融合多源遥感数据的高分辨率城市植被覆盖度估算

准确获取城市植被覆盖定量信息对城市生态环境评价,城市规划及可持续城市发展具有重要意义.遥感技术的发展为获取区域及全球植被覆盖信息提供了有效手段,目前基于单传感器、单时相遥感数据的城市植被覆盖度估算方法得到较为广泛的应用.然而,由于城市地表覆盖的复杂性、植被类型的多样性,在一定程度上影响了城市植被覆盖信息提取的精度.为此...     

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)

Satellite remote sensing has been used successfully to map leaf area index (LAI) across landscapes, but advances are still needed to exploit multi-scale data streams for producing LAI at both high spatial and temporal resolution. A multi-scale Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI) has been developed to generate 4-day time-series of Landsat-scale LAI from existing medium resolution LAI products. STEM-LAI has been designed to meet the demands of applications requiring frequent and spatially explicit information, such as effectively resolving rapidly evolving vegetation dynamics at sub-field (30 m) scales. In this study, STEM-LAI is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) based LAI data and utilizes a reference-based regression tree approach for producing MODIS-consistent, but Landsat-based, LAI. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) is used to interpolate the downscaled LAI between Landsat acquisition dates, providing a high spatial and temporal resolution improvement over existing LAI products. STARFM predicts high resolution LAI by blending MODIS and Landsat based information from a common acquisition date, with MODIS data from a prediction date. To demonstrate its capacity to reproduce fine-scale spatial features observed in actual Landsat LAI, the STEM-LAI approach is tested over an agricultural region in Nebraska. The implementation of a 250 m resolution LAI product, derived from MODIS 1 km data and using a scale consistent approach based on the Normalized Difference Vegetation Index (NDVI), is found to significantly improve accuracies of spatial pattern prediction, with the coefficient of efficiency (E) ranging from 0.77–0.94 compared to 0.01–0.85 when using 1 km LAI inputs alone. Comparisons against an 11-year record of in-situ measured LAI over maize and soybean highlight the utility of STEM-LAI in reproducing observed LAI dynamics (both characterized by r² = 0.86) over a range of plant development stages. Overall, STEM-LAI represents an effective downscaling and temporal enhancement mechanism that predicts in-situ measured LAI better than estimates derived through linear interpolation between Landsat acquisitions. This is particularly true when the in-situ measurement date is greater than 10 days from the nearest Landsat acquisition, with prediction errors reduced by up to 50%. With a streamlined and completely automated processing interface, STEM-LAI represents a flexible tool for LAI disaggregation in space and time that is adaptable to different land cover types, landscape heterogeneities, and cloud cover conditions.     

Discriminating lava flows of different age within Nyamuragira’s volcanic field using spectral mixture analysis

In this study, linear spectral mixture analysis (LSMA) is used to characterize the spectral heterogeneity of lava flows from Nyamuragira volcano, Democratic Republic of Congo, where vegetation and lava are the two main land covers. In order to estimate fractions of vegetation and lava through satellite remote sensing, we made use of 30 m resolution Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Imager (ALI) imagery. 2 m Pleiades data was used for validation. From the results, we conclude that (1) LSMA is capable of characterizing volcanic fields and discriminating between different types of lava surfaces; (2) three lava endmembers can be identified as lava of old, intermediate and young age, corresponding to different stages in lichen growth and chemical weathering; (3) a strong relationship is observed between vegetation fraction and lava age, where vegetation at Nyamuragira starts to significantly colonize lava flows ∼15 years after eruption and occupies over 50% of the lava surfaces ∼40 years after eruption. Our study demonstrates the capability of spectral unmixing to characterize lava surfaces and vegetation colonization over time, which is particularly useful for poorly known volcanoes or those not accessible for physical or political reasons.     

Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years

Global climate change has led to significant vegetation changes in the past half century. North China Plain, the most important grain production base of china, is undergoing a process of prominent warming and drying. The vegetation coverage, which is used to monitor vegetation change, can respond to climate change (temperature and precipitation). In this study, GIMMS (Global Inventory Modelling and Mapping Studies)-NDVI (Normalized Difference Vegetation Index) data, MODIS (Moderate-resolution Imaging Spectroradiometer) – NDVI data and climate data, during 1981–2013, were used to investigate the spatial distribution and changes of vegetation. The relationship between climate and vegetation on different spatial (agriculture, forest and grassland) and temporal (yearly, decadal and monthly) scales were also analyzed in North China Plain. (1) It was found that temperature exhibiting a slight increase trend (0.20 °C/10a, P < 0.01). This may be due to the disappearance of 0 °C isotherm, the rise of spring temperature. At the same time, precipitation showed a significant reduction trend (−1.75 mm/10a, P > 0.05). The climate mutation period was during 1991–1994. (2) Vegetation coverage slight increase was observed in the 55% of total study area, with a change rate of 0.00039/10a. Human activities may not only accelerate the changes of the vegetation coverage, but also c effect to the rate of these changes. (3) Overall, the correlation between the vegetation coverage and climatic factor is higher in monthly scale than yearly scale. The correlation analysis between vegetation coverage and climate changes showed that annual vegetation coverage was better correlatend with precipitation in grassland biome; but it showed a better correlated with temperature i the agriculture biome and forest biome. In addition, the vegetation coverage had sensitive time-effect respond to precipitation. (4) The vegetation coverage showed the same increasing trend before and after the climatic variations, but the rate of increase slowed down. From the vegetation coverage point of view, the grassland ecological zone had an obvious response to the climatic variations, but the agricultural ecological zones showed a significant response from the vegetation coverage change rate point of view. The effect of human activity in degradation region was higher than that in improvement area. But after the climate abruptly changing, the effect of human activity in improvement area was higher than that in degradation region, and the influence of human activity will continue in the future.     

An approach of surface coal fire detection from ASTER and Landsat-8 thermal data: Jharia coal field,India

Radiant temperature images from thermal remote sensing sensors are used to delineate surface coal fires, by deriving a cut-off temperature to separate coal-fire from non-fire pixels. Temperature contrast of coal fire and background elements (rocks and vegetation etc.) controls this cut-off temperature. This contrast varies across the coal field, as it is influenced by variability of associated rock types, proportion of vegetation cover and intensity of coal fires etc. We have delineated coal fires from background, based on separation in data clusters in maximum v/s mean radiant temperature (13th band of ASTER and 10th band of Landsat-8) scatter-plot, derived using randomly distributed homogeneous pixel-blocks (9 × 9 pixels for ASTER and 27 × 27 pixels for Landsat-8), covering the entire coal bearing geological formation. It is seen that, for both the datasets, overall temperature variability of background and fires can be addressed using this regional cut-off. However, the summer time ASTER data could not delineate fire pixels for one specific mine (Bhulanbararee) as opposed to the winter time Landsat-8 data. The contrast of radiant temperature of fire and background terrain elements, specific to this mine, is different from the regional contrast of fire and background, during summer. This is due to the higher solar heating of background rocky outcrops, thus, reducing their temperature contrast with fire. The specific cut-off temperature determined for this mine, to extract this fire, differs from the regional cut-off. This is derived by reducing the pixel-block size of the temperature data. It is seen that, summer-time ASTER image is useful for fire detection but required additional processing to determine a local threshold, along with the regional threshold to capture all the fires. However, the winter Landsat-8 data was better for fire detection with a regional threshold.     

An analysis of urban expansion and its associated thermal characteristics using Landsat imagery

There has been an increasing interest in mapping and monitoring urban land use/land cover using remote sensing techniques. However, there still exist quite a number of challenges in deriving urban extent and its expansion density from remote sensing data quantitatively. This study utilized Landsat TM/ETM+ remote sensing data to assess urban expansion and its thermal characteristics with a case study in the city of Changsha, China. We proposed a new approach for quantitatively determining built-up area, its expansion density and their respective relationship with land surface temperature (LST) patterns. An urban expansion metric was also developed using a moving window mechanism to identify urban built-up area and its expansion density based on selected threshold values. The study suggested that urban extent and its expansion density, as well as surface thermal characteristics and patterns could be identified through quantitatively derived remotely sensed indices and LST, which offer meaningful characteristics in quantifying urban expansion density and urban thermal pattern. Results from the case study demonstrated that: (1) the built-up area and urban expansion density have significantly increased in the city of Changsha from 1990 to 2001; and (2) the differences of urban expansion densities correspond to thermal effects, where a high percentage of imperviousness is usually associated with the area covered by high surface temperature.     

Detection and mapping the spatial distribution of bracken fern weeds using the Landsat 8 OLI new generation sensor

Bracken fern is an invasive plant that presents serious environmental, ecological and economic problems around the world. An understanding of the spatial distribution of bracken fern weeds is therefore essential for providing appropriate management strategies at both local and regional scales. The aim of this study was to assess the utility of the freely available medium resolution Landsat 8 OLI sensor in the detection and mapping of bracken fern at the Cathedral Peak, South Africa. To achieve this objective, the results obtained from Landsat 8 OLI were compared with those derived using the costly, high spatial resolution WorldView-2 imagery. Since previous studies have already successfully mapped bracken fern using high spatial resolution WorldView-2 image, the comparison was done to investigate the magnitude of difference in accuracy between the two sensors in relation to their acquisition costs. To evaluate the performance of Landsat 8 OLI in discriminating bracken fern compared to that of Worldview-2, we tested the utility of (i) spectral bands; (ii) derived vegetation indices as well as (iii) the combination of spectral bands and vegetation indices based on discriminant analysis classification algorithm. After resampling the training and testing data and reclassifying several times (n = 100) based on the combined data sets, the overall accuracies for both Landsat 8 and WorldView-2 were tested for significant differences based on Mann-Whitney U test. The results showed that the integration of the spectral bands and derived vegetation indices yielded the best overall classification accuracy (80.08% and 87.80% for Landsat 8 OLI and WorldView-2 respectively). Additionally, the use of derived vegetation indices as a standalone data set produced the weakest overall accuracy results of 62.14% and 82.11% for both the Landsat 8 OLI and WorldView-2 images. There were significant differences {U (100) = 569.5, z = −10.8242, p < 0.01} between the classification accuracies derived based on Landsat OLI 8 and those derived using WorldView-2 sensor. Although there were significant differences between Landsat and WorldView-2 accuracies, the magnitude of variation (9%) between the two sensors was within an acceptable range. Therefore, the findings of this study demonstrated that the recently launched Landsat 8 OLI multispectral sensor provides valuable information that could aid in the long term continuous monitoring and formulation of effective bracken fern management with acceptable accuracies that are comparable to those obtained from the high resolution WorldView-2 commercial sensor.     

Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery

Accurate wetland maps are a fundamental requirement for land use management and for wetland restoration planning. Several wetland map products are available today; most of them based on remote sensing images, but their different data sources and mapping methods lead to substantially different estimations of wetland location and extent. We used two very high-resolution (2 m) WorldView-2 satellite images and one (30 m) Landsat 8 Operational Land Imager (OLI) image to assess wetland coverage in two coastal areas of Tampa Bay (Florida): Fort De Soto State Park and Weedon Island Preserve. An initial unsupervised classification derived from WorldView-2 was more accurate at identifying wetlands based on ground truth data collected in the field than the classification derived from Landsat 8 OLI (82% vs. 46% accuracy). The WorldView-2 data was then used to define the parameters of a simple and efficient decision tree with four nodes for a more exacting classification. The criteria for the decision tree were derived by extracting radiance spectra at 1500 separate pixels from the WorldView-2 data within field-validated regions. Results for both study areas showed high accuracy in both wetland (82% at Fort De Soto State Park, and 94% at Weedon Island Preserve) and non-wetland vegetation classes (90% and 83%, respectively). Historical, published land-use maps overestimate wetland surface cover by factors of 2–10 in the study areas. The proposed methods improve speed and efficiency of wetland map production, allow semi-annual monitoring through repeat satellite passes, and improve the accuracy and precision with which wetlands are identified.     

热红外遥感浙江地表热环境分布研究

全球气候变暖与人类戚戚相关,研究发现不仅是城市的热岛效应引起了局部区域的地表热环境分布差异,很多地质构造、岩性、土壤、植被等地质及自然地理因素也有影响。本研究选取浙江省作为研究区,利用Landsat 8 OLI/TIRS遥感影像反演地表温度,分析冬季地表热环境分布及其影响因子。结果表明,断裂带附近地表热环境受到断裂带分布影响;岩石和土壤通过不同的地表覆被类型影响地表温度。地表热环境分布与断裂带等自然因素存在一定相关性。     

Temperature and emissivity separation and mineral mapping based on airborne TASI hyperspectral thermal infrared data

Thermal infrared remote sensing (8–12 μm) (TIR) has great potential for geologic remote sensing studies. TIR has been successfully used for terrestrial and planetary geologic studies to map surface materials. However, the complexity of the physics and the lack of hyperspectral data make the studies under-investigated. A new generation of commercial hyperspectral infrared sensors, known as Thermal Airborne Spectrographic Imager (TASI), was used for image analysis and mineral mapping in this study. In this paper, a combined method integrating normalized emissivity method (NEM), ratio algorithm (RATIO) and maximum–minimum apparent emissivity difference (MMD), being applied in multispectral data, has been modified and used to determine whether this method is suitable for retrieving emissivity from TASI hyperspectral data. MODTRAN 4 has been used for the atmospheric correction. The retrieved emissivity spectra matched well with the field measured spectra except for bands 1, 2, and 32. Quartz, calcite, diopside/hedenbergite, hornblende and microcline have been mapped by the emissivity image. Mineral mapping results agree with the dominant minerals identified by laboratory X-ray powder diffraction and spectroscopic analyses of field samples. Both of the results indicated that the atmospheric correction method and the combined temperature–emissivitiy method are suitable for TASI image. Carbonate skarnization was first found in the study area by the spatial extent of diopside. Chemical analyses of the skarn samples determined that the Au content was 0.32–1.74 g/t, with an average Au content of 0.73 g/t. This information provides an important resource for prospecting for skarn type gold deposits. It is also suggested that TASI is suitable for prospect and deposit scale exploration.     

长春地区地表温度异常遥感分析

热红外遥感是一项探测地热资源、植被覆盖、农作物估产等生态环境评价研究的重要技术。本次使用Landsat 7/ETM＋热红外波段（band 6）,基于单通道算法,对长春地区地表温度应用反演,从而为研究该区地热资源、土地覆盖、城市热岛效应及环境评价提供可靠的依据。研究表明,热红外遥感能够有效探测到地表温度异常,而引起其异常的原因有待我们就一步验证和深入研究。     

Estimation of soil moisture using optical/thermal infrared remote sensing in the Canadian Prairies

A new approach to estimate soil moisture (SM) based on evaporative fraction (EF) retrieved from optical/thermal infrared MODIS data is presented for Canadian Prairies in parts of Saskatchewan and Alberta. An EF model using the remotely sensed land surface temperature (Ts)/vegetation index concept was modified by incorporating North American Regional Reanalysis (NAAR) Ta data and used for SM estimation. Two different combinations of temperature and vegetation fraction using the difference between Ts from MODIS Aqua and Terra images and Ta from NARR data (Ts−Ta Aqua-day and Ts−Ta Terra-day, respectively) were proposed and the results were compared with those obtained from a previously improved model (ΔTs Aqua-DayNight) as a reference. For the estimation of SM from EF, two empirical models were tested and discussed to find the most appropriate model for converting MODIS-derived EF data to SM values. Estimated SM values were then correlated with in situ SM measurements and their relationships were statistically analyzed. Results indicated statistically significant correlations between SM estimated from all three EF estimation approaches and field measured SM values (R² = 0.42–0.77, p values < 0.04) exhibiting the possibility to estimate SM from remotely sensed EF models. The proposed Ts−Ta MODIS Aqua-day and Terra-day approaches resulted in better estimations of SM (on average higher R² values and similar RMSEs) as compared with the ΔTs reference approach indicating that the concept of incorporating NARR Ta data into Ts/Vegetation index model improved soil moisture estimation accuracy based on evaporative fraction. The accuracies of the predictions were found to be considerably better for intermediate SM values (from 12 to 22 vol/vol%) with square errors averaging below 11 (vol/vol%)². This indicates that the model needs further improvements to account for extreme soil moisture conditions. The findings of this research can be potentially used to downscale SM estimations obtained from passive microwave remote sensing techniques.     

The use of motor-glider in topoclimatic studies

This paper reports on the advantages and disadvantages of motor-glider use in studying topoclimates. Despite the widespread use of images taken from low-altitude flying platforms (planes, helicopters, UAVs), the use of a motor-glider for imagery collection has not been reported in environmental studies. In presented study, the low-altitude remote sensing techniques were used to increase the spatial resolution of thermal maps derived from Landsat ETM+ thermal bands. Thermal images from motor-glider were taken by a thermovision camera. At the local scale, land surface temperature (LST) is one of the factors influencing topoclimatic diversity hence, by analysing LST distribution one can determine topoclimatic variability. Topoclimate has been the subject of previous studies, however, they have not used thermal remote sensing in the research process but instead relied on ground measurement network. The presented research contributes to better understanding of the thermal environment of the Earth by employing an innovative data collection method suitable for relatively large areas under specific weather conditions. The data collection with motor glider offers good spatial resolution of less than 1 m and facilitates the compilation of good quality LST maps. The paper discusses the influence of spatial resolution on LST variability and demonstrates gain in information granularity resulting from sub-meter resolution of collected data.     

Remote sensing of seasonal variability of fractional vegetation cover and its object-based spatial pattern analysis over mountain areas

Fractional vegetation cover (FVC) is an important indicator of mountain ecosystem status. A study on the seasonal changes of FVC can be beneficial for regional eco-environmental security, which contributes to the assessment of mountain ecosystem recovery and supports mountain forest planning and landscape reconstruction around megacities, for example, Beijing, China. Remote sensing has been demonstrated to be one of the most powerful and feasible tools for the investigation of mountain vegetation. However, topographic and atmospheric effects can produce enormous errors in the quantitative retrieval of FVC data from satellite images of mountainous areas. Moreover, the most commonly used analysis approach for assessing FVC seasonal fluctuations is based on per-pixel analysis regardless of the spatial context, which results in pixel-based FVC values that are feasible for landscape and ecosystem applications. To solve these problems, we proposed a new method that incorporates the use of a revised physically based (RPB) model to correct both atmospheric and terrain-caused illumination effects on Landsat images, an improved vegetation index (VI)-based technique for estimating the FVC, and an adaptive mean shift approach for object-based FVC segmentation. An array of metrics for segmented FVC analyses, including a variety of area metrics, patch metrics, shape metrics and diversity metrics, was generated. On the basis of the individual segmented FVC values and landscape metrics from multiple images of different dates, remote sensing of the seasonal variability of FVC was conducted over the mountainous area of Beijing, China. The experimental results indicate that (a) the mean value of the RPB–NDVI in all seasons was increased by approximately 10% compared with that of the atmospheric correction-NDVI; (b) a strong consistency was demonstrated between ground-based FVC observations and FVC estimated through remote sensing technology (R² = 0.8527, RMSE = 0.0851); and (c) seasonal changes in the landscape characteristics existed, and the landscape diversity reached its maximum in May and June in the study area.     

基于RS和GIS的松花江流域植被覆盖动态变化研究

利用遥感和地理信息系统技术对1989,1995年的Landsat TM数据和2002年Landsat ETM＋三期遥感数据进行处理,反演和计算松花江流域的归一化植被指数（NDVI）,在此基础上,获取研究区域植被覆盖度。在ArcGIS9.2软件空间分析模块的支持下,对研究区域三期植被覆盖影像进行叠加分析,以流域尺度和栅格尺度分析植被覆盖变化的时间和空间特性,获取研究区域植被覆盖度空间格局分布特征,为该区域植被覆盖度的自动化监测提供很好的技术支持。     

Estimating very high resolution urban surface temperature using a spectral unmixing and thermal mixing approach

Land surface temperature (LST) plays a critical role in characterizing energy exchanges of the Earth's surface and atmosphere. Recent advances in thermal infrared (TIR) remote sensing technology enable the emergence of airborne very-high-resolution (VHR) TIR sensors to identify detailed LST distribution for environmental, geological and urban applications. However, the usage of airborne VHR TIR data may be limited by its high cost, long acquisition period, extensive data processing, etc. A cost-effective alternative could be VHR LST estimation. We proposed a physically based method, referred to as the VHR spectral unmixing and thermal mixing (VHR-SUTM) approach, to estimate LST at the meter level. Particularly, considering both spectral and thermal properties, spectral unmixing was employed to estimate fractional urban compositions for a comprehensive representation of heterogeneous urban surfaces. Further, VHR LST was modeled as a summation of the thermal features of representative urban compositions weighted by their respective abundances. Results suggest a high agreement between the resampled VHR LST estimates and the retrieved LSTs. With relatively high estimation accuracy (RMSE of 2.02 K and MAE of 1.51 K), the VHR-SUTM technique could serve as a promising and practical method for various applications in urban and environment studies.     

Phenology from Landsat when data is scarce: Using MODIS and Dynamic Time-Warping to combine multi-year Landsat imagery to derive annual phenology curves

Green-leaf phenology describes the development of vegetation throughout a growing season and greatly affects the interaction between climate and the biosphere. Remote sensing is a valuable tool to characterize phenology over large areas but doing at fine- to medium resolution (e.g., with Landsat data) is difficult because of low numbers of cloud-free images in a single year. One way to overcome data availability limitations is to merge multi-year imagery into one time series, but this requires accounting for phenological differences among years. Here we present a new approach that employed a time series of a MODIS vegetation index data to quantify interannual differences in phenology, and Dynamic Time Warping (DTW) to re-align multi-year Landsat images to a common phenology that eliminates year-to-year phenological differences. This allowed us to estimate annual phenology curves from Landsat between 2002 and 2012 from which we extracted key phenological dates in a Monte-Carlo simulation design, including green-up (GU), start-of-season (SoS), maturity (Mat), senescence (Sen), end-of-season (EoS) and dormancy (Dorm). We tested our approach in eight locations across the United States that represented forests of different types and without signs of recent forest disturbance. We compared Landsat-based phenological transition dates to those derived from MODIS and ground-based camera data from the PhenoCam-network. The Landsat and MODIS comparison showed strong agreement. Dates of green-up, start-of-season and maturity were highly correlated (r 0.86-0.95), as were senescence and end-of-season dates (r > 0.85) and dormancy (r > 0.75). Agreement between the Landsat and PhenoCam was generally lower, but correlation coefficients still exceeded 0.8 for all dates. In addition, because of the high data density in the new Landsat time series, the confidence intervals of the estimated keydates were substantially lower than in case of MODIS and PhenoCam. Our study thus suggests that by exploiting multi-year Landsat imagery and calibrating it with MODIS data it is possible to describe green-leaf phenology at much finer spatial resolution than previously possible, highlighting the potential for fine scale phenology maps using the rich Landsat data archive over large areas.     

基于移动激光扫描点云数据和遥感图像的建筑物三维模型快速建模方法

建筑物的三维建模是城市三维建模和可视化的重要组成部分。本文提出一种基于点云数据与遥感图像的建筑物三维模型快速建模方法。首先,运用改进的RANSAC法从点云数据中提取建筑立面,根据立面区分平顶建筑与人字形屋顶建筑;在此基础上,进一步对建筑物的高度进行提取;之后,利用区域增长法从遥感图像中提取建筑物屋顶轮廓,利用形态学方法对提取出的轮廓进行规则化处理,并基于Freeman链码提取轮廓角点,得到规整的轮廓;最后,根据提取出的建筑高度属性对屋顶轮廓拉伸并进行纹理映射,实现对建筑物的三维重建。通过实例证明,提出的方法能快速、高效地实现建筑物三维模型的重建。     

Mapping degraded grassland on the Eastern Tibetan Plateau with multi-temporal Landsat 8 data — where do the severely degraded areas occur?

The Tibetan Plateau in Western China is the world’s largest alpine landscape, sheltering a rich diversity of native flora and fauna. In the past few decades, the Tibetan Plateau was found to suffer from grassland degradation processes. Grassland degradation is assumed to not only endanger biodiversity but also to increase the risk for natural hazards in other parts of the country which are ecologically and hydrologically connected to the area. However, the mechanisms behind the degradation processes remain poorly understood due to scarce baseline data and insufficient scientific research.We argue that remote sensing data can help to better understand degradation processes and patterns by: (1) identifying the distribution of severely degraded areas and (2) comparing the patterns of key spatial attributes of the identified areas (altitude above sea level, aspect, slope, administrative districts) with existing theories on degradation drivers. Therefore, we applied four Landsat 8 images covering large portions of the three counties Jigzhi, Baima and Darlag in the Eastern Tibetan Plateau. The dates of the Landsat scenes were selected to cover differing phenological stages of the ecosystem. Reference data were collected with a remotely piloted aircraft and a standard consumer RGB camera. To exploit the phenological information in the Landsat data as well as deal with the problem of cloud cover in multiple images, we developed a straightforward PCA-based procedure to merge the Landsat scenes. The merged Landsat data served as input to a supervised support vector machine classification which was validated with an iterative bootstrap procedure and an additional independent validation set. The considered classes were “high-cover grassland”, “grassland (including several stages of grassland vitality)”, “(severely) degraded grassland”, “green shrubland”, “grey shrubland”, “urban areas” and “water bodies”. Kappa accuracies ranged between 0.84 and 0.93 in the iterative procedure, while the independent validation led to a kappa accuracy of 0.76. Mean producer’s and user’s accuracies for all classes were higher than 80%, and confusion mainly occurred between the two shrubland classes and between the three grassland classes.Analysis of the slope, aspect and altitude values of the vegetation classes revealed that the degraded areas mostly occurred at the higher altitudes of the study area (4300–4600 m), with no strong connection to any specific slope or aspect. High-cover grassland was mostly located on sunny slopes at lower altitudes (less than 4300 m), while shrubland preferred shady, relatively steep slopes across all altitudes. These observations proved to be stable across the examined counties, while the proportions of land-cover classes differed between the examined regions. Most counties showed 5–7% severely degraded land cover. Darlag, the county located at the edge of the permafrost zone, and featuring the highest average altitude and lowest annual temperature and precipitation, was found to suffer from larger areas of severe degradation (14%).Therefore, our findings support a strong connection between degradation patterns and climatic as well as altitudinal gradients, with an increased degradation risk for high altitude areas and areas in colder and drier climatic zones. This is relevant information for pastoral management to avoid further degradation of high altitude pastures.     

Thermal infrared imaging of the temporal variability in stomatal conductance for fruit trees

Repeated measurements using thermal infrared remote sensing were used to characterize the change in canopy temperature over time and factors that influenced this change on ‘Conference’ pear trees (Pyrus communis L.). Three different types of sensors were used, a leaf porometer to measure leaf stomatal conductance, a thermal infrared camera to measure the canopy temperature and a meteorological sensor to measure weather variables. Stomatal conductance of water stressed pear was significantly lower than in the control group 9 days after stress began. This decrease in stomatal conductance reduced transpiration, reducing evaporative cooling that increased canopy temperature. Using thermal infrared imaging with wavelengths between 7.5 and13 μm, the first significant difference was measured 18 days after stress began. A second order derivative described the average rate of change of the difference between the stress treatment and control group. The average rate of change for stomatal conductance was 0.06 (mmol m⁻² s⁻¹) and for canopy temperature was −0.04 (°C) with respect to days. Thermal infrared remote sensing and data analysis presented in this study demonstrated that the differences in canopy temperatures between the water stress and control treatment due to stomata regulation can be validated.