Classify Vegetation Types Research Articles

Satellite Imagery Analysis and Plane Detection

Monitoring natural vegetation and detecting planes in satellite imagery are critical tasks in remote sensing, with applications ranging from environmental conservation to security and surveillance. This project aims to develop an effective method for analyzing natural vegetation and detecting planes by utilizing satellite imagery and applying advanced image processing and machine learning techniques. By leveraging various features such as vegetation indices, texture, shape, and spectral signatures, we aim to build predictive models that accurately classify vegetation types and identify planes. This paper discusses the methodology, including data collection, preprocessing, feature extraction, model training, and evaluation, ultimately highlighting the models' performance and potential applications in real-world scenarios. Keywords— Vegetation Analysis,Deep learning ,Plane Detection.

Do different camera trap lures result in different detection rates of vertebrates because of their attractiveness to invertebrates?

SummaryThe type of attractant used in camera trap lures is recognised as an important methodological decision. We investigated whether the type of attractant in lures indirectly influences detectability of wildlife on cameras via differential attraction of invertebrates which themselves constitute prey of insectivorous animals. We indexed invertebrate abundance using pitfall and sticky‐traps at 36 camera stations deployed in a Latin Squares design for 5 days, with three lure options (peanut‐butter, tuna oil and control) in a coastal mosaic, at Cape Otway, Victoria, Australia. We classified vegetation types (habitat) as: low (no or sparse), medium (grass), and high (shrubs) from aerial imagery and estimated their percentage cover at each grid point (medium and high were uncorrelated and included as separate variables in models). We first examine if attractant types or the habitat influence invertebrate abundance and assemblage, and then examine whether invertebrate abundance influences vertebrate detectability on cameras. There was a trend for the composition of terrestrial invertebrate assemblages to be influenced by lure type (peanut‐butter, tuna oil and control attractants) and the proportional cover of medium height vegetation within 20 m; however, assemblage composition was clearly influenced by the proportional area of high vegetation cover within 20 m. The detection probability of insectivorous birds increased where medium‐sized (2.5–5 mm) flying invertebrates were present whereas insectivorous mammal detectability increased with terrestrial invertebrate species richness. Mammal detections are more likely associated with use of habitats that have more diverse invertebrate communities. This study provides some support to the hypothesis of the indirect mechanism whereby bird detections are influenced by invertebrate attraction to lures. Therefore, lure choice for camera traps is critical and the possibility of guild‐level biases in detection suggests that cautious interpretation of results is required.

Enhancing vegetation formation classification: Integrating coarse-scale traditional mapping knowledge and advanced machine learning

Mapping vegetation formation types in large areas is crucial for ecological and environmental studies. However, this is still challenging to distinguish similar vegetation formation types using existing predictive vegetation mapping methods, based on commonly used environmental variables and remote sensing spectral data, especially when there are not enough training samples. To solve this issue, we proposed a predictive vegetation mapping method by integrating an advanced machine learning algorithm and knowledge in an early coarse-scale vegetation map (VMK). First, we implemented classification using the random forest algorithm by integrating the early vegetation map as an auxiliary feature (VMF). Then, we determined the rationality of classified vegetation types and distinguished the confusing types, respectively, based on the knowledge of the spatial distributions and hierarchies of vegetation. Finally, we replaced each recognized unreasonable vegetation type with its corresponding reasonable vegetation type. We implemented the new method in upstream of the Yellow River based on GaoFen-1 satellite images and other environmental variables (i.e., topographical and climate variables). Results showed that the overall accuracy using the VMK method ranged from 67.7 % to 76.8 %, which was 10.9 % to 13.4 % and 3.2 % to 6.6 %, respectively, higher than that of the method without the early vegetation map (NVM) and the VMF method, based on cross-validation with 20 % to 60 % random training samples. The spatial details of the vegetation map using the VMK method were also more reasonable compared to the NVM and VMF methods. These results indicated that the VMK method can distinctly improve the mapping accuracy at the vegetation formation level by integrating knowledge of existing vegetation maps. The proposed method can largely reduce the requirements on the number of field samples, which is especially important for alpine mountains and arctic region, where collecting training samples is more difficult due to the harsh natural environment.

Vegetation type change in California’s Northern Bay Area: A comparison of contemporary and historical aerial imagery

The North Bay area of California is a populous and ecologically diverse area that has experienced significant changes in the past century, as well as a series of recent wildfires, after over a century of fire suppression practices. While much research has been conducted quantifying drivers and patterns of vegetation change in conifer-dominated ecosystems, and how such changes have influenced current trends in fire behavior, studies of similar focus and scale are rarer in non-conifer ecosystems, including mixed-hardwood forests or shrub-dominated ecosystems in central and coastal Northern California. This is despite the fact that ecosystems other than conifer forests make up the majority of area burned in California wildfires. As such, expanding research focused on patterns of large-scale vegetation change as a possible driver of this trend in this area is a priority. In this study, we sought to map the overall extent and patch sizes of broad vegetation classes across a 52,000 ha study area based on historical (1948) and contemporary (2014) aerial imagery and to investigate shifts in vegetation patterns, as well as potential pathways and drivers of detected changes. We classified vegetation types through segmenting our imagery into homogenous polygons, and assigning broad vegetation categories using a random forest algorithm. We then analyzed patterns of change using spatial statistics and conditional inference tree analyses. We detected a large increase (12%) in the relative landscape proportion and average patch size of the forest class, characterized by dense tree canopy cover. Woodlands and shrub patches were most susceptible to type change, with the majority (57% and 65%, respectively) of converted areas subsequently identified as denser forest stands. By contrast, herbaceous and forest patches were most persistent. We additionally found that disturbance history, specifically whether an area burned or not, and topographic variables, including elevation and aspect, were important influences on the likelihood of vegetation persistence, while slope and water availability were not. Historical aerial imagery, which provides fine resolution and accurate data over a large spatial scale, is a useful tool for detecting landscape-scale vegetation shifts in ecosystems where widespread vegetation monitoring was not common historically. The marked increase in dense forest we detected, specifically due to the conversion of large areas of shrub and woodland vegetation may correspond to higher surface and ladder fuel load and continuity, and potentially higher wildfire risk. Fuel reduction treatments typically implemented in conifer-dominated forests may also be warranted in these mixed hardwood forests. However, more research is needed to understand drivers of change in non-conifer-dominated ecosystems in California and how such change influences wildfire behavior.

Classifying Vegetation Types in Mountainous Areas with Fused High Spatial Resolution Images: The Case of Huaguo Mountain, Jiangsu, China

This study tested image fusion quality aiming at vegetation classification in the Kongquegou scenic location on the southern slope of Huaguo Mountain in Lianyungang, Jiangsu Province, China. Four fusion algorithms were used to fuse WorldView-2 multispectral and panchromatic images: GS (Gram-Schmidt) transform, Ehlers, Wavelet transform, and Modified IHS. The fusion effect was evaluated through visual comparison, quantitative index analysis, and vegetation classification accuracy. The study result revealed that GS and Wavelet transformation produced higher spectral fidelity and better-quality fusion images, followed by Modified IHS and Ehlers. In terms of vegetation classification, for the Wavelet transform, both spectral information and adding spatial structure provided higher accuracy and displayed suitability for vegetation classification in the selected area. Meanwhile, although the spectral features obtained better classification accuracy using the Modified IHS, adding spatial structure to the classification process produced less improvement and a lower robustness effect. The GS transform yielded better spectral fidelity but relatively low vegetation classification accuracy using spectral features only and combined spectral features and spatial structure. Lastly, the Ehlers method’s vegetation classification results were similar to those of the GS transform image fusion method. Additionally, the accuracy was significantly improved in the fused images compared to the multispectral image. Overall, Wavelet transforms showed the best vegetation classification results in the study area among the four fusion algorithms.

Vegetation Type Classification Based on 3D Convolutional Neural Network Model: A Case Study of Baishuijiang National Nature Reserve

Efficient and accurate vegetation type extraction from remote sensing images can provide decision makers with basic forest cover and land use information, and provides a reliable basis for long-term monitoring. With the development of deep learning, the convolutional neural network (CNN) has been used successfully to classify tree species in many studies, but CNN models have rarely been applied in the classification of vegetation types on larger scales. To evaluate the performance of CNN models in the classification of vegetation types, this paper compared the classification accuracy of nine dominant land cover types in Baishuijiang National Nature Reserve with four models: 3D-CNN, 2D-CNN, JSSAN (joint spatial–spectral attention network) and Resnet18, using sentinel-2A data. Comparing the difference in classification accuracy between the direct use of raw sentinel images and fused feature indices sentinel images, the results showed that adding feature indices can improve the overall accuracy of the model. After fusing the characteristic bands, the accuracy of the four models was improved significantly, by 5.46–19.33%. The best performing 3D-CNN model achieved the highest classification accuracy with an overall accuracy of 95.82% and a kappa coefficient of 95.07%. In comparison, 2D-CNN achieved an overall accuracy of 79.07% and a kappa coefficient of 75.44%, JSSAN achieved an overall accuracy of 81.67% and a kappa coefficient of 78.56%, and Resnet18 achieved an overall accuracy of 93.61% and a kappa coefficient of 92.45%. The results showed that the 3D-CNN model can effectively capture vegetation type cover changes from broad-leaved forests at lower elevation, to shrublands and grasslands at higher elevation, across a range spanning 542–4007 m. In experiments using a small amount of sample data, 3D-CNN can better incorporate spatial–spectral information and is more effective in distinguishing the performance of spectrally similar vegetation types, providing an efficient and novel approach to classifying vegetation types in nature reserves with complex conditions.

POSSIBILITIES OF USING MULTI-ZONE SATELLITE IMAGES AND GIS TECHNOLOGIES FOR MONITORING PIPELINE SYSTEMS

In this paper, we consider the possibility of using multi-zone satellite images and automated processing of Earth remote sensing materials for solving problems of information support of the operation of pipeline systems in fire hazard conditions. The possibility of classifying vegetation types based on Sentinel-2A satellite images, taking into account data from selective ground inspection, as well as assessment of the forest-ecological situation along pipeline routes is demonstrated. The method of using multispectral satellite images for analyzing the forest fire situation was presented. On the example woodlands of the North Sakhalin by TERRA satellite images it was shown that vegetation cover can be monitored in forest areas where pipelines are laid. The proposed and tested approach can be used for analysis of conditions in forests with a developed pipeline network. The combinations of Landsat 8 (ETM+) and Sentinel-2 (MSI S2A) channels that best allow detecting and monitoring fires are shown, as well as example of implementing by GIS-based forest fire monitoring system.

Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.

Ecological Grouping of Survey Sites When Sampling Artefacts are Present

SummaryGrouping sites on the basis of their biological information is a common goal in ecology that has scientific and management applications. Two applications are studied in this work: classifying vegetation types for management units and predicting these units into unsampled space, and finding assemblages of fish and investigating how the presence of these assemblages varies with covariates. Data that are used to find the groupings often have extraneous sources of variation, such as those related to sampling, which are often ignored but should be accounted for when finding the groupings. In ecological studies, this is increasingly common as data sets are now being combined from many smaller survey efforts. We show, through a model-based clustering method, how the groupings can be obtained, while accounting for sampling artefacts.

Vegetation type classification and vegetation cover percentage estimation in urban green zone using pleiades imagery

Open green space in the urban area has aims to maintain the availability of land as a water catchment area, creating aspects of urban planning through a balance between the natural environment and the built environment that are useful for the public needs. Local governments have to make the green zone plan map and monitor the green space changes in their territory. Medium and high resolution satellite imageries have been widely utilized to map and monitor the changes of vegetation cover as an indicator of green space area. This paper describes the use of pleaides imagery to classify vegetation types and estimate vegetation cover percentage in the green zone. Vegetation cover was mapped using a combination of NDVI and blue band. Furthermore, vegetation types in the green space were classified using unsupervised and supervised (ISODATA and MLEN) methods. Vegetation types in the study area were divided into sparse vegetation, low-medium vegetation and medium-high vegetation. The classification accuracies were 97.9% and 98.9% for unsupervised and supervised method respectively. The vegetation cover percentage was determined by calculating the ratio between the vegetation type area and the green zone area. These information are useful to support green zone management activities.

Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types

The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol) synthetic aperture radar (PolSAR) data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co- and cross-pol double, and volume scattering). We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR) observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types.

Classification and Net Primary Productivity of the Southern China's Grasslands Ecosystem Based on Improved Comprehensive and Sequential Classification System (CSCS) Approach

This research classified vegetation types and evaluated net primary productivity (NPP) of southern China's grasslands based on the improved comprehensive and sequential classification system (CSCS), and proposed 5 thermal grades and 6 humidity grades. Four classes of grasslands vegetation were recognized by improved CSCS, namely, tundra grassland class, typical grassland class, mixed grassland class and alpine grassland class. At the type level, 14 types of vegetations (9 grasslands and 5 forests) were classified. The NPP had a trend to decrease from east to west and south to north, and the annual mean NPP was estimated to be 656.3 g C m−2 yr−1. The NPP value of alpine grassland class was relatively high, generally more than 1 200 g C m−2 yr−1. The NPP value of mixed grassland class was in a range from 1 000 to 1 200 g C m−2 yr−1. Tundra grassland class was located in southeastern Tibet with high elevation, and its NPP value was the lowest (<600 g C m−2 yr−1). The typical grassland class distributed in most of the area, and its NPP value was generally from 600 to 1 000 g C m−2 yr−1. The total NPP value in the study area was 68.46 Tg C. The NPP value of typical grassland class was the highest (48.44 Tg C), and mixed grassland class was the second (16.54 Tg C), followed by alpine grassland class (3.22 Tg C), with tundra grassland class being the lowest (0.25 Tg C). For all the grasslands types, the total NPP of forest meadow was the highest (34.81 Tg C), followed by sparse forest brush (16.54 Tg C), and montane meadow was the lowest (0.01 Tg C).

Forty Years of Change in the Bulrush Marshes of the St. Lawrence Estuary and The Impact of the Greater Snow Goose

During its spring and fall migrations, the Greater Snow Goose (Chen caerulescens atlanticus) stages in the marshes along the St. Lawrence Estuary in southern Quebec, where it feeds on three-square bulrush (Schoenoplectus americanus) rhizomes. The goose population has grown from 70 000 birds to around one million over the last 40 years, thus increasing pressure on these tidal marshes. To determine the impact of geese on the ecological integrity of the marshes over this period, we used IKONOS satellite imagery and aerial photographs to classify vegetation types. We estimated changes in bulrush cover using the eCognition image analysis software (Trimble). We examined the spectral, textural, and contextual characteristics of the identified classes. The proportion of bulrush cover has declined significantly in the lower marsh since around 1980, and bulrush has been gradually replaced by wild rice (Zizania aquatica var. brevis). We also documented the erosion between the lower and upper marshes along most of the shoreline.

Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data

River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous river floodplain. FLIGHT enables simulating top-of-canopy reflectance of vegetated surfaces either in turbid (e.g., grasslands) or in 3D (e.g., forests) mode. By inverting FLIGHT against CHRIS data, LAI was computed for three main classified vegetation types, ‘herbaceous’, ‘shrubs’ and ‘forest’, and for the CHRIS view zenith angles in nadir, backward (−36°) and forward (+36°) scatter direction. The −36° direction showed most LAI variability within the vegetation types and was best validated, closely followed by the nadir direction. The +36° direction led to poorest LAI retrievals. The class-based inversion process has been implemented into a GUI toolbox which would enable the river manager to generate LAI maps in a semiautomatic way.

Vegetation Mapping of Urban Forest Using Airborne Laser Scanning in Kyoto City, Japan: Towards Woody Biomass Utilization

The use of woody biomass from forest is becoming of interest in Japan, its amount and availability should be discussed in a spatial manner. Airborne laser scanning (ALS) technology enables three-dimensional information of objects on the ground to be obtained, and such data can provide valuable information for forest management that can contribute to biomass utilization planning. This study examines the use of such ALS data for classifying vegetation types in a forest in Kyoto city, Japan. Training sample plots were established based on five different vegetation type classes; five variables were then calculated from each plot’s ALS data, and the effectiveness of the variables was quantified. Of the five variables, the coefficient of variation (CV) and the only fraction (OF) were the most effective, and consequently were used in the classification analysis to generate a map of vegetation types across the study area. The accuracy assessment yielded a kappa coefficient of agreement of 0.79. This study demonstrates that ALS data can be successfully used to discriminate between different vegetation types of forests in urban area that have good potential for biomass utilization.

Knowledge-based classification of remote sensing data for the estimation of below- and above-ground organic carbon stocks in riparian forests

Floodplain forests play a crucial role in the storage of organic carbon (Corg). However, modeling of carbon stocks in these dynamic ecosystems remains inherently difficult. Here, we present the spatial estimation of Corg stocks in riparian woody vegetation and soils (to a depth of 1 m) in a Central European floodplain using very high spatial resolution remote sensing data and auxiliary geodata. The research area is the Danube Floodplain National Park in Austria, one of the last remaining wetlands with near-natural vegetation in Central Europe. Different vegetation types within the floodplain show distinct capacities to store Corg. We used remote sensing to distinguish the following vegetation types: meadow, reed bed and hardwood, softwood, and cottonwood forests. Spectral and knowledge-based classification was performed with object-based image analysis. Additional knowledge rules included distances to the river, object area, and slope information. Five different classification schemes based on spectral values and additional knowledge rules were compared and validated. Validation data for the classification accuracy were derived from forest inventories and topographical maps. Overall accuracy for vegetation types was higher for a combination of spectral- and knowledge-based classification than for spectral values alone. While water, reed beds and meadows were clearly detectable, it remained challenging to distinguish the different forest types. The total carbon storage of soils and vegetation was quantified using a Monte Carlo simulation for all classified vegetation types, and the spatial distribution was mapped. The average storage of the study site is 428.9 Mg C ha−1. Despite certain difficulties in vegetation classification this method allows an indirect estimation of Corg stocks in Central European floodplains.

Sami herders’ classification system of reindeer winter pastures – A contribution to adapt forest management to reindeer herding in northern Sweden

The system for classifying vegetation types currently used in Swedish forestry has two major deficiencies when identifying reindeer winter pastures: it uses lichen cover as the sole criterion for defining them, and it ignores the possible adverse effects of snow cover. Based on ethnological field studies, this paper examines Sami reindeer herders' classification of reindeer winter pastures, and compares it to the system used by foresters at different levels of classification. At the lower level, which deals with identifying discrete entities, it is possible to find some correspondence between the representations of forest characteristics used by the Sami herders and the foresters. Reindeer herders discriminate the same factors – tree height, canopy enclosure, stem density, field-layer, bottom-layer – as forest manager, but the former use this knowledge to evaluate the effects on snow cover and ice, and thus on the accessibility of the lichen beneath. Inconsistencies appear at the second level of classification, which consists in ordering this variety of forest characteristics into a classificatory system. There is a mismatch between Sami herders and forester’s representations and classifications of pastures because Sami categories are ‘complex’, i.e. categories including many criteria that have to be combined and balanced before defining the pasture. Herders’ representation of pasture is thus holistic, rather than purely botanical. The comparison of the two classification systems demonstrates that it is impossible to define grazing quality solely in terms of lichen abundance, because of the multidimensional nature of reindeer winter pastures and consequent shifts (spatial and temporal) in its quality.

ESTRUCTURA Y COMPOSICIÓN DE LA VEGETACIÓN DEL CAÑÓN DEL ZOPILOTE, GUERRERO, MÉXICO

An analysis was carried out of the change in the composition and structure of vegetation along an elevation gradient in Zopilote Canyon, located in the Balsas River Basin in Guerrero. In an altitude range from 450 to 2,800 m, 28 sampling plots were established in five different vegetation types, defined by their physiognomy. Within each vegetation type, six 1,000 m2 plots were selected for sampling tree species, and within these a 100 m2 subplot was established for shrubs and two 6 m2 subplots for herbaceous plants. The structural attribute estimated was the relative importance value of each species; cluster analysis was used for classifying vegetation types. In the elevation range studied, 314 species of vascular plants were identified (including four in the NOM-059-ECOL-2001), in 204 genera and 79 families. The most species-rich families were: Asteraceae, Fabaceae, Burseracea, Lamiacaea and Euphorbiaceae. Five vegetation types were identified: tropical deciduous forest, palm swamp, Quercus forest, Pinus forest and mountain cloud forest. The close proximity of these different vegetation types is most likely due to the abrupt changes in topographical and climatic conditions in Zopilote Canyon.

Fusion of multi‐spectral SPOT‐5 images and very high resolution texture information extracted from digital orthophotos for automatic classification of complex Alpine areas

In areas with complex three‐dimensional features, slope and aspect interact with light conditions and significantly affect the spatial structure of images acquired by remote sensing instruments (for example, by changing the distribution of shadows and affecting the texture of high resolution imagery). In this scenario, this paper analyses the potential and the effectiveness of an automatic classification system to identify three fundamental vegetation classes (forest, grassland and crops) in the complex topography of the Italian Alps (Autonomous Province of Trento, Italy). This classification system is based on the fusion of spectral information provided by the SPOT‐5 multi‐spectral channels (Ground Instantaneous Field of View, GIFOV, equal to 10 m) and textural information extracted from airborne digital orthophotos (GIFOV equal to 1 m) and is designed to be user‐friendly. The texture of the digital orthophotos was modelled using defined bidirectional variograms, thereby extracting additional information unavailable in first‐order texture analyses. Using SPOT‐5 multi‐spectral information alone, the classification accuracy in the investigated alpine area was equal to 87.5%, but increased to 92.1% when texture information was included. In particular, the texture information significantly increased the classification accuracy for crops (from 68.9% to 87.9%), especially orchards that tend to be classified as lowland deciduous forests, and herbaceous crops (such as maize) that are often misclassified as grasslands. A further simple majority analysis increased the ability of detecting grassland, crops and urban zones. The combination of the majority analysis and the proposed automatic classification system seems an effective approach to classifying vegetation types in highly fragmented and complex Alpine landscapes on a regional scale.

High‐resolution spatial analysis of mountain landscapes using a low‐altitude remote sensing approach

Mountain landscapes are characterized by great spatial diversity. One basic problem is that there are few high‐resolution data for secluded mountain areas. We present a new approach towards topographic mapping and vegetation monitoring: low‐altitude remote sensing using Kite Aerial Photography (KAP). The study was conducted in the Norwegian mountains above the treeline. We assessed this approach under specific alpine circumstances. Following the collection of data, we derived a digital elevation model (DEM) from two overlapping images. The model was evaluated by the statistical correlation of 265 random field points and extracted heights from (i) linear contour line interpolation of a topographic map of scale 1 : 50 000, (ii) photogrammetric analysis of kite aerial photographs, and (iii) kriging interpolation of approximately 1000 measured field points. Finally, the vegetation was classified, using both supervised and unsupervised methods. The accuracy of the classification results was evaluated by comparing 265 random points, derived from terrestrial mappings, to classified vegetation types by an error matrix. The generation of derived data compared well with data obtained from high‐resolution field surveys and was better than data derived from public‐domain government cartography and moderate‐scale satellite remote sensing data. Our results demonstrate the economic and logistic advantages of this new KAP‐based methodology. The flexibility and outstanding high resolution of our new low‐altitude remote sensing approach proved to be particularly suitable for closing the gap between terrestrial investigations and high‐altitude remote sensing. Hence, our KAP approach addresses the challenge of multiscale research in mountain landscapes.