Comparing and Combining Landsat Satellite Imagery and Participatory Data to Assess Land-Use and Land-Cover Changes in a Coastal Village in Papua New Guinea

In regions lacking socio-economic data, pairing satellite imagery with participatory information is essential for accurate land-use/cover (LULC) change assessments. At the village scale in Papua New Guinea we compare swidden LULC classifications using remote sensing analyses alone and analyses that combine participatory information and remotely sensed data. These participatory remote sensing (PRS) methods include participatory land-use mapping, household surveys, and validation of image analysis in combination with remotely sensed data. The classifications of the swidden area made using only remote sensing analysis show swidden areas are, on average, two and a half times larger than land managers reported for 1999 and 2011. Classifications made using only remote sensing analysis are homogeneous and lack discrimination among swidden plots, fallow land, and non-swidden vegetation. The information derived from PRS methods allows us to amend the remote sensing analysis and as a result swidden areas are more similar to actual swidden area found when ground-truthing. We conclude that PRS methods are needed to understand swidden system LULC complexities.     

Remote sensing imagery in vegetation mapping: a review

Aims Mapping vegetation through remotely sensed images involves various considerations, processes and techniques. Increasing availability of remotely sensed images due to the rapid advancement of remote sensing technology expands the horizon of our choices of imagery sources. Various sources of imagery are known for their differences in spectral, spatial, radioactive and temporal characteristics and thus are suitable for different purposes of vegetation mapping. Generally, it needs to develop a vegetation classification at first for classifying and mapping vegetation cover from remote sensed images either at a community level or species level. Then, correlations of the vegetation types (communities or species) within this classification system with discernible spectral characteristics of remote sensed imagery have to be identified. These spectral classes of the imagery are finally translated into the vegetation types in the image interpretation process, which is also called image processing. This paper presents an overview of how to use remote sensing imagery to classify and map vegetation cover.Methods Specifically, this paper focuses on the comparisons of popular remote sensing sensors, commonly adopted image processing methods and prevailing classification accuracy assessments.Important findings The basic concepts, available imagery sources and classification techniques of remote sensing imagery related to vegetation mapping were introduced, analyzed and compared. The advantages and limitations of using remote sensing imagery for vegetation cover mapping were provided to iterate the importance of thorough understanding of the related concepts and careful design of the technical procedures, which can be utilized to study vegetation cover from remote sensed images.     

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods

Ecosystem service‐based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot‐level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community‐weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.     

Integrating vegetation field surveys with remotely sensed data

Summary   This paper explores data compatibility issues arising from the assessment of remnant native vegetation condition using satellite remote sensing and field-based data. Space-borne passive remote sensing is increasingly used as a way of providing a total sample and synoptic overview of the spectral and spatial characteristics of native vegetation canopies at a regional scale. However, integrating field-collected data often not designed for integration with remotely sensed data can lead to data compatibility issues. Subsequent problems associated with the integration of unsuited datasets can contribute to data uncertainty and result in inconclusive findings. It is these types of problems (and potential solutions) that form the basis of this paper. In other words, how can field surveys be designed to support and improve compatibility with remotely sensed total surveys? Key criteria were identified for consideration when designing field-based surveys of native vegetation condition (and other similar applications) with the intent to incorporate remotely sensed data. The criteria include recommendations for the siting of plots, the need for reference location plots, the number of sample sites and plot size and distribution, within a study area. The difficulties associated with successfully integrating these data are illustrated using real examples taken from a study of the vegetation in the Little River Catchment, New South Wales, Australia.     

Mapping Tension: Remote Sensing and the Production of a Statewide Land Cover Map

This article examines the process by which remotely sensed land cover maps work to both simplify and complicate landscapes. The central argument is not merely that the construction of land cover maps is complex, but that the points of complexity often arise through the process of trying to simplify. In other words, the forces of complexity are intimately connected to the forces of simplicity and vice versa. This article takes as a case study the production of WISCLAND, (Wisconsin Initiative for Statewide Cooperation on Landscape Analysis and Data), a statewide land cover map of Wisconsin derived from remote sensing data and GIS (Geographic Information Systems) technologies and proceeds by analysis of mapping methodology, practice, and representation. In addition to the development of a more nuanced critique of the use of land cover maps, it facilitates the possibility for a constructive dialogue between remote sensing practitioners and the critical GIS community.     

Analysing and Correcting the Differences between Multi-Source and Multi-Scale Spatial Remote Sensing Observations

Differences exist among analysis results of agriculture monitoring and crop production based on remote sensing observations, which are obtained at different spatial scales from multiple remote sensors in same time period, and processed by same algorithms, models or methods. These differences can be mainly quantitatively described from three aspects, i.e. multiple remote sensing observations, crop parameters estimation models, and spatial scale effects of surface parameters. Our research proposed a new method to analyse and correct the differences between multi-source and multi-scale spatial remote sensing surface reflectance datasets, aiming to provide references for further studies in agricultural application with multiple remotely sensed observations from different sources. The new method was constructed on the basis of physical and mathematical properties of multi-source and multi-scale reflectance datasets. Theories of statistics were involved to extract statistical characteristics of multiple surface reflectance datasets, and further quantitatively analyse spatial variations of these characteristics at multiple spatial scales. Then, taking the surface reflectance at small spatial scale as the baseline data, theories of Gaussian distribution were selected for multiple surface reflectance datasets correction based on the above obtained physical characteristics and mathematical distribution properties, and their spatial variations. This proposed method was verified by two sets of multiple satellite images, which were obtained in two experimental fields located in Inner Mongolia and Beijing, China with different degrees of homogeneity of underlying surfaces. Experimental results indicate that differences of surface reflectance datasets at multiple spatial scales could be effectively corrected over non-homogeneous underlying surfaces, which provide database for further multi-source and multi-scale crop growth monitoring and yield prediction, and their corresponding consistency analysis evaluation.     

遥感用于森林生物多样性监测的进展

随着物种和栖息地的丧失,全球范围的生物多样性保护已经成为迫切的需要。航空航天技术的迅猛发展使遥感成为能提供跨越不同时空尺度监测陆地生态系统生物多样性的重要工具,这方面的研究在欧美等国已经有了小范围的开展,在国内刚刚起步。国外关于生物多样性遥感探测的方法基本有3种:1.利用遥感数据直接对物种或生境制图,进而估算生物多样性;2 .建立遥感数据的光谱反射率与地面观测物种多样性的关系模型;3.与野外调查数据结合直接在遥感数据上进行生物多样性指数制图。研究表明,物种直接制图法只能应用于较小的范围;生境制图的方法,应用广泛,技术相对成熟,研究范围局限于几百公里的范畴,但不能获取生境内部的多样性信息。光谱模型技术目前正处于探索阶段,对于植被复杂、生物多样性高的地域,具有较大的应用潜力。在遥感数据上直接进行生物多样性制图在加拿大已经得到了应用。     

北京海淀区植被覆盖的遥感动态研究

 植被覆盖度fg人(植被的垂直投影面积与单位面积之比)是一个十分重要的生态气候参数。为了有效地从遥感资料中提取植被覆盖度,发展了一套计算区域植被覆盖度的亚象元分解模型法。运用该方法对北京市海淀区1975、1991和1997年的植被覆盖度进行了计算,并在此基础上,求得研究区不同植被覆盖等级的变化转移矩阵,分析了海淀区22年来植被覆盖等级变化的空间过程和变化趋势。     

基于遥感信息的作物模型重新初始化/参数化方法研究初探

作物模型与遥感信息的结合有助于利用遥感监测的大范围植被信息解决作物模型区域应用时模型初始状态和参数值难以确定的问题。该文借助叶面积指数(LAI)将经过华北冬小麦(Triticum aestivium)适应性调整的WOFOST模型与经参数调整检验的SAIL-PROSPECT模型相嵌套,利用嵌套模型模拟作物冠层的土壤调整植被指数(SAVI),在代表点上借助FSEOPT优化程序使模拟SAVI_s与MODIS遥感数据合成SAVI_m的差异达到最小,从而对WOFOST模型重新初始化。结果表明,借助于遥感信息,出苗期的重新初始化使模拟成熟期与按实际出苗期模拟的结果相差在2天以内,模拟的LAI和总干重的误差比按实际出苗期模拟结果的误差降低3~8个百分点;返青期生物量的重新初始化使模拟LAI和地上总干重在关键发育时刻的误差降至16%以内,模拟LAI和贮存器官重在整个生育期内都更加接近实测值;对返青期生物量的动态调整显示返青到抽穗期间较少次数的遥感数据即能有效地提高作物模型的模拟效果。与国外同类研究相比,该文在作物模型本地化、重新初始化变量和优化比较对象的选择上都有所不同,而利用遥感数据动态调整作物模型初始状态或参数值更具有新意。该文对区域尺度上利用遥感信息优化作物模型的研究具有基础性、探讨性意义。     

遥感主体图的准确度对景观生态学研究的影响

用各种案例系统地解释了遥感数据分类误差对景观指数误差的必然影响。一方面 ,遥感数据在各种时间和空间尺度上为景观生态学研究提供必需的土地类型数据 ;另一方面 ,遥感技术的灵活性和复杂性可以产生出各种质量的土地类型数据。但景观生态学方面的用户对土地类型数据基本上是没有选择地使用 ,甚至是不知好坏地使用 ,所以景观生态学的发现和结论具有不可避免的任意性。总结了在各种情况下景观指数的变动区间 ,指出了现实较低的遥感数据的分类准确度会引起更低的景观指数的准确度 ,当进行景观变化分析时 ,这种误差的放大效应将更加明显。当前 ,人们对除面积以外的景观指数的误差仍然束手无策 ,尽可能地提高遥感数据的分类准确度是唯一力所能及的办法。     

Leakiness: A new index for monitoring the health of arid and semiarid landscapes using remotely sensed vegetation cover and elevation data

The health of arid and semiarid lands needs to be monitored, particularly if they are used to produce food and fiber, and are prone to loss of vegetation cover and soil. Indicators of landscape health based on remotely sensed data could cost-effectively integrate structural and functional attributes of land surfaces across a range of scales. In this paper, we describe a new index for remotely monitoring changes in the health of land. The new index takes important aspects of landscape structure and function into account by focusing on the potential for landscapes to lose or ‘leak’ (not retain) soil sediments. We combined remotely sensed vegetation patchiness data with digital elevation model (DEM) data to derive a quantitative metric, the landscape leakiness index, LI. This index is strongly linked to landscape function by algorithms that reflect the way in which spatial configuration of vegetation cover and terrain affect soil loss. Linking LI to landscape function is an improvement on existing indicators that are based on qualitatively assessing remotely sensed changes in vegetation cover. Using archived Landsat imagery and Shuttle Radar Topography Mission DEMs, we found for example that LI indicated improvements in the condition or health of a rangeland paddock that was monitored from 1980 to 2002. This paddock is located in central Australia and its improved health is documented by photographs and field data. Although the full applicability of LI remains to be explored, we have demonstrated that it has the potential to serve as a useful ecological indicator for monitoring the health of arid and semiarid landscapes.     

遥感技术对早期植物病毒病的诊断

本文讨论了利用遥感技术对植物病毒病早期诊断的实验研究。试验表明,该方法对植物病毒的检测是很敏感的。本研究结果为今后利用航空、卫星遥感进行大面积植物病害的监测提供了依据。     

基于多源遥感信息融合的广东省土地利用分类方法——以雷州半岛为例

准确高效的获取土地利用信息对生态环境评价非常重要。广东省地处华南热带和亚热带季风气候区,经济作物种类繁多,土地覆盖破碎,为土地利用精确分类带来很大不确定性,而常年多云雨的天气也为有效光学影像的获取带来困难。为提高土地覆盖分类精度,以雷州半岛为实验区,综合应用Landsat-TM/ETM、多时相HJ光学影像,以及X波段Terra SAR数据,通过分析不同地物类型在光谱、极化以及多时相特征上的差别,对原始图像进行特征提取。在此基础上融合多源遥感信息的地物特征运用面向对象土地覆盖分类方法获取研究区高精度的土地利用信息。结果显示这一方法能有效提高土地覆盖利用信息获取精度,为研究生态环境变化提供更准确的数据支持。     

Satellite solar-induced chlorophyll fluorescence tracks physiological drought stress development during 2020 southwest US drought

Monitoring and estimating drought impact on plant physiological processes over large regions remains a major challenge for remote sensing and land surface modeling, with important implications for understanding plant mortality mechanisms and predicting the climate change impact on terrestrial carbon and water cycles. The Orbiting Carbon Observatory 3 (OCO-3), with its unique diurnal observing capability, offers a new opportunity to track drought stress on plant physiology. Using radiative transfer and machine learning modeling, we derive a metric of afternoon photosynthetic depression from OCO-3 solar-induced chlorophyll fluorescence (SIF) as an indicator of plant physiological drought stress. This unique diurnal signal enables a spatially explicit mapping of plants' physiological response to drought. Using OCO-3 observations, we detect a widespread increasing drought stress during the 2020 southwest US drought. Although the physiological drought stress is largely related to the vapor pressure deficit (VPD), our results suggest that plants' sensitivity to VPD increases as the drought intensifies and VPD sensitivity develops differently for shrublands and grasslands. Our findings highlight the potential of using diurnal satellite SIF observations to advance the mechanistic understanding of drought impact on terrestrial ecosystems and to improve land surface modeling.     

白洋淀湿地生态系统水分条件遥感监测方法

湿地水文条件对湿地生态系统结构和功能起到关键作用。利用遥感获取与湿地水分条件直接相关的生物物理变量,包括归一化植被指数（NDVI）和地表温度,探讨监测湿地挺水植物缺水状况的可能性,并探讨了建立湿地水分遥感监测的新方法。回归分析表明,对于同一挺水植物而言,在湿地旱化的条件下,由于植物的蒸腾作用的差异,在植被生长状况（NDVI）相同的情况下,地势较高处植物的冠层温度亦较高;在生长处高度相同的情况下,植被覆盖度高（NDVI值高）的地方,植物的冠层温度较低。这说明可以通过地表温度和NDVI来监测挺水植物的缺水程度。     

Pasture degradation in the central Amazon: linking changes in carbon and nutrient cycling with remote sensing

The majority of deforested land in the Amazon Basin has become cattle pasture, making forest‐to‐pasture conversion an important contributor to the carbon (C) and climate dynamics of the region. However, our understanding of biogeochemical dynamics in pasturelands remains poor, especially when attempting to scale up predictions of C cycle changes. A wide range of pasture ages, soil types, management strategies, and climates make remote sensing the only realistic means to regionalize our understanding of pasture biogeochemistry and C cycling over such an enormous geographic area. However, the use of remote sensing has been impeded by a lack of effective links between variables that can be observed from satellites (e.g. live and senescent biomass) and variables that cannot be observed, but which may drive key changes in C storage and trace gas fluxes (e.g. soil nutrient status). We studied patterns in canopy biophysical–biochemical properties and soil biogeochemical processes along pasture age gradients on two important soil types in the central Amazon. Our goals were to (1) improve our understanding of the plot‐scale biogeochemical dynamics of this land‐use change, (2) evaluate the effects of pasture development on two contrasting soil types (clayey Oxisols and sandy Entisols), and (3) attempt to use remotely sensed variables to scale up the site‐specific variability in biogeochemical conditions of pasturelands. The biogeochemical analyses showed that (1) aboveground and soil C stocks decreased with pasture age on both clayey and sandy soils, (2) declines in plant biomass were well correlated with declines in soil C and with available phosphorus (P) and calcium (Ca), and (3) despite low initial values for total and available soil P, ecosystem P stocks declined further with pasture age, as did a number of other nutrients. Spectral mixture analysis of Landsat imagery provided estimates of photosynthetic vegetation (PV) and non‐photosynthetic vegetation (NPV) that were highly correlated with field measurements of these variables and plant biomass. In turn, the remotely sensed sum PV+NPV was well correlated with the changes in soil organic carbon and nitrogen, and available P and Ca. These results suggest that remote sensing can be an excellent indicator of not only pasture area, but of pasture condition and C storage, thereby greatly improving regional estimates of the environmental consequences of such land‐use change.     

Temporal transferability of wildlife habitat models: implications for habitat monitoring

Aim Temporal transferability is an important issue when habitat models are used beyond the time frame corresponding to model development, but has not received enough attention, particularly in the context of habitat monitoring. While the combination of remote sensing technology and habitat modelling provides a useful tool for habitat monitoring, the effect of incorporating remotely sensed data on model transferability is unclear. Therefore, our objectives were to assess how different satellite‐derived variables affect temporal transferability of habitat models and their usefulness for habitat monitoring. Location Wolong Nature Reserve, Sichuan Province, China. Methods We modelled giant panda habitat with the maximum entropy algorithm using panda presence data collected in two time periods and four different sets of predictor variables representing land surface phenology. Each predictor variable set contained either a time series of smoothed wide dynamic range vegetation index (WDRVI) or 11 phenology metrics, both derived from single‐year or multi‐year (i.e. 3‐year) remotely sensed imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the ability of models obtained with these four variable sets to predict giant panda habitat within and across time periods by using threshold‐independent and threshold‐dependent evaluation methods and five indices of temporal transferability. Results Our results showed that models developed with the four variable sets were all useful for characterizing and monitoring giant panda habitat. However, the models developed using multi‐year data exhibited significantly higher temporal transferability than those developed using single‐year data. In addition, models developed with phenology metrics, especially when using multi‐year data, exhibited significantly higher temporal transferability than those developed with the time series. Main conclusions The integration of land surface phenology, captured by high temporal resolution remotely sensed imagery, with habitat modelling constitutes a suitable tool for characterizing wildlife habitat and monitoring its temporal dynamics. Using multi‐year phenology metrics reduces model complexity, multicollinearity among predictor variables and variability caused by inter‐annual climatic fluctuations, thereby increasing the temporal transferability of models. This study provides useful guidance for habitat monitoring through the integration of remote sensing technology and habitat modelling, which may be useful for the conservation of the giant panda and many other species.     

Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.     

Tree leaf out response to temperature: comparing field observations,remote sensing,and a warming experiment

Leaf out time is a widely used indicator of climate change and represents a critical transition point of annual seasonality in most temperate ecosystems. We compared three sources of data to determine the effect of spring temperature on tree leaf out: field observations, remotely sensed satellite data, and experimental warming. All three methods recorded earlier leaf out with warmer spring temperatures. However, leaf out timing was more than twice as sensitive to temperature in the field study (advancing at a rate of 6.1 days/°C), as under experimental warming (2.1 days/°C), with remote sensing intermediate (3.7 days/°C). Researchers need to be aware of the currently unexplained differences among methodologies when using phenological data to parameterize or benchmark models that represent ecosystem processes. The mechanisms behind these discrepancies must be better understood if we are to confidently predict responses of leaf out timing to future climates.