Prediction of spatial soil organic carbon distribution using Sentinel-2A and field inventory data in Sariska Tiger Reserve

Dynamic and vigorous top soil is the source for healthy flora, fauna, and humans, and soil organic matters are the underpinning for healthy and productive soils. Organic components in the soil play significant role in stimulating soil productivity processes and vegetation development. This article deals with the scientific demand for estimating soil organic carbon (SOC) in forest using geospatial techniques. We assessed distribution of SOC using field and satellite data in Sariska Tiger Reserve located in the Aravalli Hill Range, India. This study utilized the visible and near-infrared reflectance data of Sentinel-2A satellite. Three predictor variables namely Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, and Renormalized Difference Vegetation Index were derived to examine the relationship between soil and SOC and to identify the biophysical characteristic of soil. Relationship between SOC (ground and predicted) and leaf area index (LAI) measured through satellite data was examined through regression analysis. Coefficient of correlation (R ²) was found to be 0.95 (p value < 0.05) for predicted SOC and satellite measured LAI. Thus, LAI can effectively be used for extracting SOC using remote sensing data. Soil organic carbon stock map generated through Kriging model for Landsat 8 OLI data demonstrated variation in spatial SOC stocks distribution. The model with 89% accuracy has proved to be an effective tool for predicting spatial distribution of SOC stocks in the study area. Thus, optical remote sensing data have immense potential for predicting SOC at larger scale.     

Assessment of the impact of biogenic VOC emissions in a high ozone episode via integrated remote sensing and the CMAQ model

In many metropolitan regions, natural sources contribute a substantial fraction of volatile organic compound (VOC) emissions. These biogenic VOC emissions are precursors to tropospheric Ozone (O₃) formation. Because forests make up 59% of the land area in Taiwan Province, China, the biogenic VOC emissions from forests and farmland could play an important role in photochemical reactions. On the other hand, anthropogenic emissions might also be one of the major inputs for ground level O₃ concentrations. Hence, emission inventory data, grouped as point, area, mobile and biogenic VOC sources, are a composite of reported and estimated pollutant emission information and are used by many air quality models to simulate ground level O₃ concentrations. Before using relevant air quality models, the emission inventory data generally require huge amounts of processing for spatial, temporal, and species congruence with respect to the associated air quality modeling work. The fist part of this research applied satellite remote sensing and geographic information system (GIS) analyses to characterize land use/land cover (LULC) patterns, integrating various sources of anthropogenic emissions and biogenic emissions associated with a variety of plant species. To investigate the significance of biogenic VOC emissions on ozone formation, meteorological and air quality modeling were then employed to generate hourly ozone estimates for a case study of a high ozone episode in southern Taiwan, which is the leading industrial hub on the island. To enhance the modeling accuracy, a unique software module, SMOKE, was set up for emission processing to prepare emission inputs for the U.S. EPA’s Models-3/CMAQ. An emission inventory of Taiwan, TEDS 4.2, was used as the anthropogenic emission inventory. Biogenic emission modeling was accomplished by BEIS-2 in SMOKE, with improvement of local LULC data and revised emission factors. Research findings show that the majority of biogenic VOC emissions occur in the mountainous areas and farmlands. However, the modeling outputs show that downwind of the most heavily populated and industrialized areas, these biogenic VOC emissions have less impact on air quality than do anthropogenic emissions.     

Investigating remote sensing indices to monitor drought impacts on a local scale (case study: Fars province,Iran)

As drought occurs in different climates, assessment of drought impacts on parameters such as vegetation cover is of utmost importance. Satellite remote sensing images with various spectral and spatial resolutions represent information about different land covers such as vegetation cover. Hence, the purpose of this study was to investigate the performance of satellite vegetation indices to monitor the agricultural drought on a local scale. In this regard, satellite images including Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) data were used to evaluate vegetation cover and their gradual changes effects on agricultural drought. Fars province in Iran with relatively low precipitation values was selected as the study area. Modified Perpendicular Drought Index (MPDI), MPDI1, Vegetation Condition Index (VCI), Normalized Difference Vegetation Index Anomalies (NDVIA), and Standardized Vegetation Index (SVI), were evaluated to select the remote sensing based index with the best performance in drought monitoring. The performance of such indices were investigated during 13 years (2000–2013) for MODIS and 29 years (1985–2013) for AVHRR. To assess the efficiency of the satellite indices in drought investigation, Standardized Precipitation Index (SPI) data of five selected stations were used for 3, 6, and 9 month periods on August. The results showed that NDVI-based vegetation indices had the highest correlation with SPI in cold climate and long-term timescale (6 and 9 month). The highest correlation values between remote sensing based indices and SPI were acquired, respectively, in 9-month and 6-month time-scales, with the values of 43.5% and 40%. Moreover, VCI showed the highest capability for agricultural drought investigating in different climate regions of the study area. Overall, the results proved that NDVI-based indices can be used for drought monitoring and assessment in a long-term timescale on a local time-scale.

     

1998—2007年新疆植被覆盖变化及驱动因素分析

利用1998-2007年SPOT VGT归一化植被指数(NDVI)数据对新疆植被覆盖的年际和空间变化进行了动态监测,并从气候变化和人类活动双重角度分析了植被覆盖演变的原因.1998-2007年新疆植被覆盖变化经历了2个阶段:1998-2001年植被覆盖严重退化时期;2002-2007年植被覆盖由急剧上升到缓慢下降再到持续升高时期,NDVI明显高于20世纪末期水平.新疆植被覆盖变化存在显著的空间差异,阿尔泰山地森林、巴音布鲁克草原等自然植被NDVI明显退化,农业灌溉区和生态建设地区的植被覆盖明显提高.从不同的土地利用类型来看,沙地和耕地的NDVI上升趋势显著,林地和草地植被的NDVI退化严重.研究表明,新疆植被覆盖变化是气候变化和人类活动共同作用的结果.温度对植被覆盖变化的影响表现为对植被生长年内韵律的控制和春季植被生长期的延长,年降水量的波动式下降是导致新疆植被覆盖变化呈现2个阶段的主导冈素.农业生产水平的提高是新疆农业灌溉区NDVI不断上升的重要原因,同时,近年来大规模实施的生态建设工程所带来的生态效应正在呈现.     

An analysis of vegetation indices in relation to tree species diversity using by satellite data in the northern forests of Iran

Vegetation indices have been introduced for analyzing and assessing the status of quantitative and qualitative characteristics of vegetation using satellite images. However, choosing the best indices to be used in forest biodiversity and vegetation is one of the important problems faced by the users. The purpose of this research is to evaluate six vegetation indices in the analysis of tree species diversity in the northern forests of Iran. The present research uses LISS III sensor data from IRS-P6 satellite. Geometric rectification of images was performed using ground control points, and Chavez model was used for atmospheric correction of the data. The six spectral vegetation indices included NDVI, IPVI, Ashburn Vegetation Index (AVI), TVI, TTVI, and RVI. Shannon–Wiener species diversity index was used to analyze diversity, and the value of the index was calculated in each sample plot. Then, the spectral values of each sample plot were extracted from different bands. The best subset regression was used to analyze the relationship between species diversity and the related bands. The results obtained from the regression showed that polynomial equations under scrutiny as independent variables can assess tree and shrub species diversity better than other bands and compounds used (R ²?=?0.47). The obtained results also indicated a higher capacity in the case of the AVI index for estimating tree species diversity in the under study area.     

Modelling of longterm surface moisture and monitoring vegetation response by satellite in semi-arid Botswana

A daily surface soil moisture model described and tested previously with data from the Phoenix, AR (USA) area, has been applied now to climate data collected in semi-arid Botswana over a multi-year period. The physically based model was calibrated with observed surface moisture data from one growing season and validated with data from another. Good overall agreement (r² = .89) was found between the observed and modelled data. The seasonal variability of precipitation in this region is reflected in the simulated daily surface moisture. The response of the natural savanna vegetation as observed from monthly composites of satellite-derived Normalized Difference Vegetation Index (NDVI) is also monitored for the same period. It is seen that the NDVI peaks at different times during the rainy season from one year to the next, according not only to the total seasonal precipitation, but its temporal distribution as well. It is also seen that there is a delay in intraseasonal vegetation response. While the NDVI is poorly correlated with the current month's average soil moisture, a distinct relationship with the previous month's average soil moisture was found.     

基于LUCC和夜间灯光辐射数据的张掖市甘州区人口空间分布建模

尽管近年来统计数据的生成技术有了很大提高, 但可用的详细人口数据始终难以得到满足.在一些自然-人文要素耦合的建模研究中, 如生态经济集成建模、 环境和健康分析等都需要基于区域尺度的栅格人口分布建模方法.随着GIS和RS技术的发展, 上述建模方法已有较大进步.利用GIS技术, 基于夜间灯光辐射数据和LUCC在象元栅格水平上构建张掖市甘州区人口空间分布. 首先对DMSP夜间灯光辐射数据进行重采样, 通过普通克里金插值获得灯光数据; 然后与LUCC叠加分析, 利用回归分析的方法获取研究区土地利用、 灯光辐射指标和人口统计数据之间的定量关系, 完成空间化.并在乡镇尺度上进行模型检验, 模型总体的调整R²为0.88, 标准误差为400, 为下一步开展时空变化分析提供支持.     

松嫩平原荒漠化的EOS-MODIS 数据研究*

文章应用EOS-MODIS数据对松嫩平原的荒漠化问题进行了研究。首先计算出标准化植被指数(NDVI)和植被覆盖指数(VCI),反演求得荒漠化指数(DI),得到荒漠化面积;采用多种方法和复杂步骤,首次获得了松嫩平原碱质荒漠化、沙质荒漠化的面积,分别为160.30×10⁴hm²和50.56×10⁴hm²,分别占该区面积的24.35 ％ 和7.84 ％ ;应用数字高程模型(DEM)对荒漠化的分布规律和成因进行了分析和讨论,指出松嫩平原以碱质荒漠化为主, 当前荒漠化的发展程度已处于临界状态,对区域可持续发展构成了严重威胁,亟需采取行之有效的防治措施。     

The spatial and temporal variability of sand erosion across a stabilizing coastal dune field

This study aimed at quantifying the temporal and spatial variability in sand erosion and deposition over a coastal dune field in Israel. These were measured monthly over 2 years using 315 erosion pins over four transects that were placed perpendicular to the coastline. Vegetation cover was estimated based on aerial photographs and Landsat satellite images, whereas the relative height was based on a digital elevation model. These variables were calculated for the area upwind (south west) of the erosion pins, at various lengths, ranging from 15 to 400 m. Nine geomorphologic units were defined, five related to active units, and four to stabilized units. In active units at least 65% of the temporal variance in the annual absolute changes in sand level was explained by the index of Resultant Drift Potential, with most of the sand movement occurring during winter storms. Local rainfall had no apparent impact on sand mobility, due to the low coincidence of sand carrying winds and rainfall in Israel during the passage of frontal cyclones. As for the spatial variables, only a weak correlation was found between sand mobility with the distance from the coastline (R² = 18%). Rather, sand erosion and deposition were influenced by vegetation cover and the relative height of an area of 100–200 m upwind. The values of Soil Adjusted Vegetation Index were significantly negatively correlated with annual absolute changes (R² = 40%), whereas the relative height was significantly positively correlated (R² = 36%). Applying a multiple regression model, 68% of the spatial variability in sand mobility was explained. The resulting map of sand activity clearly shows that at this stage of the stabilization process, most of the dunes are now disconnected, and movement of sand grains from the beach or between the dunes, is very limited. These methods can be applied into spatial and temporal models of sand mobility, thus assessing the impact of different management practices on coastal dunes.     

青藏高原地表能量通量的估计

利用1981—2000年逐日气候、植被和土壤基础资料作为输入,以大气—植被相互作用模式（AVIM2）计算了青藏高原0.1°分辨率的年平均地表能量通量的空间分布和季节变化特征。结果显示,年平均地表净辐射通量由高原西南部的100 W/m2减少到东部的70 W/m2左右。高原东南部的林区潜热通量强而感热通量弱,从高原东南向西、向北潜热通量逐渐减少,而感热通量逐渐增大。夏季这种趋势更加显著。冬季除东南部外,高原上广大地区地表能量通量都较低。     

草地植被盖度的多尺度遥感与实地测量方法综述

植被盖度作为一个重要的生态学参数被用在许多气候模型和生态模型中。地表实测和遥感测量是获取植被盖度的两种基本途径。以草地植被盖度的测量为研究对象,综合讨论了目前地表实测和遥感测量常用的方法,分析了它们的优缺点,并对如何提高草地植被盖度的测量精度做出展望。数码相机、高光谱遥感以及多尺度遥感数据的综合使用可能是未来草地植被盖度测量发展的趋势。     

Assessment of spatio-temporal variations in vegetation recovery after the Wenchuan earthquake using Landsat data

The 8.0-magnitude Wenchuan earthquake in 2008 damaged the ecology of northwestern Sichuan, China. This study assessed ecological changes within a few years of the earthquake through satellite observations of vegetation dynamics in the earthquake area. As an ecological indicator, the fractional vegetation cover was extracted using the Normalized Difference Vegetation Index based on multi-year Landsat images and was validated using airborne images. We found that the entire mountainous disaster area had recovered by 68.45 % 3 years after the 2008 earthquake. After rapid recovery of vegetation in 2009, the recovery process slowed. The vegetation recovery rate (VRR) in the area heavily damaged by landslides was slightly lower but nearly that of the entire disaster area. In addition, because of differences in the proportions of soil and rock in the damaged areas, recovery of vegetation in the southwest portion of the study area was slower than in the northeast areas. Topographic analysis of vegetation recovery patterns indicated that damage to vegetation was closely related to slope, while recovery of vegetation was more sensitive to elevation. The landscape analysis showed that the recovery rate (65.21 %) of the excellent vegetation cover type was slower than the overall VRR. This study suggests that vegetation recovery is a slow ecological process and that ecological restoration should be implemented in mountainous regions affected by the earthquake.     

Lineament extraction from multi-resolution satellite imagery: a pilot study on Wadi Bani Malik, Jeddah, Kingdom of Saudi Arabia

The study of structural lineaments is important for mineral exploration, geotectonic and geotechnical studies, and for the mitigation of geologic hazards. The present work deals with the extraction of lineaments from satellite imageries of different spatial resolutions as well as the analysis of these extracted lineaments. Wadi Bani Malik area located to the east of Jeddah city on the Red Sea coastal plain is chosen for such a study. Six types of digital satellite imagery data were used in the present study. These comprise satellite imagery of low spatial resolution (LSR) including Landsat MSS of 80-m resolution, Landsat TM of 30-m resolution, and Landsat TM of 25-m resolution; satellite imagery of moderate spatial resolution (MSR) including Landsat ETM+ panchromatic of 15-m resolution and SPOT panchromatic of 10-m resolution; and satellite imagery of high spatial resolution (HSR) including the Indian Remote Sensing satellite IRS data of 5-m resolution. As expected, the analysis of the extracted lineaments from different data sets shows that the imagery data of HSR of the Indian IRS data give the highest frequency of the extracted structural lineaments (N?=?3,235), while the imagery data of LSR of the Landsat MSS data give the lowest frequency of the extracted lineaments (N?=?89). The imagery data of MSR give moderate frequency (N?=?1,643) in average. Due to the present study, it is recommended to use the imagery data of HSR and MSR for the extraction of structural lineaments for detailed and regional studies, respectively. The imagery data of LSR are not recommended for such studies due to the fact that most of the real structural lineaments framework cannot be extracted; accordingly, it is not useful in the analyses of lineaments for geological purposes.     

Multi-perspective analysis of vegetation cover changes and driving factors of long time series based on climate and terrain data in Hanjiang River Basin,China

The Hanjiang River Basin is the source area of the Middle Route Project of the South-to-North Water Diversion Project, and the vegetation coverage in this basin directly affects the quality of the ecological environment. This study is based on long time series of Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data synthesized over 16 days from 2000 to 2016 in the Hanjiang River Basin. Major climatic data (temperature and rainfall) and topographic data (elevation, slope, and aspect) are employed to analyze the driving forces of NDVI changes. The results demonstrate the following: for the 2000–2016 period, the average annual NDVI is 0.823, with a change trend of 0.025 year^?1. The overall NDVI upstream is higher than that downstream. The average annual value of NDVI upstream is 0.844, with a change trend of 0.036 year^?1, and that of downstream is 0.799, with a change trend of 0.022 year^?1. The spatial distribution of NDVI was significantly increased in the area around the upstream section of the river and near the Danjiangkou Reservoir, and the distribution of NDVI around the central city was significantly reduced. The NDVI was positively correlated with temperature and rainfall, and the impacts differed among different regions. At elevations below 2000 m, the NDVI shows an increasing trend with increasing elevation, and at elevations exceeding 2000 m, the NDVI is negatively correlated with elevation. Slope is positively correlated with the NDVI. The influence of aspect on the NDVI was small.     

Analysing the relationship between drought and soil erosion using vegetation health index and RUSLE models in Godavari middle sub-basin,India

Drought is a natural phenomenon posing severe implications for soil, groundwater and agricultural yield. It has been recognized as one of the most pervasive global change drivers to affect the soil. Soil being a weakly renewable resource takes a long time to form, but it takes no time to degrade. However, the response of soil to drought conditions as soil loss is not manifested in the existing literature. Thus, this study makes a concerted effort to analyze the relationship between drought conditions and soil erosion in the middle sub-basin of the Godavari River in India. MODIS remote sensing data was utilized for driving drought indices during 2000–2019. Firstly, we constricted Temperature condition index (TCI) and Vegetation Condition Index (VCI) from Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) derived from MODIS data. TCI and VCI were then integrated to determine the Vegetation Health Index (VHI). Revised Universal Soil Loss Equation (RUSLE) was utilized for estimating soil loss. The relationship between drought condition and vegetation was ascertained using the Pearson correlation. Most of the northern and southern watersheds experienced severe drought condition in the sub-basin during 2000–2019. The mean frequency of the drought occurrence was 7.95 months. The average soil erosion in the sub-basin was estimated to be 9.88 t ha^?1 year^?1. A positive relationship was observed between drought indices and soil erosion values (r value being 0.35). However, wide variations were observed in the distribution of spatial correlation. Among various factors, the slope length and steepness were found to be the main drivers of soil erosion in the sub-basin. Thus, the study calls for policy measures to lessen the impact of drought and soil erosion.     

Environmental change detection in the central part of Iraq using remote sensing data and GIS

This study aims to assess the potential of several ancillary input data for the improvement of unsupervised land cover change detection in arid environments. The study area is located in Central Iraq where desertification has been observed. We develop a new scheme based on known robust indices. We employ Landsat (multispectral scanner, thematic mapper, and enhanced thematic mapper) satellite data acquired in 1976, 1990, and 2002. We use the Normalized Deferential Vegetation Index, Normalized Differential Water Index (NDWI), Salinity Index (SI), and Eolian Mapping Index. Two new equations were applied for the SI and the NDWI indices. Validation was performed using ground truth data collected in 16 days. We show that such an approach allows a robust and low-cost alternative for preliminary and large-scale assessments. This study shows that desertification has increased in the study area since 1990.     

Spatial variations in methane emissions from natural wetlands in China

Natural wetlands are thought to be one of the largest natural sources of atmospheric methane concentrations. Although numerous studies referred to the rate of methane fluxes in different geophysical regions, only a few had estimates of the overall geographical methane emissions in China. This study estimated the spatial variations of annual methane emissions with the pixel size of 1 km × 1 km from natural wetlands, excluding water surface, in China. The natural wetland areas were extracted from the database of the 2000 land covers, and geophysical divisions were used to represent different climate conditions. Methane emission in every geophysical region was calculated based on methane release factors obtained from an extensive overview of published literature and the data of elevation and vegetation proportion. The estimated annual methane emissions ranged from 0 to 5,702.8 kg per pixel within the area of 1 km², and the spatial variation in methane emissions was strongly correlated with proportion of wetlands in the area. The total methane emission from natural wetland in China ranged from 3.48 to 7.16 Tg (terrogram, unit of weight) per year, with the mean value of 4.94 Tg per year, based on the area 133,000 km² of natural wetlands. Specifically, the wetland in Northeast China had the highest contribution in China (39 %). Inner Mongolia and Qinghai-Tibet highland represented for about 25 and 21 %, respectively. The other 15 % of the measured methane was released in Northwest, North, Central, and South China.     

Vegetation change detection from NOAA polar orbiting satellites

A Method is proposed for detecting vegetation change using global area coverage data from the NOAA-7 Advanced Very High Resolution Radiometer. Other methods of vegetation change detection, such as baseline images, are discussed, but not demonstrated. Vegetation Change Detection Images are produced by combining three separate weekly composites of Channel 2 minus Channel 1 AVHRR data. This compositing method takes the highest value of the differenced data for each week period and each pixel location. Three such weekly composites red imaged by individual red, green and blue CRT color guns respectively and then combined to form a color composite. The Vegetation Change Detection Image has 20 km resolution. It can be used to show an entire continent on a single image when the data point to CRT pixel ratio is 1:1. The vector vegetation change (magnitude and direction) is depicted by the color pattern resulting from the relative amount of red, green and blue placed in the pixel due to the individual weekly color composites. If there is no greenness change at a pixel location the amount of red, green and blue will be the same and a gray scale color results. The VCDI could provide a timely first alert for identifying regional and global agricultural changes.Doubts have been expressed about the reliability of this satellite data because of the unknown (and variable) effects of atmospheric attenuation. A basic qualitative rather than quantitative analysis of the data has been suggested as the correct approach.     

Investigating vegetation biophysical and spectral parameters for detecting light to moderate grazing effects: a case study in mixed grass prairie

Identifying effective vegetation biophysical and spectral parameters for investigating light to moderate grazing effects on grasslands improves management practices on grasslands. Using mixed grasslands as a case study, this paper compares responses of vegetation biophysical properties and spectral parameters derived from satellite images to grazing intensity, and identifies the suitable biophysical and spectral parameters to detect grazing effects in these areas. Biophysical properties including cover, canopy height and Leaf area index (LAI) were measured in three sites with different grazing managements and one benchmark site in 2008 and 2009 in Grasslands PlaceTypeNational Park and surrounding provincial pastures, Canada. Thirteen vegetation spectral indices, calculated by statistically combining different spectral information, were evaluated. The results indicate that canopy height and the ratio of photosynthetically active vegetation cover to non-photosynthetically active vegetation cover (PV/NPV) showed significant differences between ungrazed and grazed sites. All spectral vegetation indices except the canopy index (CI) show significant differences between grazing treatments. Red-Near infrared (Red-NIR) based vegetation indices, such as Modified Triangular Vegetation Index 1 (MTVI1), Soil-adjusted Vegetation Index (SAVI), are significantly correlated to the PV/NPV. Green/Mid-infrared (Green/MIR) related vegetation indices, i.e. Plant Senescence Reflectance Index (PRSI) and Normalized Canopy Index (NCI), show significant correlation with canopy height. Models based on a linear combination of MTVI1 and SAVI were developed for PV/NPV and PRSI and NCI for canopy height. Models that simulated PV/NPV and canopy height show significant correlations with grazing intensity, suggesting the feasibility of remote sensing to quantify light to moderate grazing effects in mixed grasslands.     

Monitoring rangeland ground cover vegetation using multitemporal MODIS data

The aim of the present research is to monitor changes in herbage production during the grazing season in the Semirom and Brojen regions, Iran, using multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) data. At first, various preprocessing steps were applied to a topography map. The atmospheric and topographic corrections were applied using subtraction of the dark object method and the Lambert method. Image processing, including false-color composite, principal component analysis, and vegetation indices were employed to produce land use and pasture production maps. Vegetation sampling was carried out over a period of 4 months during June–September 2008, using a stratified random sampling method. Twenty random sampling points were selected, and herbage production was estimated and verified with the double-checking method. Four MODIS data sets were used in this study. The models for image processing and integrating ground data with satellite images were processed, and the resulting images were categorized into seven classes. Finally, the land covers were verified for accuracy. A postclassification analysis was carried out to verify the seven class change detections. The results confirmed that Normalized Difference Vegetation Index (NDVI) and Soil-Adjusted Vegetation Index (SAVI) maps had a close relationship with the field data. The indices produced with shortwave infrared bands had a close relationship with field data where the ground cover and yields were high. The R ² value observed was 0.85. The changes in the pasture vegetation were high during the growing season in more than 90 % of the pastures. During the growing season, most changes in the pastures belonged to class 5 and 2 in the NDVI and SAVI index maps, respectively.