Mapping Prosopis juliflora in West Somaliland with Landsat 8 Satellite Imagery and Ground Information

Prosopis juliflora is a drought‐tolerant fast‐growing tree species originating from South and Central America with a high invasion potential in arid and semi‐arid areas in Africa. It was introduced in Somaliland in the 1980s and is reported to have spread vigorously since. Despite being recognized as a serious issue in the country, the actual scale of the problem is unknown. In this study, we mapped the species in a study area that includes the capital, Hargeisa, using Landsat 8 satellite imagery. During a field campaign in 2015, we collected canopy‐level spectral signatures of P . juliflora and native trees to analyse the potential use of spectral data in discriminating the invasive species. P. juliflora was found to be generally distinguishable because of its greater vigour during the dry season. We tested the accuracy of the random forest classifier and different classification set‐ups, varying the spatial resolution (original 30 m vs pan‐sharpened 15 m) and image acquisition dates (during the wet season, the dry season and a combination of the two). Best overall accuracy (84%) was achieved by using pan‐sharpened data from the two seasons. About 30 years since its introduction, the invasive species was detected in 9% of the total investigated area with highest occurrence in the proximity of human settlements and along seasonal water courses. © 2016 The Authors. Land Degradation and Development published by John Wiley & Sons, Ltd.     

Using Landsat Images and Spectral Mixture Analysis to Assess Drivers of 21‐Year LULC Changes in Sudan

Landsat images acquired in 1987, 1999 and 2008 were selected to evaluate land use and land cover changes in three different ecological zones in the Republic of the Sudan: savannah (North Kordofan), semi‐desert (River Nile) and desert (Northern State). Maps of soil, vegetation and non‐photosynthetic material were built by spectral mixture analysis using endmembers spectra derived from images. Multitemporal comparison techniques were then applied to vegetation and soil maps to estimate the long‐term land degradation/re‐growth dynamics and to emphasize land cover variation over time and in space. Multitemporal comparisons have shown that site‐specific interactions between natural processes, climate variation and human activity played a pivotal role in land use and land cover change. In savannah, human activities strongly affected degradation phenomena. Expansion of villages triggered a change in land use and mismanagement of the natural resources, mainly caused by deforestation to supply wood for domestic uses. The degradation then promoted sand encroachment and dune migration. On average, 48% of the total area was subjected to medium (25–50%) and high (50–75%) land degradation. Differently, climatic constraints drove increasing degradation processes in semi‐desert and desert zones. The phenomenon was particularly severe in the desert zone. In each site, an inversion of the general trend was due to a land use change where forestry or agricultural projects were established, partly sustained in savannah by the positive rainfall trend of the last two decades. Site‐specific strategies that take into account the interactions of the driving factors at local scale are thus necessary to combat land degradation. Copyright © 2016 John Wiley & Sons, Ltd.     

A Nondestructive,Remote Sensing-Based Estimation of the Economic Value of Aboveground Temperate Forest Biomass (Case Study: Hyrcanian Forests,Nowshahr-Iran)

ABSTRACT

Background: Traditional field-based methods of measurement of biomass and carbon storage face difficulty in collecting time-consuming and expensive, suggests the use of remote sensing-based techniques. It estimates the economic value of the aboveground biomass (AGB) using satellite remote sensing across the Hyrcanian forests of Iran. Methods: The Landsat-8 OLI sensor data were combined with field-based allometric information of 186 circular sample plots. The AGB was calculated at the plot level using the collected data and specific volumetric mass for species in the studied area. It was followed by calculating the carbon storage using a 50% carbon coefficient and the photosynthesis equation at the forest parcel level. Model results using the random forest and support vector machines. The carbon sequestration value was calculated with USD 25.3 as a shadow value of carbon in 2014 and using the replacement cost approach. Results: The highest performances achieved by RF for biomass, carbon storage and the carbon storage value (Iranian Rials of 0.67% and 16%, respectively). The value was derived once at the plot level of 12.22 million IRR (370.43 USD) per ha. In addition, at the parcel level, which resulted in an estimated value of 12.87 million IRR (390.24 USD) per ha.     

Characterizing woodland caribou habitat in sub-boreal and boreal forests

Woodland caribou (Rangifer tarandus caribou) are sensitive to changes in understory vegetation resulting from forest harvesting and are, therefore, of special concern for foresters and habitat biologists. Effective management of this species requires reliable habitat inventories which, because of the large heterogeneous areas over which caribou range, can be costly. We used Landsat Thematic Mapper (TM) imagery and digital elevation data to identify 23 vegetative cover types across the 5100 km² range of the Wolverine caribou herd of northcentral British Columbia, Canada. The classification was augmented with available geographical information system (GIS) data for a total of 27 cover types. We achieved an overall accuracy of 76.7% based on known ground samples; however, accuracy varied according to cover type. Considering the size of the study area, the procedure we employed was relatively cost effective and efficient. We discuss the advantages of such an approach for wildlife-habitat studies reliant on large-scale vegetation maps.     

基于TM影像的玛多县湿地变化监测方法

以玛多县为典型研究区,利用2010年Landsat TM数据比较分析了谱间关系法、归一化水体指数（NDWI）、修正归一化水体指数（MNDWI）及非监督分类4种分类方法提取湿地信息的准确度。结果表明,在较大显著湿地区域,4种方法信息提取的准确度都比较高;在海拔4 500 m以上且地形复杂地区,MNDWI方法的精度明显高于其他3种方法,达到86%;在中部地势较低区域,MNDWI方法不如NDWI方法的准确度高,但优于谱间关系法及非监督分类方法。总体来说,MNDWI方法能很好地将云和地形对湿地信息提取的影响降到最低,筛选最佳湿地信息提取方法有助于保护青藏高原湿地资源。     

基于Landsat8 OLI数据的山东省耕地信息提取研究

为了探讨美国最新发射的陆地卫星Landsat8 OLI影像在耕地信息提取方面的效果,为了解该影像的应用潜力提供一些信息,采用遥感图像处理软件ENVI,对覆盖山东省区域的12幅Landsat8 OLI影像进行了计算机校正和增强处理,通过目视解释、监督分类和非监督分类交互式的分类方法提取山东省的耕地信息。结果表明,基于Landsat8 OLI数据提取耕地信息适宜波段是543波段和652波段组合,提取精度达到91.8%,Landsat8 OLI数据可以满足耕地利用及管理中对耕地信息适时获取的要求。

     

综合近红外-红波段-短波红外三波段光谱特征空间的小麦冠层含水量反演

为提高返青期-拔节期-开花期-灌浆期不同覆盖条件下小麦冠层含水量的遥感反演精度,综合分析基于Nir-Red和Nir-Swir光谱特征空间开展作物含水量监测的优势与局限,利用垂直干旱指数（perpendicular drought index,PDI）和短波红外垂直失水指数（shortwave infrared perpendicular water stress index,SPSI）的比值形式,构建了一种基于近红外-红波段-短波红外（Nir-Red-Swir）三波段光谱特征空间的垂直植被水分指数（three-band perpendicular vegetation water index,TPVWI）。结果表明,在不同生育时期,TPVWI与小麦冠层含水量（vegetation water content,VWC)均具有显著相关关系（P<0.01）,且对植被含水量的敏感性优于PDI、作物水分监测指数（plant water index,PWI）、SPSI和NDVI 4种植被指数,且在反映小区域内小麦冠层含水量的时空趋势上有较好的表征能力。对比地面实测数据,利用TPVWI建立的作物含水量估测模型的预测精度较高,r与RMSE分别为0.763和2.296%,说明利用综合Nir-Red-Swir三波段光谱空间特征的植被水分指数在监测不同覆盖条件下的作物含水量具有一定的可行性,可丰富当前作物冠层含水量遥感监测的理论方法。     

Study of salinity and waterlogging in Uttar Pradesh (India) using remote sensing data

Salinization/alkalization and waterlogging have rendered a sizeable area of India's arable lands unproductive. These highly dynamic phenomena can be studied using remote sensing data collected at regular intervals. Landsat Multi-spectral Scanner data of 28 February 1975 and 9 March 1990 were interpreted visually to derive information about the spatial extent and distribution of salt-affected soils. A significant decrease in the area of salt-affected soils was noted over this period, which can be attributed to reclamation efforts by government agencies and farmers. On a large scale, i.e. 1:50000, the waterlogged area could also be mapped using a Landsat Thematic Mapper false-colour composite print.     

基于Landsat影像数据的香格里拉市优势树种蓄积量估测模型构建

以香格里拉市优势树种高山栎、高山松、云冷杉及云南松为研究对象,构建基于遥感的森林蓄积量估测模型。采用2006年和2016年的Landsat TM/OLI影像及二类调查数据,随机从每期数据中各选取100个小班,提取其影响因子,利用相关性强的因子构建RF模型和MLR模型。结果表明,2006年各树种蓄积量RF模型的拟合R²在0.779～0.862范围,预测精度P值为80.17%～92.16%;2016年RF模型的拟合R²为0.761～0.865,预测精度P值为81.61%～95.53%。建立MLR模型后,2006年树种蓄积量估测模型的拟合R²为0.214～0.336,预测精度P值为34.12%～47.16%;2016年模型的拟合R²为0.238～0.391,预测精度P值为34.82%～52.57%。估测结果与二类调查数据的误差为：高山栎、高山松、云冷杉、云南松分别增加了8.54×10⁵m³、3.65×10⁶m³、4.1...