Environmental Sensitivity Index Mapping for Environmental Sustainable Cleanup along NAOC Pipeline, Asemoku, Delta State, Nigeria

During emergency response to oil spills incident accurate information is required in order to reduce the risk associated with oil spill disasters. This study focuses on Environmental Sensitivity mapping for sustainable environmental clean-up and contingency planning along the 3.0 km of AGIP pipeline at Asemoku in Delta State, Nigeria. Geographic information systems (GIS) techniques were used to create an Environmental Sensitivity Index (ESI) map in the study area. A 2018 Google Earth Satellite imagery of the study area was downloaded, and landuse/cover classification scheme comprising of Vegetation, Farmland, Water Body, Wetland, built up area and Bare Surface was adopted. Existing categorization, ranking and classification of the inland habitat were adopted and used to create a Landuse/cover Environmental Sensitivity Index (ESI) map, while the buffer zones of 100 m, 200 m, 300 m and 400 m were adopted. In the ArcGIS 10.8 environment, the landuse/cover map was generated and buffer distances of 100 m, 200 m, 300 m and 400 m were created on the landuse/cover map to ascertain the features that are vulnerable and could be at risk in the event of oil spill. This study established that the Natural Vegetation areas are the most vulnerable and sensitive feature as a result of their size along the created buffer zones. Findings from this study thus provide insight into the most sensitive land-use/land-cover, in the event of a spill or emergency oil spill clean-up response.

Dynamic Analysis of Land Cover in Four-Lake Area of Jianghan Plain Based on MODIS-EVI Time Series Data

According to the time series data of Enhanced Vegetation Index (EVI) in Four-Lake Area of Jianghan Plain during the period 2001-2007, we use Harmonic Analysis of Time Series (HANTS) to conduct cloud removing processing, and calculate the sum of square N of time series value of each pixel. The pixels with N>0.25 are classified as vegetation coverage area; the pixels with N<0.25 are classified as non-vegetation coverage area. As to vegetation coverage area, we use the second-order difference method to judge the frequency of peak value of EVI time series data. Within one year, the vegetation coverage area with peak value happening 1 time is woodland and grassland; the vegetation coverage area with peak value happening 2 times is arable land; the vegetation coverage area with peak value happening 3 times or more is vegetable land. Supervised classification method is used to identify cities, towns, water area in non-vegetation coverage area and woodland, grassland in vegetation coverage area. We draw the land cover classification diagram of Four-Lake Area in the period 2001-2007. In comparison with the land cover classification based on multitemporal ETM data in 2001, the difference of area of arable land is within 10%. Using MODIS-EVI data, we can rapidly and efficiently conduct land cover classification with low cost. The dynamic analysis results indicate that the area of arable land is in the process of declining, while the area of other cover types shows an increasing trend.

Impact of land cover change on land surface temperature: A case study of Spiti Valley

Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.

Forest Cover Dynamics of a Lowland Rainforest in Southwestern Nigeria Using GIS and Remote Sensing Techniques

The rate of forest degradation and deforestation in Nigeria has been increasing over the years and is prominent in the southwestern parts. Despite the significant change and degradation observed in a lowland rainforest in the region—Ogbese Forest Reserve, there is a great dearth of information about the level of forest cover change. Therefore, this study determined the cover dynamics of the rainforest reserve over the epoch of 20 years using Geographic Information System and remote sensing techniques. Coordinates of the boundary and some other benchmark places within the forest reserve were obtained. Secondary data collection included: Landsat imageries of 1998, 2002 and 2018. An interview guide was used to obtain information from forest officials and locals of the surrounding communities to complement the spatial data obtained. Image classification was done using the maximum likelihood algorithm. The rate of change across the epochs was determined using the area of the land cover classes. The level of vegetation disturbance in the reserve was determined through Normalized Difference Vegetation Index. Five different forest cover classes were identified in the study area: forest, plantation, farmland, grassland, and bare land. The natural forest reduced significantly from 34.43 km2 (48%) in 1998 to 8.73 km2 (12%) in 2002 and was depleted further by 2018, while other cover classes increased. NDVI value also reduced from 0.25 to 0.13. Agriculture, among others, was observed as the main driver of forest degradation and deforestation in Ogbese Forest Reserve. The study concluded that the remaining forest (i.e. plantation) could also be depleted by 2025, as it decreases by −0.94 km2 per year if proper reforestation and management practices are not introduced.

Uncertainty Analysis of Spatial Autocorrelation of Land-Use and Land-Cover Data within Pipestem Creek in North Dakota

A major threat to biodiversity in North Dakota is the conversion of forested land to cultivable land, especially those that act as riparian buffers. To reverse this trend of transformation, a validation and prediction model is necessary to assess the change. Spatial prediction within a Geographic Information System (GIS) using Kriging is a popular stochastic method. The objective of this study was to predict spatial and temporal transformation of a small agricultural watershed—Pipestem Creek in North Dakota;USA using satellite imagery from 1976 to 2015. To enhance the difference between forested land and non-forested land, a spectral transformation method—Tasseled-Cap’s Greenness Index (TCGI) was used. To study the spatial structure present in the imagery within the study period, semivariograms were generated. The Kriging prediction maps were post-classified using Remote Sensing techniques of change detection to obtain the direction and intensity of forest to non-forest change. TCGI generated higher values from 1976 to 2000 and it gradually reduced from 2000 to 2011 indicating loss of forested land.

Land Use/Land Cover Change Detection in Pokhara Metropolitan, Nepal Using Remote Sensing

Land use and land cover are essential for maintaining and managing the natural resources on the earth surface. A complex set of economic, demographic, social, cultural, technological, and environmental processes usually result in the change in the land use/land cover change (LULC). Pokhara Metropolitan is influenced mainly by the combination of various driving forces: geographical location, high rate of population growth, economic opportunity, globalization, tourism activities, and political activities. In addition to this, geographically steep slope, rugged terrain, and fragile geomorphic conditions and the frequency of earthquakes, floods, and landslides make the Pokhara Metropolitan region a disaster-prone area. The increment of the population along with infrastructure development of a given territory leads towards the urbanization. It has been rapidly changing due to urbanization, industrialization and internal migration since the 1970s. The landscapes and ground patterns are frequently changing on time and prone to disaster. Here a study has been carried to study on LULC for the last 18 years (2000-2018). The supervised classification on Landsat Imagery was performed and verified the classification through computing the error matrix. Besides, the water bodies and vegetation area were extracted through the Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDWI) respectively. This research shows that during the last 18 years the agricultural areas diminishing by 15.66% while urban area is increasing by 13.2%. This research is beneficial for preparing the plan and policy in the sustainable development of Pokhara Metropolitan.

利用地理国情数据分析长江沿岸人为干扰度

为了解近年来长江沿岸人为干扰变化情况,基于江苏省地理国情数据,结合人为干扰理论,利用GIS、RS技术,从时空视角分析了2015—2020年长江苏州段1 km沿岸区域的人为干扰动态变化情况。结果表明:①区域内地表覆盖呈现“生态用地—建设用地—农业用地”协调发展的分布特征,生态保护修复成效显著;②区域内人为干扰总强度以重度干扰和中度干扰为主,整体变化较平稳,变化剧烈区域零星分布在生态修复重点区域和一些大型码头周边。由此可知,定量分析长江流域的人为干扰情况,对评估长江大保护成效具有积极意义。

黄河源区下垫面变化对水文过程的影响

量化评估下垫面变化对水平衡要素的影响,对揭示黄河源区径流变化归因具有重要意义。论文建立了考虑土地覆被变化(LUCC)和叶面积指数(LAI)动态变化的可变下渗容量(VIC)模型,模拟了下垫面变化条件下的降水-径流响应关系。结果表明,考虑下垫面动态变化的VIC模型能更好地模拟源区水文过程,相对误差降低8.8%~12.9%。2001—2018年LAI和LUCC综合作用导致植物蒸腾量年均增加约15%,玛曲和唐乃亥断面径流分别减少约9.19%和7.17%。LAI对径流影响较LUCC大,LAI对玛曲和唐乃亥断面多年平均径流量的贡献分别为-4.80%和4.48%,而LUCC的贡献为0.16%和-3.15%。研究解释了下垫面变化是源区降水增加条件下径流变化不显著的原因,对认识气候变化和生态保护的水文响应规律有借鉴意义。

滴灌工程对农业生产能力的影响评估

滴灌工程作为干旱区绿洲灌溉农业增产增收的一项革命性技术工程,对农业生产和生态环境产生着深远影响。为了有效评估滴灌工程对农业生产能力的影响,将沙雅县作为研究区域,利用归一化差异植被指数(Normalized Difference Vegetation Index,NDVI)数据集、气象数据集和土地利用/覆被变化(Land Use/Cover Change,LUCC)数据集,对滴灌工程在建设与运行期间(2014-2022年)的县域NDVI及耕地面积时空变化进行分析。结果表明:①2014-2022年,全县耕地面积增加了83.83 km^(2),其中滴灌工程区占比71.22%。②滴灌工程区耕地NDVI平均增长率为0.44%/a,而非滴灌工程区耕地NDVI平均增长率仅为0.30%/a,滴灌工程区耕地NDVI变化较为明显。③滴灌工程实施后,年降水量降低了42.48%,然而县域NDVI却呈现出“增加—平缓—增加”的变化特征,平均增速为0.27%/a,平均增长11.53%。农业生产能力没有降低反而增加。滴灌工程的实施,不仅促进了土地流转与规模化经营,田块的破碎化程度降低,显著扩大了有效耕地面积,更重要的是县域农业产能(NDVI)与节水抗旱能力显著提升,沙雅县整体农业生产能力得到提高。因此,滴灌工程对于提高干旱区农业生产能力和生态环境具有重要意义。

高分一号与Landsat-8影像在荒漠绿洲过渡带应用对比

针对国产高分一号卫星(GF-1)成像质量是否可以满足区域生态环境监测需求的问题,开展了宽幅多光谱相机(wide field view,WFV)在荒漠绿洲过渡带的成像质量评估研究。从辐射质量、纹理、地类识别精度和归一化植被指数等方面构建评估指标,定量分析了GF-1 WFV和Landsat-8OLI在荒漠绿洲过渡带的成像质量差异。结果表明:GF-1 WFV影像虽然具有较高的空间分辨率,但在辐射质量、地类识别效果、纹理信息及植被指数等方面与Landsat-8OLI相比有一定差距;GF-1 WFV影像的信噪比优势明显,对噪声的抑制效果较好;通过与纹理信息的波段组合,可以有效提高GF-1WFV影像的地物识别效果,缩小与Landsat-8OLI在分类精度上的差距;鉴于明显的光谱范围差异,二者归一化植被指数数据在协同应用的过程中宜分地物类型转换,在西北荒漠绿洲过渡带的国土资源调查、城市规划、农情监测等方面可发挥积极作用。

面向土地覆盖精准分类的遥感特征参数优选方法

在显著气候变化叠加人类活动干扰的背景下,可持续的自然资源管理对于精准掌握土地覆盖信息的需求不断提升。为应对地表形态的复杂性、地物类型的多样性、遥感图像特征的非线性给传统遥感图像分类方法带来的挑战,本文基于随机森林Gini指数,提出了一种10%阈值决策的遥感特征参数优选方法,旨在筛选出最优的遥感特征参数组合,达到最佳土地覆盖分类效果。首先,选择光谱特征、纹理特征、温热特征、高程特征、主成分特征组成遥感影像堆栈。然后,设置多组决策树对特征贡献度进行交叉验证,并根据特征重要性的归一化均值确定特征排序。最后,设定阈值,筛选出符合要求的遥感特征参数,并迭代过程。选择覆盖江苏盐城自然保护区的Sentinel-2遥感图像开展试验,结果表明,本文方法筛选出的遥感特征参数代表性好,与CART、SVM、KNN和只使用波段信息的RF相比,分类结果地物边界清晰,类别属性准确,总体精度和Kappa系数分别为96.20%和0.9556。本文研究能够为区域空间规划和可持续发展提供技术支持。

城市“热岛效应”遥感监测研究——以珠海市为例

为完成珠海市热岛效应与土地覆被相关性分析,利用多期Landsat遥感影像,基于单通道算法反演地表温度,基于指数法完成地表覆被类型提取分类,引入分布指数构建热岛分布指数P,分析珠海市热岛效应的分布与变化规律,实现地表覆被与热岛效应的空间关系相关性分析。研究结果表明:珠海市热岛效应强度2013—2021年间呈持续上升趋势,与城市建设用地增长趋势一致,与城市发展实际基本相符。

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。

Landsat8不透水面遥感信息提取方法对比

不透水面是重要的地表覆盖类型,利用卫星遥感影像准确提取不透水面信息对于掌握地表覆盖变化具有重要意义。现有研究已提出了多种不透水面遥感信息提取指数,但目前尚缺乏对这些不透水面指数的系统对比分析。利用Landsat8卫星遥感影像,测试了目前8种主要不透水面指数的提取精度。结果表明,在现有不透水面指数中,垂直不透水层指数能够有效增强不透水面信息,不透水面提取精度最高(89.6%),其次是比值居民地指数和生物物理组分指数(87.5%和87.4%),城市指数与新建筑指数提取精度再次之(82.9%和80.0%),归一化差值不透水面指数、归一化建筑指数和基于指数的建筑指数未能有效增强不透水面信息,提取精度较低(<75.0%)。此外,这8种不透水面指数都未能有效解决不透水面与大片裸地光谱混淆的问题,在裸地广泛分布的区域难以准确提取不透水面,平均提取精度仅为71.0%,影响了不透水面指数的大区域应用。

基于Landsat TM数据的若尔盖县LUCC时空特征研究

若尔盖县是世界著名若尔盖湿地的主要组成部分,是青藏高原高寒生态系统的典型代表。基于1989年、1997年和2004年3期Landsat TM影像的土地利用/土地覆被分类结果,运用地理信息系统空间分析方法和数理统计学方法,深入分析了四川省若尔盖县近15年来各土地利用/覆被类型尤其是草地和沼泽的数量和空间变化特征。结果表明:①研究区主要土地利用与土地覆被类型为草地、沼泽、林地和裸地,其中草地与沼泽面积逐步减少,而裸地面积成倍增长。②通过建立研究区LUCC幅度、LUCC数量和空间变化模型以及趋势与状态指数模型,很好的表现了研究区LUCC的时空特征。从整个区域来看,前期综合LUCC趋势和状态指数为0.37,处于准平衡状态;后期小于前期,为0.23,处于平衡状态,整个时段其指数为0.35,为准平衡状态,呈现双向转换态势。③定位分析了研究区LUCC情况,发现区域草地和沼泽退化相当严重,而且前后两期退化区在空间上有所转移。

PIE-Engine平台下土地利用变化对生态质量影响的遥感评估与时空分析

针对濂溪区土地利用变化引起生态质量的问题,本文基于PIE-Engine Studio遥感云平台,采用多时序卫星数据结合第三次全国国土调查数据,在土地利用/覆盖变化基础上,深入剖析2008—2023年土地覆盖和生态环境时空演变特征及其驱动因素。结果表明:①在这段时间内濂溪区生态质量整体有所改善,特别是在生态质量优等级(0.8~1.0)上,耕地向草地的转变面积和比例均有显著增加,但仍存在局部退化现象;②良等级(0.6~0.8)面积有所减少,可能是某些区域建筑用地增加引起的生态质量下降。通过实施有效的土地管理加强生态保护和修复,可以进一步提高生态环境质量。

Spatial pattern of impervious surfaces and their impacts on land surface temperature in Beijing,China

Land surface temperature （LST）, which is heavily influenced by urban surface structures, is a significant parameter in urban environmental analysis. This study examined the effect impervious surfaces （IS） spatial patterns have on LST in Beijing, China. A classification and regression tree model （CART） was adopted to estimate IS as a continuous variable using Landsat images from two seasons combined with QuickBird. LST was retrieved from the Landsat Thematic Mapper （TM） image to examine the relationships between IS and LST. The results revealed that CART was capable of consistently predicting LST with acceptable accuracy （correlation coefficient of 0.94 and the average error of 8.59%）. Spatial patterns of IS exhibited changing gradients across the various urban-rural transects, with LST values showing a concentric shape that increased as you moved from the outskirts towards the downtown areas. Transect analysis also indicated that the changes in both IS and LST patterns were similar at various resolution levels, which suggests a distinct linear relationship between them. Results of correlation analysis further showed that IS tended to be positively correlated with LST, and that the correlation coefficients increased from 0.807 to 0.925 with increases in IS pixel size. The findings identified in this study provide a theoretical basis for improving urban planning efforts to lessen urban temperatures and thus dampen urban heat island effects.

Spatio-Temporal Variability in Land Surface Temperature and Its Relationship with Vegetation Types over Ibadan, South-Western Nigeria

Ibadan has experienced a rapid urbanization over the past few decades due to heavy influx of people from different parts of the country as a result of improved economy of the region. This development induced a great change in land use and land cover over the region which has become a major environmental concern recently. This study assessed Land Surface Temperature (LST) and its spatio-temporal relationship with land cover type over Ibadan. Land use/Land cover dynamics were assessed using index maps generated from Landsat Satellite data (TM, ETM+ and OLI) of Ibadan. The corrected thermal Infrared bands of the Landsat data were used to retrieve LST. The results revealed a notable increase in built-up areas from 5.64% of the total land cover area in 1984 to 14.05% in 2014. This change has caused increase in surface temperature of Ibadan from 3.56?C to 8.54?C between 1984 and 2014 respectively. The study recorded a continuous decrease in the vegetal part of Ibadan (from 43.28% in 1984 to 14.76 in 2014) which could be attributed to anthropogenic activities as the vegetated land area lost was been converted to other form of land use. The change was found to be positively correlated to the surface temperature intensity over the region with correlation coefficient, r value of 0.9251, 0.8256 and 0.7017 in 1984, 2000 and 2014 respectively. It is recommended that Policies should be considered for planting trees, new guidelines for urban landscape design and construction.

RS and GIS-based Analysis of Landscape Pattern Changes in Urban-rural Ecotone:A Case Study of Daiyue District,Tai' an City,China

Based on RS data of Daiyue District,Tai'an City in 2000,2005 and 2010,changes of land use types in urban-rural ecotone of Tai'an City from 2000 to 2010 were analyzed.Ecological theories,ArcGIS techniques and landscape structure analysis software Fragstats were applied to select relevant landscape pattern indexes and analyze changes of landscape structure,and compare changes in two durations(2000-2005,2005-2010).The results showed that area of natural landscapes in the study area declined from 2000 to 2010,construction land expanded,land use types were mainly transferred from natural landscapes to man-made landscapes.In terms of landscape level,number of patches(NP),patch density(PD),patch shape index(SHAPE),Shannon's diversity index(SHDI) and Shannon's evenness index(SHEI) increased,the largest patch index(LPI) declined.In terms of type level,arable land were influenced by the most human interventions,large-scale patches turned fragmented,and landscape dominance degraded;woodland landscapes were concentrated in mountainous areas,waterscape indexes showed slight changes.Dominance of regional dominant landscape types degraded,landscape fragmentation and landscape heterogeneity increased,and landscape stability declined.

Assessing Naturalness Changes Resulting from a Historical Land Use in Brazil South Region: An Analysis of the 1986-2016 Period

The effects of land use and cover changes and the application of the Urbanity Index were analyzed to identify critical or non-critical naturalness scenery in the Northern Region of Rio Grande do Sul (Brazil), over a period of 30 years. Between 1986 and 2016, the main significant land use change was the reduction of the agricultural anthropic and the increase of the natural vegetation land use areas. About 90% - 80% of the total study areas were characterized by anthropic pattern features, with a predominantly agricultural matrix. The conversion of agricultural anthropic into natural areas resulted in increased naturalness landscape and a gain in the stock of natural capital. The increase in the natural vegetation area, mainly to the north and east of the region, set in a steep relief, became remarkable after 1990. The Urbanity Index values evidenced a highest naturalness condition (natural vegetation areas) to the northern and eastern, while a smaller naturalness condition (agricultural and non-agricultural areas) to the southern and western and central of the Northern Region of Rio Grande do Sul. These changes are related to a non-impaired (northern and eastern) and a impaired scenery (southern, western and central portion) of the ecological sustainability of the Northern Region of Rio Grande do Sul, after a 30-year period (1986-2016). These results pointed out that land use transitions resulted in a consolidated farming scenario in which agricultural intensification coincided with the reduction of farming area and increased naturalness as a reverse trend to the agricultural frontier dynamics.