Estimating agricultural production in marginal and food insecure areas in Kenya using very high resolution remotely sensed imagery

Regularly monitoring the amount of food produced in food insecure, isolated, subsistence farming areas can be used to help identify households or communities who may be in need of additional food resources. Measuring seasonal food production in developing countries, particularly at a sub-national level, is complicated by lack of data. In this study we use high resolution remotely sensed data to calculate cultivated area in two different growing areas, during two different seasons in Kenya. The results of the research support the usefulness of this approach for agricultural monitoring in the developing world and suggest that monitoring cultivated area requires attention to the specific growing characteristics of an area.     

Assessment of charcoal driven deforestation rates in a fragile rangeland environment in North Eastern Somalia using very high resolution imagery

Multi-temporal very high resolution satellite images and field work have been used for quantifying the tree cutting rate over a 5 years period in a very arid tiger bush area of North Eastern Somalia with intensive charcoal production activities. By applying both a classical area frame sampling approach with visual interpretation of the samples and a semi-automatic tree detection algorithm, it was possible to create baseline tree density layers for the 2 years of observation and to quantify the tree cutting rates for the period from 2001 to 2006. An average annual tree loss of −2.8%, coupled with the total absence of regrowth during the 5 years study period, confirm the tremendous ecological impacts of charcoal driven tree cutting on tiger bush vegetation. Analysis of the results evidences spatial and temporal patterns in the cutting locations and poses the basis for a better understanding of the ecological and human dimensions of deforestation in the fragile rangeland environment of Northern Somalia.     

The application of very high resolution satellite image in urban vegetation cover investigation： a case study of Xiamen City

With the technological improvements of satellite sensors, we will acquire more information about the earth so that we have reached a new application epoch of observation on earth environmental change and cartography. But with the enhancement of spatial resolution, some questions have arisen in the application of using traditional image processing and classification methods. Aiming for such questions, we studied the application of IKONOS very high resolution image (1 m) in Xiamen City on Urban Vegetation Cover Investigation and discussed the difference between the very high resolution image and traditional low spatial resolution image at classification,information abstraction etc. It is an advantageous test for the large-scale application of very high resolution data in the future.     

应用高精度卫星图像进行城市植被覆盖调查:以厦门市为例

With the technological improvements of satellite sensors, we will acquire more informationabout the earth so that we have reached a new application epoch of observation on earthenvironmental change and cartography. But with the enhancement of spatial resolution, somequestions have arisen in the application of using traditional image processing and classificationmethods. Aiming for such questions, we studied the application of IKONOS very high resolutionimage (1 m) in Xiamen City on Urban Vegetation Cover Investigation and discussed the differencebetween the very high resolution image and traditional low spatial resolution image at classification,information abstraction etc. It is an advantageous test for the large-scale application of very highresolution data in the future.     

The application of very high resolution satellite image in urban vegetation cover investigation: a case study of Xiamen City

With the technological improvements of satellite sensors, we will acquire more information about the earth so that we have reached a new application epoch of observation on earth environmental change and cartography. But with the enhancement of spatial resolution, some questions have arisen in the application of using traditional image processing and classification methods. Aiming for such questions, we studied the application of IKONOS very high resolution image (1 m) in Xiamen City on Urban Vegetation Cover Investigation and discussed the difference between the very high resolution image and traditional low spatial resolution image at classification, information abstraction etc. It is an advantageous test for the large-scale application of very high resolution data in the future.     

Increasing the spatial resolution of agricultural land cover maps using a Hopfield neural network

Land cover class composition of remotely sensed image pixels can be estimated using soft classification techniques increasingly available in many GIS packages. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques that attempt to provide an improved spatial representation of land cover have been developed, but not tested on the difficult task of mapping from real satellite imagery. The authors investigated the use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification previously. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. In previous studies, the authors described the application of the technique to target identification, pattern prediction and land cover mapping at the sub-pixel scale, but only for simulated imagery. We now show how the approach can be applied to Landsat Thematic Mapper (TM) agriculture imagery to derive accurate estimates of land cover and reduce the uncertainty inherent in such imagery. The technique was applied to Landsat TM imagery of small-scale agriculture in Greece and largescale agriculture near Leicester, UK. The resultant maps provided an accurate and improved representation of the land covers studied, with RMS errors for the Landsat imagery of the order of 0.1 in the new fine resolution map recorded. The results showed that the neural network represents a simple efficient tool for mapping land cover from operational satellite sensor imagery and can deliver requisite results and improvements over traditional techniques for the GIS analysis of practical remotely sensed imagery at the sub pixel scale.     

基于MODIS NDVI的新疆土地覆盖类型划分与潜在荒漠化区域探测

采用2002年MODIS 1km的全年NDVI时序数据对新疆及周边地区进行了土地覆盖分类,在分类的过程中重点强调了稀疏植被覆盖区域,这些区域具有潜在荒漠化的趋势。介绍了一种针对不同土地覆盖类型并能重点突出稀疏植被的分类方法,这种方法较好地综合了季节性影响因素和多变的自然条件影响因素。从16天合成的优化过的时序NDVI图像上,通过分析物候变化,可以获得较好的分类结果。将具有潜在荒漠化趋势的区域模型化研究以后,研究结果表明新疆及周边地区40万km2的土地有潜在荒漠化的趋势。由于MODIS NDVI数据覆盖范围较大,并且对植被的生长变化有较高的敏感度,所以它可以被有效地应用于监测大尺度环境变化和荒漠化进程。     

Using high resolution satellite imagery to estimate cropped area in Guatemala and Haiti

Guatemala and Haiti are two of the most food insecure nations in the Western Hemisphere. Measurements of food availability and access are instrumental in developing targeted hunger reduction strategies yet no estimates of cropped area (a critical input in the calculation of food production) at either a national or sub-national-level exist. The purpose of this research is to produce estimates of cropped area for Guatemala and Haiti using an area frame sampling approach and very high resolution (∼1 m) satellite imagery. Related research has combined livelihood data with topographic information to construct cropped area estimates in other settings using generalized additive models. We expand this approach with the inclusion of specific population variables in place of the livelihood data. We produce estimates of cropped area for the two countries and sub-national units and our results highlight the significance and complexity of incorporating explicit population characteristics into models of cropped area.     

An analysis of land use pattern in the industrial development city using high resolution satellite imagery

The spatial differentiation of land use changes of Tuticorin is studied using high resolution LISS III satellite imagery and Maximum Likelihood algorithms. The classification accuracy of 95.2% was obtained. In this study, the land use of Tuticorin is classified as settlement, salt pan, agricultural land, wasteland, water bodies and shrubs. The settlement area is increased to 4.6 km2 during the year 2001 and 2006. The settlement area change is mainly driven by growth of industries and migration of people from peripheral villages. Shrub is increased to 3.63 km2 in the six year period. Water logging due to growth of shrubs in Tuticorin leads to several environmental and health hazard. This study warrants proper urban planning for Tuticorin for sustainable use of resource and environment.     

利用高分辨率卫星影像分析工业城市的土地利用

The spatial differentiation of land use changes of Tuticorin is studied using high resolution LISS III satellite imagery and Maximum Likelihood algorithms. The classification accuracy of 95.2% was obtained. In this study,the land use of Tuticorin is classified as settlement,salt pan,agricultural land,wasteland,water bodies and shrubs. The settlement area is increased to 4.6 km2 during the year 2001 and 2006. The settlement area change is mainly driven by growth of industries and migration of people from peripheral villages. Shrub is increased to 3.63 km2 in the six year period. Water logging due to growth of shrubs in Tuticorin leads to several environmental and health hazard. This study warrants proper urban planning for Tuticorin for sustainable use of resource and environment.     

A surge of Perseibreen, Svalbard, examined using aerial photography and ASTER high resolution satellite imagery

The identification of surge activity is important in assessing the duration of the active and quiescent phases of the surge cycle of Svalbard glaciers. Satellite and aerial photographic images are used to identify and describe the form and flow of Perseibreen, a valley glacier of 59 km² on the east coast of Spitsbergen. Heavy surface crevassing and a steep ice front, indicative of surge activity, were first observed on Perseibreen in April 2002. Examination of high resolution (15 m) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery confirmed this surge activity. Perseibreen retreated by almost 750 m between 1961 and 1990. Between 1990 and the summer of 2000, Perseibreen switched from retreat and its front began to advance. Rapid advance was underway during the period June 2000 to May 2001, with terminus advance at over 400 m yr⁻¹. Between May and August 2001 the rate increased to over 750 m yr⁻¹. The observed crevasse orientation indicates that ice was in longitudinal tension, suggesting the down-glacier transfer of mass. Ice surface velocities, derived from image correlation between ASTER images, were 2-2.5 m d⁻¹ between May and August 2001. The glacier was flowing at a relatively uniform speed with sharp velocity gradients located close to its lateral margins, a velocity structure typical of ice masses in the active phase of the surge cycle. The stress regime is extensional throughout and the surge appears to be initiated low on the glacier. This is similar to the active-phase dynamics of other Svalbard tidewater glaciers. Perseibreen has probably been inactive since at least 1870, a period of about 130 years to the present surge which defines a minimum length for the quiescent phase.     

Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty

In this study, we propose, describe, and demonstrate a new geovisualization tool to demonstrate the use of exploratory and interactive visualization techniques for a visual fuzzy classification of remotely sensed imagery. The proposed tool uses dynamically linked views, consisting of an image display, a parallel coordinate plot, a 3D feature space plot, and a classified map with an uncertainty map. It allows a geoscientist to interact with the parameters of a fuzzy classification algorithm by visually adjusting fuzzy membership functions and fuzzy transition zones of land-cover classes. The purpose of this tool is to improve insight into fuzzy classification of remotely sensed imagery and related uncertainty. We tested our tool with a visual fuzzy land-cover classification of a Landsat 7 ETM+ image of an area in southern France characterized by objects with indeterminate boundaries. Good results were obtained with the visual classifier. Additionally, a focus-group user test of the tool showed that insight into a fuzzy classification algorithm and classification uncertainty improved considerably.     

过去2000年中国农耕区拓展与垦殖率变化基本特征

通过梳理和集成近年研究成果,综述了过去2000年中国主要农耕区拓展的阶段性及其间全国耕地面积和其中近千年垦殖率变化的主要特征.主要结论有:①中国主要农耕区第一次大规模拓展出现在西汉,从黄河中下游拓至整个长江以北地区;第二次在唐宋时期,主要是长江以南农耕区域从平原低地拓垦至丘陵山地;第三次在清中叶以后,主要是对东北、西北...     

中国积雪资源时空演变特征及其旅游开发关键区识别

积雪资源深刻影响着滑雪旅游的发展与布局,刻画中国积雪资源时空特征,识别旅游开发关键区,对中国冰雪旅游高质量发展具有重要意义。本文通过Mann-Kendall突变检验、热点分析、重要-感知实绩分析（IPA）等方法刻画中国1979—2020年积雪资源时空演变特征,构建资源稳定性-旅游开发适宜性指标体系,识别中国滑雪旅游开发关键区。结果表明：(1)中国积雪资源过去40年变化可划为3个阶段,1980—1995年（Ⅰ）积雪资源下降,1995—2010年（Ⅱ）增加,2010—2020年（Ⅲ）下降。在空间上,变化面积呈现Ⅰ-Ⅱ（33.2%）>Ⅱ-Ⅲ（31.1%）>Ⅰ-Ⅲ（29.3%）的特征;(2)中国积雪资源在月尺度上,1—3月（Ⅰ）积雪资源基本维持稳定,3—10月（Ⅱ）显著下降,10—12月（Ⅲ）恢复。在空间上,变化面积呈Ⅰ-Ⅲ（30.3%）>Ⅱ-Ⅲ（28.3%）>Ⅰ-Ⅱ（26.8%）的变化规律;(3)中国境内滑雪旅游开发极关键区面积占比为4.90%、关键区为11.69%、一般区为31.57%、不关键区为25.73%、极不关键区为26.11%,总体来看中国境内85%的区域不...     

Spatial variability in classification accuracy of agricultural crops in the Dutch national land-cover database

Variability in per cell classification accuracy is predominantly modelled with land-cover class as the explanatory variable, i.e. with users' accuracies from the error matrix. Logistic regression models were developed to include other explanatory variables: heterogeneity in the 3×3 window around a cell, the size of the patch and the complexity of the landscape in which a cell is located. It was found that per cell, the probability of correct classification was significantly (α=0.05) higher for cells with a less heterogeneous neighbourhood, for cells part of larger patches and for cells in regions with a less heterogeneous landscape. To validate the models, a leave-one-out procedure was applied in which the absolute difference between the actual and the model-estimated number of cells correctly classified was summarized over 55 regions in the Netherlands. The sum of differences reduced from 60.9 to 48.1 after adding the variables ‘patch size’ and ‘landscape dominance’ to the land-cover class model. Spatial variability thus modelled therefore led to a substantial improvement in the estimation of the per cell classification accuracy.     

畜禽粪便农用区土壤-小麦系统中重金属污染及迁移

畜禽粪便农用导致农产品中重金属含量超标,通过生物链传递,最终会影响人体健康。以山东禹城典型畜禽粪便农用区为例,研究了不同畜禽粪便农用区土壤—小麦系统中重金属的污染及迁移累积情况。结果显示:某些区域存在着一定的重金属污染,鸡粪农用区、猪粪农用区污染较高,综合污染指数均值分别为0.83、0.78,都达到了警戒级,其中某些点的污染指数超过了1和2,达到了轻污染或中污染级;牛粪农用区污染较轻,综合污染指数为安全级。不同畜禽粪便农用区小麦中的Cr、Ni、Cd、As均存在不同程度超标情况,其中鸡粪农用区Cr的超标率达66.67%,Ni、Pb超标率都为16.67%;牛粪农用区小麦中Cr、Ni超标,Cr超标率达91.67%;猪粪农用区小麦中只有Cr超标,超标率为41.67%。研究区小麦显示出不同的重金属富集能力,小麦对Cd、Zn、Cu几种元素的富集情况较高,而对Pb、As的富集能力则较低。除Zn外,小麦茎叶对同一种重金属的富集能力要大于小麦的种子。     

The use of Landsat imagery to assess large-scale forest cover changes in space and time,minimizing false-positive changes

A new approach, based on the application of multi-spectral remote sensing data of Landsat imagery, is introduced to determine large-scale spatiotemporal variations of forest cover changes quantitatively and with a high degree of precision. The test area covers about 837,330.5 ha of a mountainous region in Central Italy. The approach employs several multi-temporal Landsat acquisitions to account for forest cover changes larger than 0.5 ha for the period from March 2002 to July 2011. In contrast to automated approaches that strongly curtail mapping time, the approach introduced here allowed us to map only the real forest cover change, based on a robust validation and rectification of the detected forest change. Derived high spatial resolution data of forest change estimates indicate that about 5.7% (47,670.5 ha) of the observed forest area was subject to human-induced change between 2002 and 2011. Moreover, the detected forest cover changes, most of which are identifiable as timber harvesting, are considerably larger than those reported in the official statistics and often fall within the perimeter of restricted areas (i.e., national parks and natural reserves).     

Utility of Landsat 7 satellite data for continued monitoring of forest cover change in protected areas in Southeast Asia

Satellite instruments, particularly the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) series of sensors, are important tools in the interdisciplinary study of tropical forests that are increasingly integrated into studies that monitor changes in vegetation cover within tropical forests and tropical protected areas, and also applied with other types of data to investigate the drivers of land cover change. However, further advances in the use of Landsat to study and monitor tropical forests and protected areas are threatened by the scan line corrector failure on the ETM+ sensor, as well as uncertainty about the continuity of the Landsat mission. Given these problems, this paper illustrates how ETM+ data were used in an interdisciplinary study that effectively monitored forest cover change in Gunung Palung National Park in West Kalimantan, Indonesian Borneo. Following 31 May 2003, when the ETM+ sensor's scan line corrector failed, we analysed how this failure impedes our ability to perform a similar study from this date onwards. This analysis uses six simulated post-scan line corrector failure (SLC-off) images and reveals that data gaps caused by SLC-off introduce maximum errors of 1.47 per cent and 4.04 per cent in estimates of forest cover and rates of forest loss, respectively. The analysis also demonstrates how SLC-off has transformed ETM+ data from a complete inventory dataset to a statistical sample with variable sample fraction, and notes how this data loss will confound the use of Landsat data to model land cover change in a spatially explicit manner. We discuss potential limited uses of SLC-off data and suggest alternative sensors that may provide essential remotely sensed data for monitoring tropical forests in Southeast Asia.     

中国典型山区农业水土资源匹配格局变化——以太行山区、横断山区、黔桂喀斯特山区为例

为了解中国典型山区农业水土资源分布的匹配程度及其格局变化,研究以区域内单位面积耕地拥有的农业水资源量刻画太行山区、横断山区和黔桂喀斯特山区农业水土资源的时空匹配情况。结果表明：① 三大山区耕地分布的集中程度较高。太行山区的耕地主要分布在河北境内的北段地区和山西境内的南段地区,横断山区的耕地主要分布在南段地区,喀斯特山区的耕地分布呈现“南北多中部少”的格局。② 三大山区农业水资源的分布及变化都主要集中在低海拔地区。太行山区的农业水资源主要分布在山西、河南段,横断山区的农业水资源主要分布在四川境内,喀斯特山区的农业水资源较均匀地分布在广西和贵州境内。③ 太行山区农业水土资源匹配程度较差,横断山区、喀斯特山区农业水土资源匹配程度较好。2000—2015年间,太行山区中部、横断山区东部、喀斯特山区南部地区的农业水土匹配程度有变好趋势,而太行山区南部、横断山区西南部、喀斯特山区北部地区的农业水土匹配程度有变差趋势。研究时段内,太行山区的水田转旱地情况突出,太行山区也是三大山区中农业水土资源匹配程度最差的,应该关注太行山区的用水压力,重视农田水利设施维护和农田灌溉水有效利用。