lower than the uncertainties associated with climate projections. Although the impact and their implications are different on different spatial and temporal scales as shown for urban area differences in the land cover maps. For future development, more attention should be given to land cover classification in complex areas, including more land cover types or single vegetation species and regional representative classification sample selection. Including more sophisticated urban and vegetation modules with synchronized input data in RCMs would improve the underestimation of the urban and vegetation effect on local climate.

Holling meets habitat selection: functional response of large herbivores revisited

Background Holling (Can Entomol 91(5):293–320, 1959) was the first to describe a functional response between a predator’s consumption-rate and the density of its prey. The same concept can be applied to the habitat selection of herbivores, specifically, the change in relative habitat use with the change in habitat availability. Functional responses in habitat selection at a home-range scale have been reported for several large herbivores. However, a link to Holling’s original functional response types has never been drawn, although it could replace the current phenomenological view with a more mechanistically based understanding of functional responses. Methods In this study, discrete choice models were implemented as mixed-effects baseline-category logit models to analyze the variation in habitat selection of a large herbivore at seasonal and diurnal scales. Thus, changes in the use of land cover types with respect to their availability were investigated by monitoring 11 land cover types commonly used by roe deer (Capreolus capreolus) in the Bavarian Forest National Park, Germany. Functional response curves were then fitted using Holling’s formulas. Results Strong evidence of non-linear functional responses was obtained for almost all of the examined land cover types. The shape of the functional response curves varied depending on the season, the time of day, and in some cases between sexes. These responses could be referenced to Holling’s types, with a predominance of type II. Conclusions Our results indicate that Holling’s types can be applied to describe general patterns of the habitat selection behavior of herbivores. Functional responses in habitat selection may occur in situations requiring a trade-off in the selection of land cover types offering different resources, such as due to the temporally varying physiological needs of herbivores. Moreover, two associated parameters defining the curves (prey density and predation rate) can aid in the identification of temporal variations and in determinations of the strength of the cost-benefit ratio for a specific land cover type. Application of our novel approach, using Holling’s equations to describe functional responses in the habitat selection of herbivores, will allow the assignment of general land cover attraction values, independent of availability, thus facilitating the identification of suitable habitats.