Vegetation Cover Area Research Articles

黄河三角洲滨海湿地微生物多样性及其驱动因子

微生物在湿地的生物地球化学循环和生态功能调节中发挥着重要作用，对全球气候变化具有重大影响，对维持全球生态系统的健康至关重要。以黄河三角洲滨海湿地为研究对象，通过采集代表性植被群落的土壤表层和部分植物根系，探究土壤微生物群落组成、根际微生物、环境因子及其内在的关联性和影响机制。研究结果表明不同植被覆盖地区微生物多样性存在差异，芦苇区和柽柳区微生物丰度高于泥滩区、碱蓬区和棉田，海漫滩微生物丰度高于河漫滩地和泥滩。土壤微生物菌群结构和多样性显著高于根际：土壤细菌的香农指数约为4-5.5，根际微生物的香农指数约为0-4。土壤细菌主要为厚壁菌门、变形菌门、拟杆菌门和放线菌门，占样品总数的90%以上；而根际细菌主要是蓝藻门、变形菌门和放线菌门，二者在属水平上的菌群结构差异更加明显。环境因子的含量与生境类型有关，SO₄^2-和NO₃^-的相关性最高，植被覆盖区土壤中Mn⁴⁺、Fe³⁺和水解氮的含量低于滩涂裸地。冗余分析（RDA）表明，pH值在小空间尺度上对湿地土壤中细菌群落的影响较小，环境因子在门和属水平的解释率分别为89.7%和86.8%，其中K（23.4%）、NO₂^-（11.8%）、Mn⁴⁺（9.8%）和Na（8.0%）是解释门水平微生物区系结构变化和组成的主要因子。研究为理解湿地微生物多样性与湿地生态系统功能之间的影响机制提供了一个生态学视角，有助于了解黄河三角洲滨海湿地土壤和植物根际的细菌分布特征，对黄河三角洲退化滨海湿地的生物修复具有重要的指导意义。;Microorganisms play an important role in the biogeochemical cycle and ecological function regulation of wetlands. They have a major impact on global climate change and are critical for maintaining the health of the global ecosystem. Taking the coastal wetland of the Yellow River delta as the research object, this study explored the composition of the soil microbial community, rhizosphere microorganisms, environmental factors and their internal correlations and influencing mechanisms by collecting the soil surface layer of representative vegetation communities and part of plant roots. Research results showed that there were differences in microbial diversity among areas with different vegetation covers. The microbial abundance in Phragmites australis and Tamarix chinensis areas was higher than that in mudflat, Suaeda glauca and cotton field. Moreover, the microbial abundance in mudflat was significantly higher than that in washland and floodplain. The structure and diversity of soil flora were significantly higher than those of rhizosphere microorganism: the Shannon index of soil bacteria was about 4-5.5, while that of rhizosphere microorganism was about 0-4. The soil bacteria were mainly Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria, accounting for more than 90% in all samples, while rhizosphere bacteria were mainly Cyanobacteria, Proteobacteria and Actinomycetes, and the differences of flora structure at the genus level were more obvious. The species composition of bacteria in different habitat types and the microbial composition of different sampling sites in the same habitat type were different. The content of environmental factors was related to the habitat type, SO₄^2- and NO₃^- had the highest significance. The content of Mn⁴⁺, Fe³⁺ and hydrolyzed nitrogen in vegetation-covered areas in wetland soil was lower than that in bare mudflats. Redundancy analysis (RDA) showed that pH value had little influence on the bacterial community in wetland soil on a small spatial scale. The explanatory rates of environmental factors at the phylum and genus levels were 89.7% and 86.8%, respectively. K (23.4%), NO₂^- (11.8%), Mn⁴⁺ (9.8%) and Na (8.0%) were the main factors explaining the structural changes and composition of microbial flora at the phylum level. This study provides an ecological perspective for understanding the influence mechanism between wetland microbial diversity and wetland ecosystem function, and help us to understand the distribution and structure of bacteria in the soil and plant rhizosphere of coastal wetlands in the Yellow River delta, which has important guiding significance for the bioremediation of the degraded coastal wetlands in the Yellow River delta.

Estimating Soil Organic Matter Content Using Sentinel-2 Imagery by Machine Learning in Shanghai

Soil organic matter (SOM) plays an important role in the field of climate change and terrestrial ecosystems. SOM in large areas, especially in urban areas, is difficult to monitor and estimate by traditional methods. Urban land structure is complex, and soil is a mixture of organic and inorganic constituents with different physical and chemical properties. Previous studies showed that remote sensing techniques that provide diverse data in the visible-near-infrared (VNIR)-shortwave infrared (SWIR) spectral range, are promising in the prediction of SOM content on a large scale. Sentinel-2 covers the important spectral bands (VNIR-SWIR) for SOM prediction with a short revisit time. Thus, this article aimed to evaluate the capacity of Sentinel-2 for SOM prediction in an urban area (i.e., Shanghai). 103 bare soil samples filtrated from 398 soil samples at a depth of 20 cm were selected. Three methods, partial least square regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The root mean square error (RMSE) of modelling (mRMSE) and the coefficient of determination ( $R^{2}$ ) of modelling $(mR^{2})$ were used to reflect the accuracy of the model. The results show that PLSR has the poorest performance. ANN has the highest modelling accuracy (mRMSE = 7.387 g kg $^{-1}$ , $mR^{2}=0.446$ ). The ANN prediction accuracy of RMSE (pRMSE) is 4.713 g kg $^{-1}$ and the prediction accuracy of $R^{2}~(pR^{2})$ is 0.723. For SVR, the pRMSE is 4.638 g kg $^{-1}$ , and the $pR^{2}$ is 0.732. The prediction accuracy of SVR is slightly higher than that of ANN. The spatial distribution of SOM demonstrates that the value obtained by ANN is the closest to the range of the bare soil samples, and ANN performs better in vegetation-covered areas. Therefore, Sentinel-2 can be used to estimate SOM content in urban areas, and ANN is a promising method for SOM estimation.

Comprehensive Evaluation of Sentinel-2 Red Edge and Shortwave-Infrared Bands to Estimate Soil Moisture

This article aims to explore the applicability of SMMI (soil moisture monitoring index), MSMMI (modified soil moisture monitoring index), PDI (perpendicular drought index), and MPDI (modified perpendicular drought index) in estimating soil moisture (SM) in farmland. The random forest classifier was used to obtain two-stage land cover types maps. The sensitivity of Sentinel-2 spectral bands to the measured SM at a depth of 0-5 cm was optimized by random forest regression. According to the sensitive bands, SMMI and PDI from different feature spaces were constructed to explore their feasibility for monitoring SM under different land cover types. Second, fractional vegetation cover (FVC) in the study area was estimated by nine kinds of FVC estimation models and compared with the measured FVC. The effects of different FVC methods on estimating SM by MSMMI and MPDI were evaluated. The results show that red edge and short-wave infrared (SWIR) bands of Sentinel-2 had irreplaceable effects on the land cover classification. In terms of monitoring SM in bare soil areas, the SM indices with SWIR bands had high correlations with measured SM. For vegetation-covered areas, MSMMI from the FVCgr model (dimidiate pixel model with red edge bands) and the Short wave infrared1-Short wave infrared2 feature space had the highest correlation with the measured 0-5 cm depth SM. Whether vegetation-covered areas or bare soil areas, the combination of red edge and SWIR bands can effectively improve the estimation accuracy of SM. MSMMI can be used as the best SMMI in the study area. Sentinel-2 images, with great potential, can effectively estimate SM at a depth of 0-5 cm in farmland with complex environments.

Socioeconomic development evaluation for Chinese poverty-stricken counties using indices derived from remotely sensed data

ABSTRACT Comprehensively and objectively evaluating the development of poverty-stricken counties is important for poverty alleviation. In this study, we propose a relatively objective and efficient method that utilizes indicators derived from remote sensing images to assess the socioeconomic development of poverty-stricken counties. Four indicators representing the area of developed land, area of forest vegetation cover, area of cultivated land, and average nighttime light intensity were integrated via the entropy method and used to construct a model for evaluation of the socioeconomic development of poverty-stricken counties (ESDPC). Then, 42 impoverished counties in Henan Province and Liangshan Yi Autonomous Prefecture of Sichuan Province in China were selected, and their ESDPC values for 2013, 2015, and 2017 were estimated. The average ESDPC value of the selected poverty-stricken counties increased from 0.29 to 0.34 between 2013 and 2017. The accuracy of this estimate was verified through an assessment based on the comprehensive development index (CDI) model constructed using 10 socioeconomic indices. The correlation coefficient between the ESDPC values and the CDI values was 0.60, and the fitting relationships between the two models and the remotely sensed indicators showed good consistency, indicating the potential for use of these remotely sensed indicators to assess regional socioeconomic development.

Vegetation cover effects on sediment concentration and overland flow under artificial rainfall intensity

Soil erosion depends on a number of factors including rainfall intensity, density of plant cover, and area cover. The objective of this study is to investigate the impact of these factors on flow velocity, overland flow regimes, sediment concentration, and absolute soil detachment. The soil used in this study was sandy remolded agricultural soil. The soil is packed in a tray of 1 m2 fixed on a slope of 3%; five different intensities were simulated under different vegetation cover (density and area). The results indicated that the overland flow velocity with vegetation cover was best described by polynomial function. The mean flow velocity varied from 0.021 to 1.244 m/s. Overland flow regime is subcritical and laminar. However, there are significant relationships between the vegetation cover density and sediment concentration and absolute soil detachment. The sediment concentration ranged from 1.38 to 5.65 kg/m3 whereas the absolute soil detachment ranged from 0.021?10-3 to 1.244?10-3 kg/m2/s. Finally, the vegetation cover presented a good protector to soil sediment from erosion.

Contribution of Sentinel-2/Landsat-8 OLI Images to Extracting Vegetation Cover and Wetlands Area in Urban Zones: Case of the Dakar Region (Senegal)

Urban areas house vegetation cover in several forms, fulfilling several ecological functions like thermal regulator, biodiversity, air quality, etc. However, their extent is often not very well known, especially in African cities, making it sometimes difficult to assess their real impact on the urban ecosystem functioning. This work aims to analyse the capacity of satellite sensors for mapping vegetation and wetlands in urban areas. The data produced by the MSI sensors of Sentinel 2 and OLI of Landsat-8 are used to identify and map the vegetation cover in the Dakar region through a supervised classification with the Support Vector Machine (SVM) algorithm. The results show that it is sometimes not very easy to analyse urban vegetation with high spatial resolution images (HRS) resulting from the configuration of the vegetation in an urban environment, sometimes characterized by isolated trees or small green spaces. This explains why Sentinel-2 data which spatial resolution of 10 meters gives a better result compared to Landsat-8 data which is 30 meters. However, a good rendering is noted for the vegetation around the wetlands area for the two sensors resulting from the high density and the size of the vegetated perimeters in this part of the capital. Overall, there is an underestimation of urban vegetation cover, particularly for Landsat-8. The use of very high spatial resolution images could be necessary to better assess the potential of satellite data for monitoring urban vegetation in Sahelian context.

Modelling the effect of density vegetation coverage and the occurrence of peridomestic infestation by Triatoma infestans in rural houses of northwest of Córdoba, Argentina.

To better understand the dispersion strategies of Triatoma infestans (Klug) (Hemiptera: Reduviidae, Triatominae), we evaluated the spatial effect of infested peridomicile and density vegetation cover in a historically endemic area for Chagas disease. The study was conducted in rural houses of the northwest of Córdoba province, Argentine, during 2012-2013. Active search of triatomines were made in domicile and peridomicile habitats. To characterize vegetation coverage, a thematic map was obtained considering five types of vegetation cover (closed/open forest, closed/open shrubland and cultural land). From each house we extracted the area of vegetation coverage, housing density and infested peridomiciles density. We used generalized linear models to evaluate the effect of these variables on the occurrence of infested peridomicile. According to our results, the probability of a peridomicile to be infested increases by 1.34 (95%CI [0.98; 1.90]) times more when peridomicile structures are in environments with higher housing density and by 1.25 (95%CI [0.84; 1.88]) more times when houses are surrounded by open shrublands. Among the multiple ecological determinants of peridomestic infestation, the influence of vegetation cover has been poorly studied. In this study we discussed the effect of the vegetation as a potential modulator of the dispersion strategies of T. infestans.

An Analysis of Spatio-Temporal Trends of Land Surface Temperature in the Dhaka Metropolitan Area by Applying Landsat Images

Land surface temperature (LST) is a basic determinant of the global thermal behavior of the Earth surface. LST is a vital consideration for the appraisal of gradual thermal change for urban areas to examine the strength of the thermal intensity of the surface of urban heat island (SUHI) and to see how hot the surface of the Earth would be in a particular location. In this respect, the most developed urban city like Dhaka Metropolitan Area (DMA), Bangladesh is considered for estimation of LST, and Normalized Difference Vegetation Index (NDVI) changes trend in more developed and growing developing areas. The focus of this study is to find out the critical hotspot zones for further instantaneous analysis between these two types of areas. The trends of long-term spatial and temporal LST and NDVI are estimated applying Landsat images-Landsat 5-TM and Landsat OLI_TIRS-8 for the period of 1988 to 2018 for DMA and for developed and growing developing areas during the summer season like for the month of March. The supervised classification was used to estimate the land cover categories and to generate the LST trends maps of the different percentiles of LSTs over time using the emissivity and effective at sensor brightness temperature. The study found the change in land cover patterns by different LST groups based on 50th, 75th, and 90th percentile where the maximum LST for the whole DMA went up by 2.48°C, 1.01°C, and 3.76°C for the months of March, April, and May, respectively for the period of 1988 to 2018. The highest difference in LST was found for the most recently developed area. The moderate change of LST increased in the built-up areas where LST was found more sensitive to climate change than the growing developed areas. The vegetation coverage area decreased by 6.74% in the growing, developing areas compared to the developed areas from 1988 to 2018. The findings of the study might be helpful for urban planners and researchers to take up appropriate measures to mitigate the thermal effect on urban environment.

气候变化和人类活动对中国陆地生态系统总初级生产力的影响厘定研究

总初级生产力（GPP）是生态系统植被光合作用生成有机物的能力表征，是生态系统服务功能的基础，关系到区域社会经济可持续发展及区域生态安全。基于生态系统过程模型CEVSA2，应用中分辨率成像光谱仪（MODIS）卫星遥感的叶面积指数数据产品（MCD15A2H），以强迫法构建了遥感数据驱动的模型新版本——CEVSA-RS；基于CEVSA-RS模拟分析了气候变化和人类活动对中国陆地生态系统GPP时空变化的相对影响，从气候潜在总初级生产力（GPP_CL）和现实总初级生产力（GPP_RS）的大小和趋势两方面厘定了人类活动影响。2000至2017年全国平均潜在GPP （1016.36 gC m^-2a^-1）略高于对应现实GPP （962.85 gC m^-2a^-1），但存在明显的空间分异：长江以南大部、秦岭、太行山脉以东以及大兴安岭以东和长白山地区等森林植被覆盖区，现实GPP高于潜在GPP；而西部草地及灌丛等地区现实GPP低于潜在GPP。全国GPP呈显著增加趋势（P<0.05），其中现实GPP的增速（46.04 gC m^-210a^-1）高于潜在GPP的增速（41.46 gC m^-210a^-1），人类活动影响促进GPP增长，主要体现在华南地区和华北平原等地；内蒙古东部、东北平原北部、青藏高原西部等地人类活动呈负面影响。人类活动影响大于气候影响的区域可达全国陆地面积的53%，其中西部生态相对脆弱的草地区人类活动仍为负面影响，这些地区以草定畜，发展草牧业和保护生态，仍然任重道远。;Gross primary productivity (GPP) is the fundament of all ecological services that ecosystems can provide, and its change will influence the sustainable development of regional socio-economic and ecological security. Based on the ecosystem process model CEVSA2, this study applied the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite remote sensing data to construct a new version of the remote sensing data-driven model, CEVSA-RS. The relative impacts of climate change and human activities on the spatial and temporal changes of terrestrial ecosystem gross primary productivity were analyzed based on CEVSA-RS, and the impacts of human activities were determined in terms of both the magnitude and trends of potential gross primary productivity (GPP_CL) driven by climate and actual gross primary productivity (GPP_RS) driven by climate and remote sensing. According to the national average, potential GPP (1016.36 gC m^-2a^-1) was slightly higher than actual GPP (962.85 gC m^-2a^-1) from 2000 to 2017. However, there is an obviously spatial disparity:the actual GPP is higher than the potential GPP in forested vegetation cover areas such as most of the south of the Yangtze River, the east of the Qinling and Taihang Mountains, and the east of the Daxinganling and Changbai Mountain areas. Meanwhile, the actual GPP is lower than the potential GPP in areas such as grassland and shrubland over western China. The GPP of whole Chinese terrestrial ecosystems showed a significant increasing trend (P<0.05), and the increasing speed of actual GPP (46.04 gC m^-210a^-1) exceeded that of potential GPP (41.46 gC m^-210a^-1), which meant human activities positively contributed to the most area in South China and North China Plain, while negatively impacted the ecosystems in eastern Inner Mongolia, northern Northeast Plains and western Tibetan Plateau. The areas where the impacts of human activities are greater than the impacts of climate chang accounted for 53% of the total land area in China. Considering negative impacts of human activities in the western China, as an area of fragile eco-environment, there is a long way to protect its ecosystems while to develop grassland-based husbandry through determining livestock stocking rate according forage production.

基于多源遥感数据的城市热环境响应与归因分析——以深圳市为例

城市热环境是城市局部气候与环境的综合表现，它与土地利用格局密切相关，但是相应的机制研究还不充分。以深圳市为例，首先基于2018年LANDSAT 8遥感影像，采用支持向量机方法和线性光谱混合模型提取土地利用与覆盖度信息，分析了城市土地覆盖对地表温度以及热量收支状况的影响。基于2003-2018的MODIS地表温度数据，进一步研究了深圳市城市热岛现象的时空变化，从地表能量的角度分析城市热岛变化背后的形成机制。结果表明，深圳市地表温度从西北到东南逐渐降低，城市不透水面温度显著高于植被覆盖区域，城市热岛效应明显。不透水面和城市植被共同影响深圳市的地表温度与热量收支状况，不透水地表与感热具有较好的相关性，城市植被与潜热具有较好的相关性。长时间序列分析表明深圳的城市热岛现象在夏季较高而冬季较低，月均热岛强度为2.14℃；对于年际变化，深圳在2003-2018表现出显著的下降趋势。归因分析显示感热通量的影响在深圳起主导作用，这一模式在全年和季节上都较为明显。结果表明深圳市经过高速扩张阶段，目前发展方向是提高建成区的利用效率，该现象强调了热传输在加强城市热岛效应过程中对近地面湍流的干扰作用。本研究可以为缓解热岛效应与景观格局优化研究提供借鉴。;Urban thermal environment is a comprehensive expression of local climate and environment. While it is closely related to land use patterns, its underlying mechanisms are largely unexplored. Urban heat island affects not only regional climate, vegetation growth and air quality, but also human health. Therefore, the urban thermal environment has been regarded as a critical variable. It is important to characterize the spatial and temporal patterns of urban thermal environments and quantify the response of the associated influencing factors. This study uses the city of Shenzhen as an example where rapid urbanization has occurred. Based on LANDSAT 8 remote sensing images collected in 2018, we first extract land cover types and coverage information using support vector machine and multiple linear spectral analysis models. The accuracies of the estimated land covers are acceptable, which provides confidence for further analyses. The impacts of urban land cover on land surface temperature and heat energy component are then investigated. Using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature dataset obtained from 2003 to 2018, this study further explores the spatiotemporal variations of urban heat island in Shenzhen, and analyzes its potential formation mechanisms from the perspective of surface energy balance. Our results show that the land surface temperature gradually decreases from northwest to Southeast of Shenzhen. This is largely attributed to the spatial distribution of impervious surfaces and the urban vegetation. Specifically, the land surface temperature of impervious surfaces is substantially higher than that of vegetation-covered area, implying that the urban heat island effect is obvious in Shenzhen. Impervious surface and urban vegetation jointly affect the surface temperature and heat budget through, for example, latent heat flux and sensible heat flux. Impervious surface is more correlated with sensible heat flux while urban vegetation is more correlated with latent heat flux. Obvious differences occur in sensible heat and latent heat among land cover types, which provides implications for the formation and elimination of urban heat island. Time series analysis regarding the period of 2003-2018 illustrates that the urban heat island effect in Shenzhen is stronger during summer season whereas relatively weaker during winter season. The urban heat island effect is remarkably exhibited using the time series dataset, with an average monthly intensity of 2.14 ℃. Regarding the internal variation, the downward trend in urban heat island intensity is shown from 2003 to 2018. This negative trend is significantly present during all seasons except spring, with a change rate of approximately 0.1 ℃/annual. Attribution analysis based on MODIS dataset and FLDAS flux dataset reveals that sensible heat flux plays a more important role in the urban heat island of Shenzhen in comparison to latent heat flux. This is consistent in the both annual and seasonal scales, and the relative contribution of latent heat flux and sensible heat flux is 42% and 58%, respectively. Results indicate that after a rapid urban expansion period in Shenzhen, the current development focus is to improve the utilization efficiency of built-up areas. This emphasizes the interference of heat transfer with the near-surface turbulence that strengthens the urban heat island effect. Our study provides reference values for mitigating the urban heat island effect and optimizing landscape patterns.

Phytoplankton Species in Linkage to Water Quality and Landsat Data Indices at the Southern Part of Burullus Lake, Egypt

In aquatic ecosystems, phytoplankton assemblages are mostly affected by different water quality parameters. In March 2020, water and phytoplankton samples were collected from seven geo-referenced stations distributed along the southern part of Lake Burullus, in front of drains. Physio-chemical parameters (temperature T°C, pH, dissolved oxygen DO, Salinity, nitrite NO2 , phosphate PO4 , ammonia NH4 , and silicates SiO4 ) were analyzed in water samples. Phytoplankton samples were collected using a net mesh of a size of 20 µm. The most abundant classes are Chlorophyceae, Cyanophyceae, and Euglenophyceae in stations 7, 2, and 6, respectively. The difference between classes and species is based on the different discharge of wastes, the high load of organic matter, and salinity. Results of the average water quality index AWQI showed low water quality in front of drainage waters. The vegetative cover increases on the eastern side. Landsat images were acquired, treated, and processed for concluding three important indices along with the three sectors of the lake. These indices are normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and phytoplankton turbidity index (PTI). The NDVI showed that the vegetation-covered areas of about 11.3, 2.6, and 1.36 km2 , and NDWI showed water areas of about 105.09, 153.25, and 43.57 km2 for eastern, middle, and western parts, respectively. PTI indicated that nearly 55.3% of phytoplankton turbidities concentrated towards the western side of the lake, followed by 44.21% at the middle part and 25.14 % on the eastern side. It is concluded that water quality nearby drains are bad, where the phytoplankton turbidities as observed from Landsat images were increased towards the southwestern parts.

ISBA-MEB (SURFEX v8.1): model snow evaluation for local-scale forest sites

Abstract. Accurate modeling of the effect of snow cover on the surface energy and mass fluxes is required from land surface models. The Interactions between Soil–Biosphere–Atmosphere (ISBA) model uses a composite soil–vegetation approach that has limitations when representing snow and soil phase change processes in areas of high vegetation cover since it does not explicitly represent the snowpack lying on the ground below the canopy. In particular, previous studies using ISBA have pointed out that the snowpack ablation tends to occur to early in the season in forest regions in the Northern Hemisphere. The multi-energy balance (MEB) version of ISBA has been developed recently, to a large degree, to address this issue. A vegetation layer, which is distinct from the soil, has been added to ISBA and new processes are now explicitly represented, such as snow interception and an understory litter layer. To evaluate the behavior of this new scheme in a cold forested region, long-term offline simulations have been performed for the three BERMS forest sites located in Saskatchewan, Canada. It is shown that the new scheme leads to an improved energy budget representation, especially in terms of the ground and sensible heat fluxes, with decreases in root-mean-square error (RMSE) of 77 % and 18 %, respectively. A positive impact for soil temperatures, consistent with the improvement of the ground heat flux, is obtained, particularly in terms of bias, which is reduced from −6.2 to −0.1 K at a 10 cm soil depth on average for the three sites and 12 studied years. The impact of using MEB on the snowpack simulation is a better agreement with observations during the snow season, especially concerning the last day of snow in the season: errors are on the order of 1 d averaged over the three sites and all of the years using MEB, which represents a reduction in error of 20 d compared to the composite scheme. The analysis shows that this improvement is mostly caused by the ability of MEB to represent a snowpack that nearly completely covers the soil below the canopy and that decouples the soil from the atmosphere, while keeping a close coupling between the vegetation and the atmosphere.

Combined Sentinel-1A With Sentinel-2A to Estimate Soil Moisture in Farmland

In this article, seven filter algorithms were compared. The Lee sigma method was more suitable for estimating soil moisture (SM) than the other filtering methods under different land cover types. First, we used a combination of roughness and the dual-polarized Sentinel-1A backscattering coefficients (VV and VH) to estimate SM in bare soil areas. Second, we employed water cloud model (WCM) to remove the influence of vegetation signals on the land surface backscattering and estimate SM in vegetation-covered areas. SM was also retrieved by modified soil moisture monitoring index (MSMMI) and modified perpendicular drought index (MPDI) of Sentinel-2A images. The results show that MSMMI can more accurately monitor SM in bare soil areas, which was slightly better than synthetic aperture radar (SAR) results. The SAR backscattering coefficients after the removal of vegetation influence by WCM can more precisely estimate SM in vegetation-covered areas, which is significantly better than MSMMI and MPDI, especially in high vegetation-covered areas. Optics and SAR differ in their abilities to estimate SM under different land cover, but the powerful fitting ability of machine learning can make full use of their advantages. We employed the generalized regression neural network (GRNN), support vector regression (SVR), random forest regression (RFR), and deep neural network (DNN) algorithms to estimate SM combining Sentinel-1A with Sentinel-2A images. The estimation accuracies of SM by regression algorithms were higher than those by the semiempirical SAR and optical models. The accuracy of estimated SM by DNN was higher than that of GRNN and RFR, which were better than SVR.

Feasibility of terrestrial laser scanning for quantification of vegetation structure parameters of restored sandy land in the southern Qinghai–Tibeten Plateau

AbstractThe efficiency of ecosystem restoration of degraded land is typically assessed using vegetation structure parameters (VSP). However, the field measurement of VSP, particularly in the Qinghai–Tibet Plateau (QTP), which is a problematic area for ecological restoration because of its harsh environment, is generally time‐consuming and labour intensive. Therefore, new methods for VSP measurement should be developed. In this study, VSP were quantified at the individual scale, including their number, height, and crown width. Furthermore, the quadrat scale, such as vegetation coverage (VC) and leaf area index (LAI) on a restored sandy plot (100 m × 100 m) in the middle reaches of the Yarlung Zangbo River basin, is quantified using terrestrial laser scanning. Thereafter, the TLS‐derived VSP are validated by field measurements. To extract the VSP of low‐height vegetation (shrubs) at the individual scale, three approaches are tested: the manual measurement of VSP from TLS data, individual vegetation segmentation (IVS) without specific root locations, and IVS with specific root locations. The results indicate that the VSP of shrubs can be manually derived from the TLS data and are consistent with field measurements. By specifying the root locations before performing the IVS, the identification rate of the number of shrubs can be improved significantly from 40.3% to 100%. The height extracted by the IVS corresponds better with the manual measurement (R2 &gt; 0.99) than with the crown width (R2 &lt; 0.65). At the quadrat scale, the optimal resolution is analyzed to extract the VC. The analysis suggests that the optimal resolution for extracting the VC based on the canopy height model is in the 1‐2 cm range. In extracting the LAI using the voxel‐based method, it is discovered that the appropriate voxel size is linearly dependent on the mean point spacing (y = 2.143x, R2= 0.826). This study provides useful guidance for monitoring the effectiveness of ecological conservation and restoration on the QTP.

New approach for obtaining the C-factor of RUSLE considering the seasonal effect of rainfalls on vegetation cover

We present a new approach for calculating the C-factor of RUSLE considering the effect of low-reflectance vegetation cover areas on the reduction of the effects on erosion caused by rainfall seasonality. For this, we propose the coefficients Cr2 (rescaled 2) and C-PC (Precipitation Correction), which represent the C-factor, and an adaptation in NDVI calculation, according to the seasonality of precipitation (NDVI-PC). The Cr2 factor is used when there is no seasonal effect of rainfall on vegetation, while the C-PC factor is calculated for localities under the influence of seasonality, from NDVI-PC. The proposed approaches were tested using different satellites images in the Palmares-Ribeirão do Saco watershed, Rio de Janeiro, Brazil. The values of Cr2 and C-PC factors were compared to the Cr factor (rescaled) and to mean values from the literature for different land covers. Our results indicated that the Cr2 factor represents an improvement in accuracy in relation to Cr by considering specific values of the studied area to normalize the data without generalizations. Furthermore, the C-PC factor is able to simulate the effect of seasonality, providing more realistics values of soil loss by the RUSLE as a function of the proportion of area affected by the rainfall seasonality obtained from NDVI-PC. We conclude that both Cr2 and C-PC factors generate values similar of the C-factor observed in the literature, and therefore are able to provide better soil loss estimation than that using the Cr factor.

A feasibility study of uninhabited aircraft systems for rapid and cost-effective plant stress monitoring at green stormwater infrastructure facilities

Abstract Vegetation health monitoring is key to identifying early signs of water stress, pollutant-induced toxicity, and plant diseases in green urban stormwater facilities. However, rigorous monitoring to collect accurate quantitative data is an expensive and time-consuming process. This paper examines the feasibility of using uninhabited aircraft systems (UAS), in comparison to standard ground-based methods, for monitoring biomass and primary production in two bioswale cells at an urban stormwater facility. Implementation of the UAS-based approach involved flight planning in an urban area to meet resolution requirements of bioswale imagery obtained from near-infrared and red-green-blue cameras. The resulting normalized difference vegetation index (NDVI) estimated from UAS data was tracked over a 2-month period during the transition from spring to summer, showing the spatial distribution of NDVI and the change in vegetation coverage areas over time. In comparison, ground-based measurements of the fraction of intercepted photosynthetically active radiation (PAR) presented multiple practical challenges during implementation in the field, leading to over- and underestimates of intercepted PAR. Overall, UAS-derived NDVI was found to be a valuable reflectance-based, vegetation health-monitoring methodology that can be used by utilities and cities for practical, cost-effective, and rapid assessment of vegetation stress and for long-term maintenance in green stormwater facilities.

Spatial and temporal variation Trend Analysis of vegetation cover in Urban agglomeration of Pearl River Delta from 2000 to 2015

Based on MODIS NDVI remote sensing data, the methods of regression analysis, CV stability analysis, Hurst exponent analysis are used to invert the spatio-temporal variation trend of vegetation cover in the Pearl River Delta region from 2000 to 2015. On this basis, the future change trend of vegetation cover was predicted. The results indicate that (1) the vegetation coverage in the Pearl River Delta was on a rising trend in general from 2000 to 2015 (0.029/10 years). (2). The vegetation change in The Pearl River Delta region was mainly significantly improved (39.73%), and significantly degraded and slightly degraded areas accounted for only 13.22%, which were staggered with significantly improved regions. (3) Degree of vegetation cover fluctuation in cities and surrounding areas has significant spatial consistency with degraded areas of vegetation cover, which indicates that there is a certain correlation between the change of vegetation cover around the city and human activities. (4) Combined with the analysis of future and past trends, the condition that vegetation cover firstly degrades and then improves will occur. While vegetation degradation may occur in non-urban concentrated areas without good ecological planning. The research results will be helpful to understand the trend and spatial distribution of vegetation in the Pearl River Delta region.

Effects of Land Cover Changes on Net Primary Productivity in the Terrestrial Ecosystems of China from 2001 to 2012

The 2001–2012 MODIS MCD12Q1 land cover data and MOD17A3 NPP data were used to calculate changes in land cover in China and annual changes in net primary productivity (NPP) during a 12-year period and to quantitatively analyze the effects of land cover change on the NPP of China’s terrestrial ecosystems. The results revealed that during the study period, no changes in land cover type occurred in 7447.31 thousand km2 of China, while the area of vegetation cover increased by 160.97 thousand km2 in the rest of the country. Forest cover increased to 20.91%, which was mainly due to the conversion of large areas of savanna (345.19 thousand km2) and cropland (178.96 thousand km2) to forest. During the 12-year study period, the annual mean NPP of China was 2.70 PgC and increased by 0.25 PgC, from 2.50 to 2.75 PgC. Of this change, 0.21 PgC occurred in areas where there was no land cover change, while 0.04 PgC occurred in areas where there was land cover change. The contributions of forest and cropland to NPP exhibited increasing trends, while the contributions of shrubland and grassland to NPP decreased. Among these land cover types, the contributions of forest and cropland to the national NPP were the greatest, accounting for 40.97% and 27.95%, respectively, of the annual total NPP. There was no significant correlation between changes in forest area and changes in total annual NPP (R2 &lt; 0.1), while the correlation coefficient for changes in cropland area and total annual NPP was 0.48. Additionally, the area of cropland converted to other land cover types was negatively correlated with the changes in NPP, and the loss of cropland caused a reduction in the national NPP.

Assessment of red-edge vegetation descriptors in a modified water cloud model for forward modelling using Sentinel – 1A and Sentinel – 2 satellite data

ABSTRACT This study investigates the potential of different vegetation descriptors (V) in the modified water cloud model (MWCM), with a focus on comparing the Red – Edge vegetation indices (VI) based and other vegetation descriptors using Sentinel – 1A and Sentinel − 2 satellite data. In order to reduce the influences of vegetation and roughness effectively, a soil geometrical model of equivalent roughness was coupled with a MWCM for the simulation in forward modelling. The optimum value of vegetation extinction coefficient (K VI) and dense vegetation indices () of modified Beer’s law were calculated using non-linear least square optimization technique. After the parameterization of MWCM, the five different types of V for wheat crop were tested for the simulation of backscattering coefficients () at VV polarization. The higher statistical performance indices like coefficient of determination (R2 = 0.96), root-mean-square error (RMSE = 0.17 (dB)) and Nash sutcliffe efficiency (NSE = 0.93) were found for the case of Red-Edge-based vegetation descriptors (for VI = NDVIRE) than others in MWCM. Therefore, this methodology reveals that the VI = NDVIRE by modified Beer’s law can be used effectively as the vegetation parameter in the MWCM for accurate simulation in forward direction over vegetation-covered areas.

Assessing the expansion of saline lands through vegetation and wetland loss using remote sensing and GIS

Soil is a non-renewable and dynamic source that most of the human food supply comes from the soil, and one of the factors affecting soil degradation is soil salinity. The aim of this study was to predict the salinity expansion of Golpayegan's plain through estimated losses in vegetation cover and wetland area, using indices, and spatial techniques. The first 22 Landsat images were selected from 1985 to 2016. Two sets of maps, Soil Salinity Index (SSI) and Normalized Difference Vegetation Index (NDVI) were extracted from the Landsat images. By using of two indices, the classification of these lands was carried out. These maps with six classes, vegetation, and salinity were entered into the Land Change Modeler (LCM). Smart modeling was continued by Multi-Layer Perceptron (MLP) Neural Network method. After validation of the model, the transition potential probability maps were prepared. By using the Cellular Automata (CA) Markov method, 2025 forecast map was obtained. The results showed that among different salinity indices, SSI6 was the best index. In the study area, the land changing to high salinity has occurred such that the low salinity classes are first obtained and gradually changed to medium and high salinity classes. There is a reverse relationship between vegetation density and soil salinity, that is, with wetland drying up and increasing salinity, the density of vegetation decreases. Decision-makers should be control of drainage networks and water resources exploitation to control the salinity expansion.