Accurate Quantification of Grassland Cover Density in an Alpine Meadow Soil Based on Remote Sensing and GPS

The principles of remotely estimating grassland cover density in an alpine meadow soil from space lie in the synchronous collection of in situ samples with the satellite pass and statistically linking these cover densities to their image properties according to their geographic coordinates. The principles and procedures for quantifying grassland cover density from satellite image data were presented with an example from Qinghai Lake, China demonstrating how quantification could be made more accurate through the integrated use of remote sensing and global positioning systems (GPS). An empirical model was applied to an entire satellite image to convert pixel values into ground cover density. Satellite data based on 68 field samples was used to produce a map of ten cover densities. After calibration a strong linear regression relationship (r2 = 0.745) between pixel values on the satellite image and in situ measured grassland cover density was established with an 89% accuracy level. However, to minimize positional uncertainty of field samples, integrated use of hyperspatial satellite data and GPS could be utilized. This integration could reduce disparity in ground and space sampling intervals, and improve future quantification accuracy even more.

Development of a large-scale remote sensing ecological index in arid areas and its application in the Aral Sea Basin

The Aral Sea Basin in Central Asia is an important geographical environment unit in the center of Eurasia.It is of great significance to the ecological protection and sustainable development of Central Asia to carry out dynamic monitoring and effective evaluation of the eco-environmental quality of the Aral Sea Basin.In this study,the arid remote sensing ecological index(ARSEI)for large-scale arid areas was developed,which coupled the information of the greenness index,the salinity index,the humidity index,the heat index,and the land degradation index of arid areas.The ARSEI was used to monitor and evaluate the eco-environmental quality of the Aral Sea Basin from 2000 to 2019.The results show that the greenness index,the humidity index and the land degradation index had a positive impact on the quality of the ecological environment in the Aral Sea Basin,while the salinity index and the heat index exerted a negative impact on the quality of the ecological environment.The eco-environmental quality of the Aral Sea Basin demonstrated a trend of initial improvement,followed by deterioration,and finally further improvement.The spatial variation of these changes was significant.From 2000 to 2019,grassland and wasteland(saline alkali land and sandy land)in the central and western parts of the basin had the worst ecological environment quality.The areas with poor ecological environment quality are mainly distributed in rivers,wetlands,and cultivated land around lakes.During the period from 2000 to 2019,except for the surrounding areas of the Aral Sea,the ecological environment quality in other areas of the Aral Sea Basin has been improved in general.The correlation coefficients between the change in the eco-environmental quality and the heat index and between the change in the eco-environmental quality and the humidity index were–0.593 and 0.524,respectively.Climate conditions and human activities have led to different combinations of heat and humidity changes in the eco-environmental quality of the Aral Sea Basin.However,human activities had a greater impact.The ARSEI can quantitatively and intuitively reflect the scale and causes of large-scale and long-time period changes of the eco-environmental quality in arid areas;it is very suitable for the study of the eco-environmental quality in arid areas.

Landscape ecology of the region around Qinghai Lake,Qinghai Province of China based on romote sensing

A region surrounding Qinghai Lake was chosen as the study area and nine ecological landscape types that were recognized based on Landsat TM image classification of land cover/use types along with the ancillary data, and their ecological features and the measures dealing with the eco environmental problems are presented in this paper. The study has shown that using this approach the ecological landscape types in a region like the study area can be readily recognized and their ecological features can be rather accurately derived. Moreover, the deterioration in near all landscape ecological types in the area is quite serious. Therefore, effective and proper measures have to be taken in order to realize a sustainable development of the region.

Analysis of Ecological Environment Changes in Linze from 2000 to 2017 Based on Remote Sensing Images

With the support of RS and GIS technology,the ecological environment of Linze was evaluated and the changes of ecological environment were analyzed from the spatial scope of ecosystem with the remote sensing images in 2000 and 2017 as the information source.The results showed that the areas with excellent,relatively good and relatively poor ecological environment in Linze showed a decreasing trend from 2000 to 2017,which decreased by 262.50,7,156.25 and 12,256.30 hm^2,respectively.From the landscape patch pattern,the patch density in areas with relatively good ecological environment increased the fastest from 2000 to 2017,which was 13.05 pieces/hm^2.The largest plaque index in areas with relatively good ecological environment decreased by 1.314%,and the dominance of large plaque in the region decreased.The assessment results of ecological environment based on remote sensing demonstrated that the ecological environment in Linze was generally healthy from 2000 to 2017,and the deterioration was improved.Among them,the area transformed from relatively poor ecological environment to other types was the largest,which was 17,512.92 hm^2,and the proportion of the area transformed into general ecological environment was the highest,accounting for 80.7%of the transformed area of the region.

Monitoring the frozen duration of Qinghai Lake using satellite passive microwave remote sensing low frequency data

The Qinghai Lake is the largest inland lake in China.The significant difference of dielectric properties between water and ice suggests that a simple method of monitoring the Qinghai lake freeze-up and break-up dates using satellite passive microwave remote sensing data could be used.The freeze-up and break-up dates from the Qinghai Lake hydrological station and the MODIS L1B reflectance data were used to validate the passive microwave remote sensing results.The validation shows that passive microwave remote sensing data can accurately monitor the lake ice.Some uncertainty comes mainly from the revisit frequency of satellite overpass.The data from 1978 to 2006 show that lake ice duration is reduced by about 14―15 days.The freeze-up dates are about 4 days later and break-up dates about 10 days earlier.The regression analyses show that,at the 0.05 significance level,the correlations are 0.83,0.66 and 0.89 between monthly mean air temperature(MMAT) and lake ice duration days,freeze-up dates,break-up dates,respectively.Therefore,inter-annual variations of the Qinghai Lake ice duration days can significantly reflect the regional climate variation.

Classification of Desertification on the North Bank of Qinghai Lake

In this paper,RS,GIS and GPS technologies are used to interpret the remote sensing images of the north shore of Qinghai Lake from 1987 to 2014 according to the inversion results of vegetation coverage(FVC),albedo,land surface temperature(LST),soil moisture(WET)and other major parameters after image preprocessing,such as radiometric correction,geometric correction and atmospheric correction.On this basis,the decision tree classification method based on landsat8 remote sensing image is used to classify the desertification land in this area,and the development and change of desertification in this period are analyzed.The results show that the fluctuation of desertification land area in this area increased during the study period,but from 2003 to 2014,the land area of mild desertification,moderate desertification and severe desertification landwere respectively decreased 0.92,145.89 and 29.39 km2,while the area of serious desertification land still has a slow increasing trend.Whether the driving force of desertification change trend in this area is caused by human factors or global change needs to be further studied.

Evolution of Desertification Types on the North Shore of Qinghai Lake

Land desertification is a widely concerned ecological environment problem.Studying the evolution trend of desertification types is of great significance to prevent and control land desertification.In this study,we applied the decision tree classification method,to study the land area and temporal and spatial change law of different types of desertification in the North Bank of Qinghai Lake area from 1987 to 2014,based on the current land use situation and TM remote sensing image data of Haiyan County,Qinghai Province,The results show that the area of mild desertification land and moderate desertification land in the study area has decreased,while the area of severe desertification land and extreme desertification land has increased significantly in the past 30 years.The area of desertification land decreased by 4.02 km2,of which the area of mild and moderate desertification land decreased by 39.73 km2 and 36.8 km2 respectively,and the area of severe and extreme desertification land increased by 32.78 km2 and 39.73 km2 respectively.As for the mutual transformation relationship,the transformation from severe desertification land to extreme desertification land is the main,and the junction of severe desertification land and extreme desertification land is the sensitive area of transformation.In the north shore of Qinghai Lake,the sandy land tends to expand eastward.The research provides reference basis for local land desertification monitoring,and has a great guidance for local effective land desertification and soil and water conservation.

农田防护林林网景观结构定量评价与分析

以东北地区的“三北”防护林工程重点建设区为研究对象,选取林网的带斑比、连接度、环度、优势度等作为评价指标,利用高分辨率的ZY-3影像,根据景观生态学原理及地理信息系统技术,对2021年实际林网和合理状态下林网的景观指标值进行定量评估及对比,并分析现有农田防护林景观结构参数的空间分布差异性。结果表明:该区域的防护林数量与面积基本达到优质林网的要求,并且分布较均匀,但是主副林带条数不足,成型状况较差,且形成的闭合网格太少,削弱了林网的防护功能。从林网数量、分布以及成型状况等方面对防护林工程进行了宏观评价,指明了下阶段建设工作方向,为该区域防护林的管理和更新提供参考。

Landscape dynamitic change in Mu Us Desert derived from landsat TM data

Landscape ecology emphasizes large areas and ecological effects of the spatial patterning of ecosystem. Recent developments in landscape ecology have emphasized the important relationship between spatial patterns and many ecological processes. Quantitative methods in landscape ecology link spatial patterns and ecological processes at broad spatial and temporal scales. In turn the increased attention on temporal change of ecosystem has highlighted the need for quantitative methods that can analyze patterns. This research applies quantitative methods——change detection to assess the ecosystem temporal change in the arid and semiarid area. Remote sensing offers the temporal change of ecosystem on landscape characteristics.

1980-2020年长江中游阳新县湖群湿地景观格局演变及影响因素

为探究县域经济发展对湿地景观格局演变的影响,以长江中游阳新县湖群湿地为例,运用景观生态学的理论与方法,分析该区域湿地景观的时空演变格局及影响因素。结果表明:1)1980-2020年间阳新县湿地面积总体上呈现缩减状态,从851.03 km^(2)缩减至836.39 km^(2),其中人工湿地面积呈递减趋势,自然湿地面积总体呈增加趋势。人工湿地中水田面积损失最多,达43.72 km^(2),主要转移为旱地、林草地、湖泊和建设用地;自然湿地中湖泊面积增加了22.09 km^(2),主要由水田和旱地转移而来。2)阳新县湿地景观总体上趋于破碎化,其中水田的破碎度最大,水田和湖泊是阳新县湿地景观中的优势景观地类,湖泊和河流的聚集度相对较高。3)在降雨量充足的地方,降雨量对湿地面积的影响并不显著;气温增高,人口和第二、三产业的用地需求对水田的侵占是造成人工湿地水田面积减少的主要因素。退田还湖、退垸还湖等湿地保护政策是自然湿地湖泊面积增加的主要因素。4)对比阳新县湖群湿地与武汉城市湖群湿地景观格局的变化情况,发现县域湖泊因湿地保护政策得到有效恢复,而城市由于用地需求大,使得城市湖泊面积缩减并且湖泊破碎化。

厦漳泉新兴都市区生态质量遥感评价

城市生态系统和地表生物物理组分之间存在复杂的潜在非线性关系,这使得城市聚集的都市区生态质量客观评估遇到了技术挑战。针对性选取干度、路网密度、土地利用程度综合指数、植被覆盖率、生态连接度、湿度、热度、生态系统服务价值指数8个城市生态重要影响因子,利用主成分分析进行指标集成建立城市生态评价遥感指数UEQ,并分析不同等级UEQ与地表景观格局指标的定量关系,评价区域一体化背景下厦漳泉新兴都市区生态状况。结果表明,受8个指标综合作用,厦漳泉都市区UEQ均值为0.558,生态指数等级为中等。对比UEQ指数的8个指标分量,对生态质量起正向作用的生态系统服务价值指标值较低,对生态质量起负向作用的干度、土地利用程度综合指数和热度指标值较高,其它4个指标贡献度较低。从空间分布来看,厦漳泉都市区生态质量总体呈“西高东低”的空间格局。东部核心区高不透水面覆盖地区,生态用地的生态效益较低,其生态质量比都市区整体平均值低0.212;西部绿色山地保育带,完整连续的生态用地发挥的生态效益也更高,其平均生态质量比总体平均值高0.104。不透水面斑块小、斑块内部连通度低且斑块聚集度低的区域,其生态质量好于不透水面斑块大、斑块内部高度连通且斑块高度聚集的区域。

基于改进的遥感生态指数的黄河三角洲生态环境质量时空变化分析

在黄河流域生态保护和高质量发展的背景下,为监测黄河三角洲整体生态环境变化,为区域未来发展提供合理建议,以黄河三角洲为研究区,选取5期Landsat影像为数据源,在遥感生态指数(RSEI)模型基础上,用盐度指数(SI)代替干度指标,同时引入空气质量指标(DI)构建改进的遥感生态指数(MRSEI),并结合景观指数,借助空间自相关分析方法,通过地理探测器进行驱动因子分析,定量评价黄河三角洲生态环境时空分异特征。结果表明:(1)与RSEI相比,MRSEI更接近生态环境状况指数(EI)值,评价结果与真实地表情况更为接近,更适用于黄河三角洲生态环境质量评价。(2)黄河三角洲生态环境表现为略微变差,生态指数均值由0.53降到0.51。生态环境质量差的区域主要集中在沿海滩涂附近,生态环境质量较好的区域分布在西南部与黄河沿岸地区,随着距海岸距离缩小生态环境质量逐渐降低。(3)不同等级的遥感生态指数斑块破碎度都不断上升,景观格局趋于破碎化。(4)各时期区域生态指数空间分布都存在显著的空间自相关性,局部以高-高聚集与低-低聚集为主,与MRSEI分布变化趋于一致。(5)土地利用是影响区域生态环境空间分异性的主要因素,各因子间的交互作用均表现为双因子增强。

基于冷暖季放牧区划分的玉树州草畜平衡估算及变化分析

玉树州作为三江源高寒生态屏障区以牧为主农牧业结合发展的核心地区,草畜平衡不仅关系生态屏障安全,也对实现科学的畜牧业经济管理具有重要的指导意义。因此,本研究以探究高寒生态屏障区天然草地的草畜平衡变化为目的,选择玉树州为研究区,利用2014-2019年MOD13A1 NDVI产品数据和MOD17A3H NPP产品数据、统计数据、植被类型数据,在划分县域冷暖两季放牧区的基础上,以“木桶效应”确定了各县(市)天然草地的合理载畜量并对其草畜平衡进行了评估。结果表明:1)按照理论上的自然轮牧规律,参考固定面积比例和年内NDVI的时空分布规律划分的玉树州各县(市)冷季放牧区分布于较温暖的沟谷地区,暖季放牧区处于高寒区域,且冷季放牧区产草量大于暖季放牧区。2)年际间冷暖季放牧区干草产量均呈波动增长趋势,年均增长率均在3.00%以上且暖季放牧区年产草量增长率大于冷季放牧区。3)2014-2019年玉树州整体的草畜平衡状态由临界超载和超载波动转为平衡,但各县(市)草畜平衡状态存在差异,推测可能是因为自然保护区和国家公园生态工程支持的空间范围差异引起的。本研究对进一步修订三江源自然保护区和国家公园建设方案,实施高寒生态屏障区生态和畜牧业经济的良性发展策略具有一定的参考价值。

2010—2019年间洞庭湖流域生态环境状况时空动态特征及影响因素

洞庭湖流域是长江中下游重要生态区,开展其生态环境状况定量监测与评价是进行区域生态保护、修复及治理的先决条件。该文采用2010—2019年MODIS遥感数据产品,基于绿度、湿度、干度和热度4个生态指标构建了洞庭湖流域遥感生态环境指数(remote sensing ecological index,RSEI),并在此基础上研究了流域生态环境状况时空动态特征及其影响因素。结果表明:①2010—2019年洞庭湖流域绿度指标呈增长趋势,湿度指标呈下降趋势,而干度和热度指标变化较为平稳;②洞庭湖流域整体生态环境状况较好,RSEI多年均值为0.58,并呈波动增长,空间上则表现为西部优于东部、四周优于中部的分布格局;③降水、气温、高程以及土地覆盖与RSEI均具有较强相关性。其中,林地RSEI(0.65)最高,建筑用地RSEI(0.31)最低;在流域2种主要土地转化类型中(草地→林地、耕地→草地),“草地→林地”转化有助于促进区域生态环境(ΔRSEI=0.0025,贡献率为46.3%),而后者则有可能导致生态环境恶化(ΔRSEI=-0.0004,贡献率为44.44%)。研究结果有助于深刻把握流域生态环境时空特征及其内在驱动机制,辅助开展科学的土地规划与生态环境治理,因而具有较强的理论与实践意义。

青海生态环境状况20年(2000—2020)变化遥感地质评价

青海省处于青藏高原腹地,属于典型的大陆气候,地质生态环境较为脆弱,近些年来地质生态环境持续得以改善,对生态环境状况进行评价,具有重要的意义。本文借鉴生态环境评价技术方法,利用2000年和2020年两期遥感影像数据为研究基础,建立了地质生态环境状况遥感评价模型,在计算生物丰度、植被覆盖、水网密度、土地胁迫4个指标指数的基础上,应用综合指数法得出生态环境状况指数(EI)并开展对比研究,从遥感影像图的角度来评价青海生态环境20年的变化状况。评价结果显示生态环境状况指数值均小于0.75,生态环境状况等级为“一般”的面积有45.1万km^(2),占比63%。2000—2020年间,青海生态环境状况约60.56万km^(2)(约87%)无明显变化,局部地区趋向变好的趋势。

基于景观格局与遥感生态指数的成都市碳排放分析

首先利用景观格局分析方法建立最优的土地利用分类粒度;随后进行城市生态质量评价并对土地利用分类结果进行碳排放计算,然后分析成都市各区、县碳排放空间格局变化。分析可得,30 m为最佳分类粒度,2015—2020年成省市城市整体生态质量得到提升,各区、县碳排放总量处于增加状态,且城市生态质量提升区域多处于碳排放增加量大的区域。综上可得,成都市没有因为城市化进程加快使城市生态质量变差,而在城市化进程快速区域的生态质量改善明显,因此成都市整体处于生态改善建设中。

基于RSEI鄱阳湖生态经济区生态质量评价

本文以陆地卫星(Landsat)影像为基础,运用遥感技术提取绿度、湿度、干度和热度四个指标,通过主成分分析方法构建了2002—2022年的鄱阳湖生态经济区遥感生态指数(RSEI),全面评估了该地区生态质量的时空变化。研究表明:2002—2020年,鄱阳湖生态经济区RSEI指数呈增长趋势,大部分区域的RSEI等级为良和优,占比从76.86%提高到了82.46%,变化最明显的地区主要集中在鄱阳湖流域的湖泊湿地。本研究为进一步实现鄱阳湖生态经济区的生态环境和经济的协调发展提供了科学依据。

Remotely sensed estimation and mapping of soil moisture by eliminating the effect of vegetation cover

Soil moisture(SM), which plays a crucial role in studies of the climate, ecology, agriculture and the environment, can be estimated and mapped by remote sensing technology over a wide region. However, remotely sensed SM is constrained by its estimation accuracy, which mainly stems from the influence of vegetation cover on soil spectra information in mixed pixels. To overcome the low-accuracy defects of existing surface albedo method for estimating SM, in this paper, Qinghai Lake Basin, an important animal husbandry production area in Qinghai Province, China, was chosen as an empirical research area. Using the surface albedo computed from moderate resolution imaging spectroradiometer(MODIS) reflectance products and the actual measured SM data, an albedo/vegetation coverage trapezoid feature space was constructed. Bare soil albedo was extracted from the surface albedo mainly containing information of soil, vegetation, and both albedo models for estimating SM were constructed separately. The accuracy of the bare soil albedo model(root mean square error=4.20, mean absolute percent error=22.75%, and theil inequality coefficient=0.67) was higher than that of the existing surface albedo model(root mean square error=4.66, mean absolute percent error=25.46% and theil inequality coefficient=0.74). This result indicated that the bare soil albedo greatly improved the accuracy of SM estimation and mapping. As this method eliminated the effect of vegetation cover and restored the inherent soil spectra, it not only quantitatively estimates and maps SM at regional scales with high accuracy, but also provides a new way of improving the accuracy of soil organic matter estimation and mapping.

青藏高原冰湖溃决灾害隐患识别、发育规律及危险性评价

青藏高原是全球冰湖溃决灾害发生最频繁的区域之一,冰湖溃决对人类及工程建设安全造成严重威胁。以2015-2018年Landsat 8 OLI_TIRS等遥感影像及数据为基础,对青藏高原40000余条冰川10 km范围内且面积大于900 m^(2)的冰湖进行了遥感解译,分析了冰湖分布与发育特征,建立了冰湖溃决隐患的识别指标体系,利用突变级数法(CPM)对隐患点进行了危险性分级评价。结果表明:①青藏高原发育冰湖16481处,海拔分布在5000~5500 m之间的冰湖占总量的43.69%;面积集中在100~500 km^(2)之间的占总量的47.40%;行政分布上主要分布在西藏自治区,有12664个,占总量的76.84%;流域上主要分布在雅鲁藏布江流域,有8321个,占总量的50.49%。②识别出冰湖灾害隐患点369个,其中低危险点126个,中危险点177个,高危险点66个。③冰湖溃决隐患点面积多为0.1~0.2 km^(2);海拔主要分布在5000~5500 m之间;与母冰川距离大多小于100 m;冰碛坝宽度一般小于300 m,背水坡坡度大多小于50°;冰湖溃决隐患点的母冰川冰舌端坡度分布在10°~20°之间;绝大多数冰湖溃决的方向朝向北方。

基于土地利用/覆盖的甘肃省景观生态风险时空变化特征

甘肃省景观类型丰富多样,在中国西部地区的生态安全中起关键性作用,也为当地发展提供着重要的物质基础和生态服务。由于气候变化和人类不合理活动的影响,甘肃省部分地区生态环境的安全与稳定遭到严峻挑战。本研究基于欧洲航空局全球土地利用/覆盖数据(CCI-LC),利用动态度、转移矩阵方法分析了2005-2019年甘肃省土地利用/覆盖变化特征;通过构建景观生态风险指数并运用空间自相关分析法分析了景观生态风险的空间分布特征。结果显示:1)2005-2019年间,裸地面积减少最多,变化率1.88%,耕地次之,变化率2.31%;草地面积增加最多,变化率1.83%,建设用地次之,变化率191.08%。2)所有土地利用/覆盖类型中,建设用地的动态度最高且在整个研究时期内变化速率都保持在较高水平,综合动态度最大的时期为2010-2019年。3)土地利用/覆盖转移矩阵主要表现为耕地、林地、草地、裸地之间的转入与转出以及其他土地类型向建设用地的转入。4)2019年整体的生态风险值较2005年明显上升。其中低风险区和较低风险区面积最多,转化最频繁;中等风险区、较高风险区和高风险区面积虽然较少,但都存在不同程度的增长。5)2005-2019年间,Moran’s I维持在较高水平,景观生态风险的空间分布呈正相关,聚集效应强。本研究旨在为西北地区土地利用规划和生态风险管控提供科学依据。