Mapping of boreal vegetation of a temperate mountain in China by multitemporal Landsat TM imagery

The Changbai Mountain Natural Reserve (2000 km 2 ), north-east China, is a very important ecosystem representing the temperate biosphere. The cover types were derived by using multitemporal Landsat TM imagery, which was modified with DEM data on the relationship between vegetation distribution and elevation. It was classified into 20 groups by supervised classification. By comparing the results of the classification of different band combinations, bands 4 and 5 of an image from 18 July 1997 and band 3 of an image from 22 October 1997 were used to make a false colour image for the final output, a vegetation map, which showed the best in terms of classification accuracy. The overall accuracy by individual images was less than 70%, while that of the multitemporal classification was higher than 80%. Further, on the basis of the relationship of vegetation distribution and elevation, the accuracy of multitemporal classification was raised from 85.8 to 89.5% by using DEM. Bands 4 and 5 showed a high ability for discriminating cover types. Images acquired in late spring and mid-summer were recognized better than other seasons for cover type identification. NDVI and band ratio of B4/B3 proved useful for cover type discrimination, but were not superior to the original spectral bands. Other band ratios like B5/B4 and B7/B5 were less important for improving classification accuracy. The changes of spectral reflectance and NDVI with season were also analysed with 10 images ranging from 1984 to 1997. Seperability of images in terms of classification accuracy was high in late spring and summer, and decreased towards winter. There were five vegetation zones on the mountain, from the base to the peak: deciduous forest zone, mixed forest zone, conifer forest zone, birch forest zone and tundra zone. Spruce-fir conifer dominated forest was the most dominant vegetation (33%), followed by mixed forest (26%), Korean pine forest (8%) and mountain birch forest (5%).     

Detection of forest harvest type using multiple dates of Landsat TM imagery

A simple and relatively accurate technique for classifying time-series Landsat Thematic Mapper (TM) imagery to detect levels of forest harvest is the topic of this research. The accuracy of multidate classification of the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI) were compared and the effect of the number of years (1–3, 3–4, 5–6 years) between image acquisition on forest change accuracy was evaluated. When Landsat image acquisitions were only 1–3 years apart, forest clearcuts were detected with producer's accuracy ranging from 79% to 96% using the RGB-NDMI classification method. Partial harvests were detected with lower producer's accuracy (55–80%) accuracy. The accuracy of both clearcut and partial harvests decreased as time between image acquisition increased. In all classification trials, the RGB-NDMI method produced significantly higher accuracies, compared to the RGB-NDVI. These results are interesting because the less common NDMI (using the reflected middle infrared band) outperformed the more popular NDVI. In northern Maine, industrial forest landowners have shifted from clearcutting to partial harvest systems in recent years. The RGB-NDMI change detection classification applied to Landsat TM imagery collected every 2–3 years appears to be a promising technique for monitoring forest harvesting and other disturbances that do not remove the entire overstory canopy.     

Comparison of two types of forest disturbance using multitemporal Landsat TM/ETM+ imagery and field vegetation data

Forest disturbances influence many landscape processes, including changes in microclimate, hydrology, and soil erosion. We analyzed the spectral response and temporal progress of two types of disturbances of spruce forest (bark beetle outbreak and clear-cuts) in the central part of Šumava Mountains at the border between the Czech Republic and Germany, Central Europe. The bark beetle (Ips typographus [L.]) outbreak in this region in the last 20 years resulted in regional-scale spruce forest decay. Clear-cutting was done here to prevent further bark-beetle propagation in the buffer zones.The aim of the study is to identify the differences in spectral response between the two types of forest disturbances and their temporal dynamics. General trends were analyzed throughout the study area, with sampled disturbance areas selected to assess the relationship between field vegetation data and their spectral response. Thirteen Landsat TM/ETM+ scenes from 1985 to 2007 were used for the assessment. The following spectral indices were estimated: NDMI, Tasseled Cap (Brightness, Greenness, Wetness), DI, and DI′. The DI′, Wetness, and Brightness indices show the highest sensitivity to forest disturbance for both disturbance types (clear-cuts and bark beetle outbreak). The multitemporal analysis distinguished three different stages of development. The highest spectral differences between the clear-cuts and the bark beetle disturbances were found in the period between 1996 and 2004 with increased levels of forest disturbance (repeated measures ANOVA, Scheffé post hoc test; p ≤ 0.05). Clear-cut disturbance resulted in significantly higher spectral differences from the original forest and occurred as a more discrete event in comparison to bark beetle outbreak.     

Mapping leaf area index over a mixed natural forest area in the flooding season using ground-based measurements and Landsat TM imagery

Leaf area index (LAI) is an important structural parameter in terrestrial ecosystem modelling and management. Therefore, it is necessary to conduct an investigation on using moderate-resolution satellite imagery to estimate and map LAI in mixed natural forests in southeastern USA. In this study, along with ground-measured LAI and Landsat TM imagery, the potential of Landsat 5 TM data for estimating LAI in a mixed natural forest ecosystem in southeastern USA was investigated and a modelling method for mapping LAI in a flooding season was developed. To do so, first, 70 ground-based LAI measurements were collected on 8 April 2008 and again on 1 August 2008 and 30 July 2009; TM data were calibrated to ground surface reflectance. Then univariate correlation and multivariate regression analyses were conducted between the LAI measurement and 13 spectral variables, including seven spectral vegetation indices (VIs) and six single TM bands. Finally, April 08 and August 08 LAI maps were made by using TM image data, a multivariate regression model and relationships between April 08 and August 08 LAI measurements. The experimental results indicate that Landsat TM imagery could be used for mapping LAI in a mixed natural forest ecosystem in southeastern USA. Furthermore, TM4 and TM3 single bands (R ² > 0.45) and the soil adjusted vegetation index, transformed soil adjusted vegetation index and non-linear vegetation index (R ² > 0.64) have produced the highest and second highest correlation with ground-measured LAI. A better modelling result (R ²?=?0.78, accuracy?=?73%, root mean square error (RMSE)?=?0.66) of the 10-predictor multiple regression model was obtained for estimating and mapping April 08 LAI from TM data. With a linear model and a power model, August 08 LAI maps were successfully produced from the April 08 LAI map (accuracy?=?79%, RMSE?=?0.57), although only 58–65% of total variance could be accounted for by the linear and non-linear models.     

Classification of Amazonian primary rain forest vegetation using Landsat ETM+ satellite imagery

A lack of spatially and thematically accurate vegetation maps complicates conservation and management planning, as well as ecological research, in tropical rain forests. Remote sensing has considerable potential to provide such maps, but classification accuracy within primary rain forests has generally been inadequate for practical applications. Here we test how accurately floristically defined forest types in lowland tropical rain forests in Peruvian Amazonia can be recognized using remote sensing data (Landsat ETM+ satellite image and STRM elevation model). Floristic data and a vegetation classification with four forest classes were available for eight line transects, each 8 km long, located in an area of ca 800 km². We compared two sampling unit sizes (line transect subunits of 200 and 500 m) and several image feature combinations to analyze their suitability for image classification. Mantel tests were used to quantify how well the patterns in elevation and in the digital numbers of the satellite image correlated with the floristic patterns observed in the field. Most Mantel correlations were positive and highly significant. Linear discriminant analysis was used first to build a function that discriminates between forest classes in the eight field-verified transects on the basis of remotely sensed data, and then to classify those parts of the line transects and the satellite image that had not been visited in the field. Classification accuracy was quantified by 8-fold crossvalidation. Two of the tierra firme (non-inundated) forest types were combined because they were too often misclassified. The remaining three forest types (inundated forest, terrace forest and Pebas formation/intermediate tierra firme forest) could be separated using the 500-m sampling units with an overall classification accuracy of 85% and a Kappa coefficient of 0.62. For the 200-m sampling units, the classification accuracy was clearly lower (71%, Kappa 0.35). The forest classification will be used as habitat data to study wildlife habitat use in the same area. Our results show that remotely sensed data and relatively simple classification methods can be used to produce reasonably accurate forest type classifications, even in structurally homogeneous primary rain forests.     

Mapping inland lake water quality across the Lower Peninsula of Michigan using Landsat TM imagery

The number, size, and distribution of inland freshwater lakes present a challenge for traditional water-quality assessment due to the time, cost, and logistical constraints of field sampling and laboratory analyses. To overcome this challenge, Landsat imagery has been used as an effective tool to assess basic water-quality indicators, such as Secchi depth (SD), over a large region or to map more advanced lake attributes, such as cyanobacteria, for a single waterbody. The overarching objective of this research application was to evaluate Landsat Thematic Mapper (TM) for mapping nine water-quality metrics over a large region and to identify hot spots of potential risk. The second objective was to evaluate the addition of landscape pattern metrics to test potential improvements in mapping lake attributes and to understand drivers of lake water quality in this region. Field-level in situ water-quality measurements were collected across diverse lakes (n = 42) within the Lower Peninsula of Michigan. A multicriteria statistical approach was executed to map lake water quality that considered variable importance, model complexity, and uncertainty. Overall, band ratio radiance models performed well (R² = 0.65–0.81) for mapping SD, chlorophyll-a, green biovolume, total phosphorus (TP), and total nitrogen (TN) with weaker (R² = 0.37) ability to map total suspended solids (TSS) and cyanobacteria levels. In this application, Landsat TM and pattern metrics showed poor ability to accurately map non-purgable organic carbon (NPOC) and diatom biovolume, likely due to a combination of gaps in temporal overpass and field sampling and lack of signal sensitivity within broad spectral channels of Landsat TM. The composition and configuration of croplands, urban, and wetland patches across the landscape were found to be moderate predictors of lake water quality that can complement lake remote-sensing data. Of the 4071 lakes, over 4 ha in the Lower Peninsula, approximately two-thirds, were identified as mesotrophic (n = 2715). This application highlights how an operational tool might support lake decision-making or assessment protocols to identify hot spots of potential risk.     

Characterizing landscape changes in central Rondonia using Landsat TM imagery

An analysis of landscape changes in a region of pioneer settlements in central Rondonia, western Brazilian Amazon, was derived from Landsat TM data. Total deforested area increased from 206 x 103 ha in 1977, to 565 x 103 ha in 1985 and to 1210 x 103 ha, or 35.5% of the region, in 1995. Eighty-one per cent of the total 1995 deforestation had occurred in regions within 12.5km from areas of pioneer colonization deforested by 1977. Deforested area exceeded 79% in regions within 12.5km from the region's first road.     

Estimation of tropical forest biomass using Landsat TM imagery and permanent plot data in Xishuangbanna,China

Because of its complexity, it is very difficult to obtain information about distribution of biomass in tropical forests. This article describes the estimation of tropical forest biomass by using Landsat TM and forest plot data in Xishuangbanna, PR China. The method includes several steps. First, the biomass for each forest permanent plot is calculated by using field inventory data. Second, Landsat TM images are geometrically corrected by using topographic maps. Third, a map of the tropical forest is obtained by using data from a variety of sources such as Landsat TM, digital elevation model (DEM), temperature and precipitation layers and expert knowledge. Finally, the biomass and carbon storage of each forest vegetation type in the forest map is calculated by using the tropical forest map and the forest plot biomass GIS database. In the study area, forest area accounts for 57% of the total 1.7?×?10⁶ hectares. The total forest biomass is 2.0?×?10⁸ tonne. It is shown that the forest vegetation map, the forest biomass and the forest carbon storage can be obtained by effectively integrating Landsat TM, ancillary data including DEM, temperature and precipitation, forest permanent plots and knowledge using the method proposed here.     

Mapping fire scars in a southern African savannah using Landsat imagery

The spectral, spatial and temporal characteristics of the Landsat data record make it appropriate for mapping fire scars. Twenty-two annual fire scar maps from 1972–2002 were produced from historical Landsat imagery for a semi-arid savannah landscape on the South Africa–Botswana border, centred over Madikwe Game Reserve (MGR) in South Africa. A principal components transformation (PCT) helped differentiate the spectral signal of fire scars in each image. A simple, nonparametric, supervised classification (parallelepiped) of the PCT data differentiated burned and unburned areas. During most years, fire occurrences and the percentage of area burned annually were lowest in Botswana, highest in MGR, and intermediate in South Africa outside MGR. These fire scar maps are aiding MGR managers, who are endeavouring to restore a more active fire regime following decades of fire exclusion.     

Mapping fallow lands in Vietnam's north-central mountains using yearly Landsat imagery and a land-cover succession model

The objective of this article is to investigate whether it is possible to use Landsat data together with ancillary data and temporal context to accurately identify land covers found in the fallow areas of Montane Mainland Southeast Asia's (MMSEA's) difficult-to-map swidden landscapes. A rule-based non-parametric hybrid classification method that integrates knowledge about the vegetation regrowth patterns in these landscapes with analysis of Landsat imagery is developed. The method is applied to three upland districts of the Nghe An Province, Vietnam. The results show that the hybrid classification approach, with an overall accuracy of 90%, is superior to using a traditional maximum likelihood classifier, which generated an overall accuracy of 68%. The hybrid classification results indicate that the landscape is dominated by bush and bamboo, while the maximum likelihood classification suggests a landscape that is predominantly grass covered. The hybrid classification results are in agreement with local knowledge and information from fieldwork-based reports and articles on swidden systems in the study area and other parts of MMSEA.     

Mapping crop cover using multi-temporal Landsat 8 OLI imagery

Crop classification maps are useful for estimating amounts of crops harvested, which could help address challenges in food security. Remote-sensing techniques are useful tools for generating crop maps. Optical remote sensing is one of the most attractive options because it offers vegetation indices (VIs) with frequent revisits and has adequate spatial and spectral resolution and some data has been distributed free of charge. However, sufficient consideration has not been given to the potential of VIs calculated from Landsat 8 Operational Land Imager (OLI) data. This article describes the use of Landsat 8 OLI data for the classification of crops in Hokkaido, Japan. In addition to reflectance, VIs calculated from simple formulas that consisted of combinations of two or more reflectance wavebands were evaluated, as well as the six components of the Kauth–Thomas transform. The VIs based on shortwave infrared bands (bands 6 or 7) improved classification accuracy, and using a combination of all derived data from Landsat 8 OLI data resulted in an overall accuracy of 94.5% (allocation disagreement = 4.492 and quantity disagreement = 1.017).     

Effect of thinning cutting on boreal forest reflectance: a comparison of simulations and Landsat TM estimates

A forest reflectance model was used to simulate the reflectance changes due to thinning in some stands of boreal forest in Sweden. The simulated reflectance changes were compared with empirical reflectance changes, measured by multi-temporal analysis of Landsat TM data. The stand data from a field survey served as the main inputs for the reflectance model. The mean absolute difference between stimulated and measured reflectance change was about one third of the average measured change.     

Mapping montane tropical forest successional stage and land use with multi-date Landsat imagery

When mapping land cover with satellite imagery in montane tropical regions, varying illumination angles and ecological zones can obscure the differences between spectral responses of old-growth forest, secondary forest and agricultural lands. We used multi-date, Landsat Thematic Mapper (TM) imagery to map secondary forests, agricultural lands and old-growth forests in the Talamanca Mountain Range in southern Costa Rica. With stratification by illumination and ecological zone, the overall accuracy for this classification was 87% with a Kappa coefficient of 0.83. We also examined spectral responses to forest successional stage, ecological zone and aspect illumination for the TM Tasselled Cap indices, TM (2 x 6)/7, TM 4/5 and TM difference bands, and whether using digital data from multiple decades improved classification accuracy. Digital maps of ecological zones should be useful for large-scale mapping of land use and forest successional stage in complex montane regions such as those in Central America.     

Impact of understory vegetation on forest canopy reflectance and remotely sensed LAI estimates

Forest leaf area index (LAI), is an important variable in carbon balance models. However, understory vegetation is a recognized problem that limits the accuracy of satellite-estimated forest LAI. A canopy reflectance model was used to investigate the impact of the understory vegetation on LAI estimated from reflectance values estimated from satellite sensor data. Reflectance spectra were produced by the model using detailed field data as input, i.e. forest LAI, tree structural parameters, and the composition, distribution and reflectance of the forest floor. Common deciduous and coniferous forest types in southern Sweden were investigated. A negative linear relationship (r² = 0.6) was observed between field estimated LAI and the degree of understory vegetation, and the results indicated better agreement when coniferous and deciduous stands were analysed separately. The simulated spectra verified that the impact of the understory on the reflected signal from the top of the canopy is important; the reflectance values varying by up to ± 18% in the red and up to ± 10% in the near infra-red region of the spectra due to the understory. In order to predict the variation in LAI due to the understory vegetation, model inversions were performed where the input spectra were changed between the minimum, average and maximum reflectance values obtained from the forward runs. The resulting variation in LAI was found to be 1.6 units on average. The LAI of the understory could be predicted indirectly from simple stand data on forest characteristics, i.e. from allometric estimates, as an initial step in the process of estimating LAI. It is suggested here that compensation for the effect of the understory would improve the accuracy in the estimates of canopy LAI considerably.     

Estimating forest canopy density of tropical mixed deciduous vegetation using Landsat data: a comparison of three classification approaches

Although a number of image classification approaches are available to estimate forest canopy density (FCD) using satellite data, assessment of their relative performances with tropical mixed deciduous vegetation is lacking. This study compared three image classification approaches – maximum likelihood classification (MLC), multiple linear regression (MLR) and FCD Mapper – in estimating the FCD of mixed deciduous forest in Myanmar. The application of MLC and MLR was based on spectral reflectance of vegetation, whereas FCD Mapper was operated on integrating the biophysical indices derived from the reflectance of the vegetation. The FCD was classified into four categories: closed canopy forest (CCF; FCD ≥ 70%), medium canopy forest (MCF; 40% ≥ FCD < 70%), open canopy forest (OCF; 10% ≥ FCD < 40%) and non-forest (NF; FCD < 10%). In the three classification approaches, producer's and user's accuracies were higher for more homogeneous vegetation such as NF and CCF than for heterogeneous vegetation density (VD) such as OCF and MCF. FCD Mapper produced the best overall accuracy and kappa coefficient. This study revealed that only spectral reflectance is not enough to get good results in estimating FCD in tropical mixed deciduous vegetation. This study indicates that FCD Mapper, an inexpensive approach because it requires only validation data and thus saves time, can be applied to monitor tropical mixed deciduous vegetation over time at lower cost than alternative methods.     

Nondestructive estimation of canopy chlorophyll content using Hyperion and Landsat/TM images

Remote sensing offers a nondestructive tool for the quick and precise estimation of canopy chlorophyll content that serves as an important indicator of the plant ecosystem. In this study, the canopy chlorophyll content of 26 samples in 2007 and 40 samples in 2008 of maize were nondestructively estimated by a set of vegetation indices (VIs; Normalized Difference Vegetation Index, NDVI; Green Chlorophyll Index, CI_green; modified soil adjust vegetation index, MSAVI; and Enhanced Vegetation Index, EVI) derived from the hyperspectral Hyperion and Thematic Mapper (TM) images. The PROSPECT model was used for sensitivity analysis among the indices and results indicated that CI_green had a large linear correlation with chlorophyll content ranging from 100–1000 mg m^?2. EVI showed a moderate ability in avoiding saturation and reached a saturation of chlorophyll content above 600 mg m^?2. Both of the other two indices, MSAVI and NDVI, showed a clear saturation at chlorophyll content of 400 mg m^?2, which demonstrated they may be inappropriate for chlorophyll interpretation at high values. A validation study was also conducted with satellite observations (Hyperion and TM) and in-situ measurements of chlorophyll content in maize. Results indicated that canopy chlorophyll content can be remotely evaluated by VIs with r ² ranging from the lowest of 0.73 for NDVI to the highest of 0.86 for CI_green. EVI had a greater precision (r ²=0.81) than MASVI (r ²=0.75) in canopy chlorophyll content estimation. The results agreed well with the sensitivity study and will be helpful in developing future models for canopy chlorophyll evaluation.     

Estimation of timber volume in a coniferous plantation forest using Landsat TM

Optimisation of economic return from forests requires that comprehensive forest inventory data be available to support the design of harvesting strategies. Such inventory data can potentially be obtained by remote sensing. This study investigates the accuracy with which wood volume (m3 ha -1) in a plantation forest can be calculated from Landsat TM data at the pixel and foreststand spatial scales. Wood volumes were estimated from regression analysis, nonparametric line-fitting, and an N-dimensional K-nearest-neighbour classification scheme. At the pixel scale, relations between Landsat data and measured wood volume were found to be significant but weak, with r2 values of 0.3, and with correspondingly poor estimates of wood volume (root-mean-square errors rmses of 100 m3 ha -1). By averaging the pixel-scale estimates, wood volume estimates of acceptable accuracy were obtained for forest-stand areas of about 40 ha (rmses of 46 m3 ha -1). Parametric regression performed slightly better overall than non-parametric line fitting techniques for estimating wood volume. Estimates of similar accuracy to those obtained by regression were also given by NK-classification at the pixel-scale, provided K was large ( 15), although the classifier produced biased results at the forest-stand scale. It is concluded that Landsat TM only provides an acceptable data source for estimating wood volumes in plantation forests for areas of about 40 ha and larger. The very low dynamic range in the Landsat data is probably a significant factor limiting its use for inventory at more detailed scales.     

Comparison of automatic thresholding methods for snow-cover mapping using Landsat TM imagery

Knowledge of snow cover is essential to understanding the global water and energy cycle. Thresholding the normalized difference snow index (NDSI) image is a method frequently used to map snow cover from remotely sensed data. However, the threshold is dependent on the scenario and needs to be determined accordingly. In this study, nine automatic thresholding methods were tested on the NDSI. Comparisons of the automatic thresholding methods, optimal threshold, and support vector machine (SVM) classification show that Otsu's and Nie's methods appear to be the most robust among the nine automatic thresholding methods, achieving comparable accuracies with the latter two approaches. In addition, NDSI from the digital number (DN) can be an efficient substitution for NDSI obtained from atmospherically or topographically corrected data, with similar accuracy.     

Mapping land cover change in a reindeer herding area of the Russian Arctic using Landsat TM and ETM+ imagery and indigenous knowledge

Much of Russia north of the treeline is grazed by reindeer, and this grazing has materially altered the vegetation cover in many places. Monitoring vegetation change in these remote but ecologically sensitive regions is an important task for which satellite remote sensing is well suited. Further difficulties are imposed by the highly dynamic nature of arctic phenology, and by the difficulty of obtaining accurate official data on land cover in arctic Russia even where such data exist. We have approached the problem in a novel fashion by combining a conventional multispectral analysis of satellite imagery with data on current and historical land use gathered by the techniques of social anthropology, using a study site in the Nenets Autonomous Okrug (NAO). A Landsat-7 ETM+ image from the year 2000 was used to generate a current land cover classification. A Landsat-5 TM image was used to generate a land-cover classification for 1988, taking due account of phenological differences and between the two dates. A cautious comparison of these two classifications, again taking account of possible effects of phenological differences, shows that much of the study area has already undergone a notable transformation to grass-dominated tundra, almost certainly as a result of heavy grazing by reindeer. The grazing pattern is quite heterogeneous, and may have reached unsustainable levels in some areas. Finally, we suggest that this situation is unlikely to be unique to our study area and may well be widespread throughout the Eurasian tundra zone, particularly in the west.     

A preliminary assessment of Landsat TM imagery for mapping vegetation and sediment distribution in the Wash estuary

Abstract

Analysis of Landsat Thematic Mapper (TM) image of 14 May 1984 has shown that such data can be used to survey vegetation and sediment distributions in the intertidal zone of the Wash estuary at a spatial detail comparable with current methods practised by the.Nature Conservancy Council. Multispectral classification of this TM image showed good separation of salt-marsh vegetation communities which had recently been surveyed by the Nature Conservancy Council and for which reliable training data could be taken. The sensitivity of classification performance, using both parametric and non-parametric algorithms, to apparently minor differences in phenology at training site locations demonstrates two requirements for improved salt-marsh classification. They are the need for strict definition of training data and that TM wave bands 2, 3, 4 and 5 provide suitable spectral vectors for classifying intertidal environments.