Change vector analysis to categorise land cover change processes using the tasselled cap as biophysical indicator

The continuous extraction of wood and the conversion of forest to small- and large-scale agricultural parcels is rapidly changing the land cover of the mount Cameroon region. The changes occur at varying spatial scales most often not more than 2ha for the small-scale subsistence farms and above 10ha for the extensive agricultural plantations of cocoa and palm. Given the importance of land use and land cover data in conservation planning, accurate and efficient techniques to provide up-to-date change information are required. A number of techniques for realising the detection of land cover dynamics using remotely sensed imagery have been formulated, tested and assessed with the results varying with respect to the change scenario under investigation, the information required and the imagery applied. In this study the Change Vector Analysis (CVA) technique was implemented on multitemporal multispectral Landsat data from the Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) sensors to monitor the dynamics of forest change in the mount Cameroon region. CVA was applied to multi-temporal data to compare the differences in the time-trajectory of the tasseled cap greenness and brightness for two successive time periods - 1987 and 2002. The tasseled cap was selected as biophysical indicator because it optimises the data viewing capabilities of vegetation, representing the basic types of land cover - vegetation, soil and water. Classes were created arbitrarily to predict the technique's potential in monitoring forest cover changes in the mount Cameroon region. The efficiency of the technique could not be fully assessed due to the inavailability of sufficient ground truth data. Assessment was based on the establishment of an error matrix of change versus no-change. The overall accuracy was 70%. The technique nevertheless demonstrated immense potentials in monitoring forest cover change dynamics especially when complemented with field studies.     

A change vector analysis technique for monitoring land cover changes in Copsa Mica,Romania, in the period 1985–2011

During the communist regime, Romania’s planned economy focused exclusively on production neglecting the environment protection. The lack of less polluting production technologies and of environmental protection measures led to excessive pollution in certain industrialized areas. This is the case of the town of Copsa Mica in Sibiu County, which in 1987 was considered one of the most polluted towns in Europe. The present study assesses the change vector analysis (CVA) technique using a Landsat Thematic Mapper (TM) image time series to monitor land cover changes caused by carbon black and heavy metal pollution. CVA was applied to the tasseled cap greenness (TCG) and tasseled cap brightness (TCB) indices, as well as to the Normalized Difference Vegetation Index (NDVI) and bare soil index (BI). Various maps were generated for the periods 1985–1994, 1994–2003, 2003–2011, and 1985–2011, and threshold values were determined for the detection of land cover change/no change. The change direction and magnitude values were cross-tabulated and classified. The technique was assessed based on the change versus no-change error matrix. The results show that in the area of Copsa Mica, land cover changes occurred because of a considerable decrease in the area affected by carbon black and heavy metal pollution. The CVA technique proved efficient in monitoring the land cover changes caused by pollution and especially by carbon black pollution. Soil pollution by heavy metals is reflected in the bare soil surfaces present in the imagery.     

Environmental change monitoring in the arid and semi-arid regions: a case study Al-Basrah Province, Iraq

In recent years, land use/cover dynamic change has become a key subject urgently to be dealt with in the study of global environmental change. This research utilizes the integrated remote sensing and geographic information systems (GIS) in the southern part of Iraq (Basrah Province was taken as a case) to monitor, map, and quantify the environmental change using a 1:250,000 mapping scale. Remote sensing and GIS software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation land, sand land, urban area, unused land, and water bodies. Supervised classification and normalized difference buildup index, normalized difference vegetation index, normalized difference bare land index, the normalized differential water index, crust index (CI) algorithms, and change detection techniques were adopted in this research and used, respectively, to retrieve its class boundary. An accuracy assessment was performed on the 2003 LULC map to determine the reliability of the map. Finally, GIS software was used to quantify and illustrate the various LULC conversions that took place over the 13-year span of time. The results showed that the urban area, sand lands, and bare lands had increased by the rate of 1.2%, 0.8%, and 0.4% per year, with area expansion from 3,299.1, 4,119.1 km², and 3,201.9 km² in 1990 to 3,794.9, 4,557.7, and 3,351.7 km² in 2003, respectively. While the vegetation cover and water body classes were about 43.5% in 1990, the percentage decreased to about 39.6% in 2003. This study demonstrates the effectiveness of the remote sensing and GIS technologies in detecting, assessing, mapping, and monitoring the environmental changes.     

Assessment of some remote sensing techniques used to detect land use/land cover changes in South-East Transilvania,Romania

This paper assesses the image differencing technique for the Normalized Difference Vegetation Index (NDVI), the second principal component (PC2), and the TM 4 band (TM 4), as well as the post-classification comparison (PCC) in order to analyze the land use/land cover changes in the South-East Transilvania, Romania. The analysis was performed using two frames from Landsat 5 TM satellite images acquired on August 5, 1993 and July 24, 2009. After applying the NDVI, PC2, and TM 4 image differencing techniques, the images obtained were transformed into change/no change maps. The thresholds identified to highlight the changes were set at 0.6 s for NDVI and 0.7 s for PC2 and TM 4. Before applying the PCC technique, the satellite images were classified through the supervised classification method. The overall accuracy obtained was 85.91 % and the kappa statistics 0.8249 for 1993, 88.18 % and 0.8497 for 2009, respectively. The assessment of the changes detection methods in the studied area shows that the first place is occupied by NDVI image differencing with an overall accuracy of 83.80 %, followed by PCC method with 83.20 %, PC2 difference with an overall accuracy of 81.60 %, and TM 4 difference with an overall accuracy of 79.40 %.     

Applying the change vector analysis technique to assess the desertification risk in the south-west of Romania in the period 1984–2011

The desertification risk affects around 40% of the agricultural land in various regions of Romania. The purpose of this study is to analyse the risk of desertification in the south-west of Romania in the period 1984–2011 using the change vector analysis (CVA) technique and Landsat thematic mapper (TM) satellite images. CVA was applied to combinations of normalised difference vegetation index (NDVI)-albedo, NDVI-bare soil index (BI) and tasselled cap greenness (TCG)-tasselled cap brightness (TCB). The combination NDVI-albedo proved to be the best in assessing the desertification risk, with an overall accuracy of 87.67%, identifying a desertification risk on 25.16% of the studied period. The classification of the maps was performed for the following classes: desertification risk, re-growing and persistence. Four degrees of desertification risk and re-growing were used: low, medium, high and extreme. Using the combination NDVI-albedo, 0.53% of the analysed surface was assessed as having an extreme degree of desertification risk, 3.93% a high degree, 8.72% a medium degree and 11.98% a low degree. The driving forces behind the risk of desertification are both anthropogenic and climatic causes. The anthropogenic causes include the destruction of the irrigation system, deforestation, the destruction of the forest shelterbelts, the fragmentation of agricultural land and its inefficient management. Climatic causes refer to increase of temperatures, frequent and prolonged droughts and decline of the amount of precipitation.     

Seagrass resource assessment using remote sensing methods in St. Joseph Sound and Clearwater Harbor,Florida, USA

In the event of a natural or anthropogenic disturbance, environmental resource managers require a reliable tool to quickly assess the spatial extent of potential damage to the seagrass resource. The temporal availability of the Landsat 5 Thematic Mapper (TM) imagery provided a suitable option to detect and assess damage of the submerged aquatic vegetation (SAV). This study examined Landsat TM imagery classification techniques to create two-class (SAV presence/absence) and three-class (SAV estimated coverage) SAV maps of the seagrass resource. The Mahalanobis Distance method achieved the highest overall accuracy (86%) and validation accuracy (68%) for delineating the seagrass resource (two-class SAV map). The Maximum Likelihood method achieved the highest overall accuracy (74%) and validation accuracy (70%) for delineating the seagrass resource three-class SAV map. The Landsat 5 TM imagery classification provided a seagrass resource map product with similar accuracy to the aerial photointerpretation maps (validation accuracy 71%). The results support the application of remote sensing methods to analyze the spatial extent of the seagrass resource.     

Quantifying structural physical habitat attributes using LIDAR and hyperspectral imagery

Structural physical habitat attributes include indices of stream size, channel gradient, substrate size, habitat complexity, and riparian vegetation cover and structure. The Environmental Monitoring and Assessment Program (EMAP) is designed to assess the status and trends of ecological resources at different scales. High-resolution remote sensing provides unique capabilities in detecting a variety of features and indicators of environmental health and condition. LIDAR is an airborne scanning laser system that provides data on topography, channel dimensions (width, depth), slope, channel complexity (residual pools, volume, morphometric complexity, hydraulic roughness), riparian vegetation (height and density), dimensions of riparian zone, anthropogenic alterations and disturbances, and channel and riparian interaction. Hyperspectral aerial imagery offers the advantage of high spectral and spatial resolution allowing for the detection and identification of riparian vegetation and natural and anthropogenic features at a resolution not possible with satellite imagery. When combined, or fused, these technologies comprise a powerful geospatial data set for assessing and monitoring lentic and lotic environmental characteristics and condition.     

Monitoring habitat preserves in southern California using high spatial resolution multispectral imagery

Habitat preserve systems have been established adjacent to the densely populated regions of southern California to support indigenous plant and animal species that are listed as rare, threatened, or endangered. Monitoring the condition of habitat across these broad preserves is necessary to ensure their long-term viability and may be effectively accomplished using remote sensing techniques with high spatial resolution visible and near-infrared (VNIR) multispectral imagery. The utility of 1 m spatial resolution VNIR imagery for detailed change detection and monitoring of Mediterranean-type ecosystems is assessed here. Image acquisition and preprocessing procedures were conducted to ensure that image-detected changes represented real changes and not artifacts. Change classification products with six spectral-based transition classes were generated using multiband image differencing (MID) for three change periods: 1998-1999, 1998-2001, and 1998-2005. Land cover changes relevant to habitat quality monitoring such as human-induced disturbance, fire, vegetation growth/recovery, and drought related vegetation stress were readily detected using the multitemporal VNIR imagery. Suggestions for operational habitat monitoring using image products and mobile geographic information system technologies are provided.     

Spatiotemporal analysis of the effect of climate change on vegetation health in the Drakensberg Mountain Region of South Africa

The impact of climate change on mountain ecosystems has been in the spotlight for the past three decades. Climate change is generally considered to be a threat to ecosystem health in mountain regions. Vegetation indices can be used to detect shifts in ecosystem phenology and climate change in mountain regions while satellite imagery can play an important role in this process. However, what has remained problematic is determining the extent to which ecosystem phenology is affected by climate change under increasingly warming conditions. In this paper, we use climate and vegetation indices that were derived from satellite data to investigate the link between ecosystem phenology and climate change in the Namahadi Catchment Area of the Drakensberg Mountain Region of South Africa. The time series for climate indices as well as those for gridded precipitation and temperature data were analyzed in order to determine climate shifts, and concomitant changes in vegetation health were assessed in the resultant epochs using vegetation indices. The results indicate that vegetation indices should only be used to assess trends in climate change under relatively pristine conditions, where human influence is limited. This knowledge is important for designing climate change monitoring strategies that are based on ecosystem phenology and vegetation health.     

Landsat5 TM遥感影像上太湖蓝藻水华反射光谱特征研究

利用ENVI遥感软件的FLAASH工具对2005年10月17日大规模蓝藻水华暴发的太湖Landsat5 TM影像进行大气校正处理,反演获得蓝藻水华和其他地物类型的遥感反射率图像,提取了不同集聚程度蓝藻水华的可见波段至近红外波段反射率数据,并与陆生植被、无蓝藻水面等地物的光谱反射率进行了比较。研究表明,与陆生植被、无蓝藻水面相比,蓝藻水华在TM2波段和TM4波段具有更高的反射率,在可见光波段整体反射率略高于陆生植被,在TM5、TM7波段则受水的影响反射率很低。从蓝藻水华、陆生植被的细胞生理结构、生长环境、复杂的遥感反射、散射及透射模型方面初步讨论了光谱差异的原因。     

环境卫星CCD与Landsat TM影像质量及生态监测应用比对研究——以青海湖区域为例

针对环境卫星 CCD 影像,结合影像质量评价、专题制图、土地利用/覆被解译以及常用植被指数构建,以青海湖区域为例,与Landsat TM影像进行比对研究。结果表明,在与TM影像质量评价参数比较中,TM影像较优,而环境卫星影像具有很大的改善空间;在对地物判译中,环境卫星影像色彩稍暗淡,但对大多数地物解译判读的边界更清晰;环境卫星覆盖度大,区域制图的优势非常明显;生态监测定量遥感常用的植被指数比较中两种数据大致相同。     

Estimating and mapping forest biomass using regression models and Spot-6 images (case study: Hyrcanian forests of north of Iran)

Hyrcanian forests of North of Iran are of great importance in terms of various economic and environmental aspects. In this study, Spot-6 satellite images and regression models were applied to estimate above-ground biomass in these forests. This research was carried out in six compartments in three climatic (semi-arid to humid) types and two altitude classes. In the first step, ground sampling methods at the compartment level were used to estimate aboveground biomass (Mg/ha). Then, by reviewing the results of other studies, the most appropriate vegetation indices were selected. In this study, three indices of NDVI, RVI, and TVI were calculated. We investigated the relationship between the vegetation indices and aboveground biomass measured at sample-plot level. Based on the results, the relationship between aboveground biomass values and vegetation indices was a linear regression with the highest level of significance for NDVI in all compartments. Since at the compartment level the correlation coefficient between NDVI and aboveground biomass was the highest, NDVI was used for mapping aboveground biomass. According to the results of this study, biomass values were highly different in various climatic and altitudinal classes with the highest biomass value observed in humid climate and high-altitude class.     

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.     

Using Landsat image time series to study a small water body in Northern Spain

Ramsar Convention and EU Water Framework Directive are two international agreements focused on the conservation and achievement of good ecological and chemical status of wetlands. Wetlands are important ecosystems holding many plant and animal communities. Their environmental status can be characterised by the quality of their water bodies. Water quality can be assessed from biophysical parameters (such as Chlorophyll-a concentration ([Chla]), water surface temperature and transparency) in the deeper or lacustrine zone, or from bioindicators (as submerged aquatic vegetation) in the shallow or palustrine zone. This paper proves the use of Landsat time series to measure the evolution of water quality parameters and the environmental dynamics of a small water body (6.57 ha) in a Ramsar wetland (Arreo Lake in the North of Spain). Our results show that Landsat TM images can be used to describe periodic behaviours such as the water surface temperature or the phenologic state of the submerged vegetation (through normalized difference vegetation index, NDVI) and thus detect anomalous events. We also show how [Chla] and transparency can be measured in the lacustrine zone using Landsat TM images and an algorithm adjusted for mesotrophic Spanish lakes, and the resulting values vary in time in accordance with field measurements (although these were not synchronous with the images). The availability of this algorithm also highlights anomalies in the field data series that are found to be related with the concentration of suspended matter. All this potential of Landsat imagery to monitor small water bodies in wetlands can be used for hindcasting of past evolution of these wetlands (dating back to 1970s) and will be also useful in the future thanks to the Landsat continuity mission and the Operational Land Imager.     

An integrated approach for spatio-temporal variability analysis of wetlands: a case study of Abaya and Chamo lakes, Ethiopia

Starting with the intensification of irrigation activities in the beginning of 1980s in Abaya and Chamo lakes area, the decreasing water inflow to the lakes caused denudation of the wetlands. The ecological situation in the lake region changed significantly during last four decades. The lakes and associated wetlands change have been studied using Landsat MSS (1973), Landsat TM (1986), and Ladsat ETM (2000) satellite imagery. Along with satellite imagery, other hydro-meteorological data were collected and hydro-meteorological data analyses were done to assess the variability of wetlands. From these data, lakes morphometric property estimation at different time series and water balance analysis for both lakes were done. Wetlands are mapped from the TCT image and these maps are subject to change detection to see the temporal and spatial variability of the wetlands. Moreover, the lake-morphometric area and volume variation have been studied. The result showed that between 1986 and 2000, a significant reduction has been observed but lesser than the previous decades (6.4 km(2)). The identified reason behind this change is that the free settlement and shoreline cultivation of the wetlands causing the soil erosion and eventually adds the sediment to the wetlands.     

A Special Vegetation Index for the Weed Detection in Sensor Based Precision Agriculture

Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture. The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF).     

Climate change vulnerability in a tropical region based on environmental and socio-economic factors

The understanding of the regional and local dimensions of vulnerability due to climate change is essential to develop appropriate and targeted adaptation efforts. We assessed the local dimensions of vulnerability in the tropical state of Kerala, India, using a purposely developed vulnerability index, which accounts for both environmental and socio-economic factors. The large extents of coastal wetlands and lagoons and high concentration of mangrove forests make the state environmentally vulnerable. Low human development index, large population of socially deprived groups, which are dependent on the primary sector, and high population density make the state vulnerable from a socio-economic point of view. The present study investigates climate change vulnerability at the district level in the State of Kerala relying on a purposely developed composite vulnerability index that encompasses both socio-economic and environmental factors. The Kerala coast contains the socio-economically and ecologically most vulnerable regions, as demonstrated by a composite vulnerability index.     

Montane meadows in the Sierra Nevada: comparing terrestrial and aquatic assessment methods

We surveyed montane meadows in the northern Sierra Nevada and southern Cascades for two field seasons to compare commonly used aquatic and terrestrial-based assessments of meadow condition. We surveyed (1) fish, (2) reptiles, (3) amphibians, (4) aquatic macroinvertebrates, (5) stream geomorphology, (6) physical habitat, and (7) terrestrial vegetation in 79 meadows between the elevations of 1,000 and 3,000?m. From the results of those surveys, we calculated five multi-metric indices based on methods commonly used by researchers and land management agencies. The five indices consisted of (1) fish only, (2) native fish and amphibians, (3) macroinvertebrates, (4) physical habitat, and (5) vegetation. We compared the results of the five indices and found that there were significant differences in the outcomes of the five indices. We found positive correlations between the vegetation index and the physical habitat index, the invertebrate index and the physical habitat index, and the two fish-based indices, but there were significant differences between indices in both range and means. We concluded that the five indices provided very different interpretations of the condition in a given meadow. While our assessment of meadow condition changed based on which index was used, each provided an assessment of different components important to the overall condition of a meadow system. Utilizing a multimetric approach that accounts for both terrestrial and aquatic habitats provides the best means to accurately assess meadow condition, particularly given the disproportionate importance of these systems in the Sierra Nevada landscape.     

profiling areas of ground water contamination by pesticides in california: phase ii – evaluation and modification of a statistical model

A well sampling study was conducted to evaluate anempirical approach to classifying areasof land in California as vulnerable to ground watercontamination by pesticides (Troiano et al., 1994). Wells were sampled from sections of land that had noprevious detections of pesticideresidues. The sections had been classified into vulnerablesoil clusters or into a not-classified groupusing a procedure based on Principal Components Analysis(PCA). Grape, citrus, and olive growingareas of Fresno and Tulare Counties were targeted, areas wherepre-emergence herbicide residues hadbeen detected in well water. Overall, herbicide residues weredetected in 75 of 176 sampled wells, ahigh frequency of detection in relation to results fromprevious targeted well sampling studies. Sinceresidues were also detected in the not-classified group, theclassification procedure was modified usingan approach based on Canonical Variates Analysis (CVA). Moresections were classified intovulnerable soil clusters with the CVA approach than with thePCA method. Data from two otherexplanatory variables, depth to ground water and amount ofpesticide used per section, were includedto illustrate how additional information can be incorporatedinto this approach of identifying vulnerable areas.     

Pattern of NDVI-based vegetation greening along an altitudinal gradient in the eastern Himalayas and its response to global warming

The eastern Himalayas, especially the Yarlung Zangbo Grand Canyon Nature Reserve (YNR), is a global hotspot of biodiversity because of a wide variety of climatic conditions and elevations ranging from 500 to > 7000 m above sea level (a.s.l.). The mountain ecosystems at different elevations are vulnerable to climate change; however, there has been little research into the patterns of vegetation greening and their response to global warming. The objective of this paper is to examine the pattern of vegetation greening in different altitudinal zones in the YNR and its relationship with vegetation types and climatic factors. Specifically, the inter-annual change of the normalized difference vegetation index (NDVI) and its variation along altitudinal gradient between 1999 and 2013 was investigated using SPOT-VGT NDVI data and ASTER global digital elevation model (GDEM) data. We found that annual NDVI increased by 17.58 % in the YNR from 1999 to 2013, especially in regions dominated by broad-leaved and coniferous forests at lower elevations. The vegetation greening rate decreased significantly as elevation increased, with a threshold elevation of approximately 3000 m. Rising temperature played a dominant role in driving the increase in NDVI, while precipitation has no statistical relationship with changes in NDVI in this region. This study provides useful information to develop an integrated management and conservation plan for climate change adaptation and promote biodiversity conservation in the YNR.