Accurate Vegetation Mapping Research Articles

Spectral unmixing-based Arctic plant species analysis using a spectral library and terrestrial hyperspectral Imagery: A case study in Adventdalen, Svalbard

Remote sensing is an invaluable tool for monitoring the rapid changes in Arctic vegetation distribution caused by global warming. Although hyperspectral data consisting of contiguous spectral bands enables the quantitative analysis of remote sensing data, mapping Arctic vegetation using hyperspectral remote sensing remains challenging due to the difficulty in acquiring data from the region. Additionally, the mixed pixel issue, which represents a mixture of more than one plant species due to low-spatial resolution, hinders accurate mapping of Arctic vegetation. To address these limitations, we collected hyperspectral information on the dominant plant species, such as shrubs and graminoids, in Adventdalen, Svalbard, and investigated effective methods for mapping Arctic vegetation using spectral unmixing. First, labeled datasets were constructed for Arctic plant species by extracting pixel data from terrestrial hyperspectral images. A spectral library was developed using the labeled datasets and used for spectral unmixing as endmembers. We employed three established classifiers, random forest, support vector machine, and one-dimensional convolutional neural network (1D-CNN) for hyperspectral image classification. Subsequently, we quantitatively and qualitatively compared the classification performances of these machine learning-based classifiers to determine the optimal classification method for validation purposes. The first derivative spectra of smoothed reflectance (RS,FD) and the 1D-CNN classifier were used to identify ground truth by classifying pixels of Arctic vegetation because they achieved the highest statistical accuracies of 0.9892 (Kappa = 0.9880) and 0.9352 (Kappa = 0.9280) for the two independent test sets and produced the most accurate vegetation maps. The spectral library using the RS,FD spectrum showed high spectral discriminability and potential for estimating the abundance of classes from simulated mixed pixels, resulting in good agreement with the ground truth. Accordingly, our findings provide valuable insights and references for large-scale mapping studies using remote sensing data and offer effective monitoring strategies for Arctic vegetation changes in response to climate change.

Comparison of common classification strategies for large-scale vegetation mapping over the Google Earth Engine platform

• We assessed the effect of various sample properties on classification performance. • The use of pixel time series from fusion data achieved optimal results. • Target class proportions and sample time frames affected accuracy. • Suboptimal methods had a larger impact on the accuracy of minority classes. • The combined use of Sentinel MSI and SAR data provided accurate vegetation maps. Vegetation resources have an essential role in sustainable development due to their close relationship with natural resource management and environmental protection. The monitoring of land use and cover is key for a more sustainable management of these resources, and Earth Observation satellites have provided an increasingly powerful platform for performing this task. To date, numerous classification algorithms have been developed for vegetation mapping, but a comparative evaluation of the strategies commonly used in large-scale applications (50000 km 2 and above) is lacking. We developed a classification framework based on random forests and Sentinel data within the Google Earth Engine platform to assess the performance of various strategies over a complex landscape with a wide range of vegetation classes, plot configurations, and agricultural practices. These strategies differed in key aspects related to the characteristics of the classifier and the sample, and were evaluated by class-specific and general accuracy metrics. We found that the use of pixel time series features from fusion data, class-balanced labels, and multi-season time frames enhanced overall performance by 1.3%–14.1% over alternative approaches, but in some cases also generated tradeoffs of 1.6%–8.5% between recall and precision. In general, suboptimal strategies were particularly ineffective for the detection of infrequent classes. Finally, the different parameter values used in the random forests did not have a significant influence over the results. Our results demonstrate the importance of algorithm design in the effective classification of multiple vegetation classes, corroborate the usefulness of Sentinel data to generate mapping products of high resolution and accuracy, and highlight the importance of cloud computing tools for the development of vegetation mapping tools in large-scale applications. These findings provide general guidelines for the design of future classification frameworks, which are necessary for facilitating a sustainable management of natural resources worldwide.

Machine-Learning Applications in Geosciences: Comparison of Different Algorithms and Vegetation Classes’ Importance Ranking in Wildfire Susceptibility

Susceptibility mapping represents a modern tool to support forest protection plans and to address fuel management. With the present work, we continue with a research framework developed in a pioneristic study at the local scale for Liguria (Italy) and recently adapted to the national scale. In these previous works, a random-forest-based modeling workflow was developed to assess susceptibility to wildfires under the influence of a number of environmental predictors. The main novelties and contributions of the present study are: (i) we compared models based on random forest, multi-layer perceptron, and support vector machine, to estimate their prediction capabilities; (ii) we used a more accurate vegetation map as predictor, allowing us to evaluate the impacts of different types of local and neighboring vegetation on wildfires’ occurrence; (iii) we improved the selection of the testing dataset, in order to take into account the temporal variability of the burning seasons. Wildfire susceptibility maps were finally created based on the output probabilistic predicted values from the three machine-learning algorithms. As revealed with random forest, vegetation is so far the most important predictor variable; the marginal effect of each type of vegetation was then evaluated and discussed.

A novel method to simulate AVIRIS-NG hyperspectral image from Sentinel-2 image for improved vegetation/wildfire fuel mapping, boreal Alaska

• - Vegetation mapped from simulated AVIRIS-NG image offers greater details and accuracy. • − 33% improvement in the new vegetation map accuracy. • - A novel approach to generate improved vegetation maps for Alaska’s boreal domain. Detailed vegetation maps are one of the primary inputs for forest and wildfire management. Hyperspectral remote sensing is a proven technique for detailed and accurate vegetation mapping. However, the availability of recent hyperspectral imagery in Alaska is limited because of the logistics and high cost involved in its acquisition. In this study, we simulated AVIRIS-NG (Airborne Visible InfraRed Imaging Spectrometer - Next Generation) hyperspectral data from widely available Sentinel-2 multispectral data using the Universal Pattern Decomposition Method (UPDM). The UPDM is a spectral unmixing technique that uses detailed ground spectra of vegetation classes and the Spectral Response Functions of AVIRIS-NG and Sentinel-2 sensors to simulate imagery with the same number of bands and spectral resolution as an AVIRIS-NG image. We simulated three images (each covering an area of 100 km × 100 km) from two ecoregions to test portability of the approach. We collected ground spectra of vegetation and bare ground during summers (2019–2021) using a PSR+ 3500 hand-held spectroradiometer and created a spectral library for this study. The Iterative Endmember Selection (IES) algorithm was used to optimize the spectral library and to select the most representative endmembers for simulation: birch, spruce, and gravel. We validated the simulated hyperspectral imagery by comparing it with available AVIRIS-NG images. The simulated image was visually and spectrally similar to the AVIRIS-NG image (RMSE of 0.03 and 0.02 for birch and spruce spectra, respectively). We applied the Random Forest image classification model to derive detailed vegetation maps from the simulated images. Our vegetation map showed an improvement of 33% in the map accuracy compared to the LANDFIRE EVT map. This study demonstrated an efficient and cost-effective approach to derive detailed vegetation maps at the Sentinel scene scale by simulating hyperspectral images in Google’s cloud environment. It offers a novel pathway to generate detailed vegetation and fuel maps for the whole boreal region of Alaska to aid effective forest and fire management.

Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series.

Adaptive management of large herbivores requires an understanding of how spatial-temporal fluctuations in forage biomass and quality influence animal performance. Advances in remote sensing have yielded information about the spatial-temporal dynamics of forage biomass, which in turn have informed rangeland management decisions such as stocking rate and paddock selection for free-ranging cattle. However, less is known about the spatial-temporal patterns of diet quality and their influence on large herbivore performance. This is due to infrequent concurrent ground observations of forage conditions with performance (e.g., mass gain), and previously limited satellite data at fine spatial and temporal scales. We combined multi-temporal field observations of diet quality (weekly) and mass gain (monthly) with satellite-derived phenological metrics (pseudo-daily, using data fusion and interpolation) to model daily mass gains of free-ranging yearling cattle in shortgrass steppe. We used this model to predict grazing season (mid-May to October) mass gains, a key management indicator, across 40 different paddocks grazed over a 10-year period (n=138). We found strong relationships between diet quality and the satellite-derived phenological metrics, especially metrics related to the timing and rate of green-up and senescence. Satellite-derived diet quality estimates were strong predictors of monthly mass gains (R2 =0.68) across a wide range of aboveground net herbaceous production. Season-long predictions of average daily gain and cattle off-mass had mean absolute errors of 8.9% and 2.9%, respectively. The model performed better temporally (across repeated observations in the same paddock) than spatially (across all paddocks within a given year), highlighting the need for accurate vegetation maps and robust field data collection across both space and time. This study demonstrates that free-ranging cattle performance in rangelands is strongly affected by diet quality, which is related to the timing of vegetation green-up and senescence. Senescing vegetation suppressed mass gains, even if adequate forage was available. The satellite-based pseudo-daily approach presented here offers new opportunities for adaptive management of large herbivores, such as identifying within-season triggers to move livestock among paddocks, predicting wildlife herd health, or timing the grazing season to better match earlier spring green-up caused by climate change and plant species invasion.

ACCURACY ANALYSIS OF SENTINEL 2A AND LANDSAT 8 OLI+ SATELLITE DATASETS OVER KANO STATE (NIGERIA) USING VEGETATION SPECTRAL INDICES

Abstract. This study explores the capabilities of Sentinel-2 over Landsat-8 Operational Land Imager (OLI) imageries for vegetation monitoring in the vegetated region of Minjibir LGA in Kano State. Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice. Vegetation indices, comprising the Normalized Difference Vegetation Index (NDVI), Green Chlorophyll Index (GCI), Leaf Area Index (LAI) and Moisture Stress Index (MSI) were determined for each year. The findings showed an increase in Sentinel 2A value of the vegetation indices with respect to Landsat 8 throughout the time of the study (2015–2019). The best average performance over the supervised classification was obtained using Sentinel-2A bands, which are dependent on the training sample and resolution. While the spectral consistency of the data was inferred by cross-calibration analysis using regression analysis. The spatial consistency was assessed by descriptive statistical analysis of examined variables. Regarding the spatial consistency, the mean and standard deviation values of all variables were steady for all seasons excluding for the mean value of the LAI and MSI. Based on this finding, it is recommended that Sentinel-2A data could be used as a complementary data source with Landsat 8 OLI in vegetation assessment.

Data-informed sampling and mapping: an approach to ensure plot-based classifications locate, classify and map rare and restricted vegetation types

A new approach to vegetation sample selection, classification and mapping is described that accounts for rare and restricted vegetation communities. The new method (data-informed sampling and mapping: D-iSM) builds on traditional preferential sampling and was developed to guide conservation and land-use planning. It combines saturation coverage of vegetation point data with a preferential sampling design to produce locally accurate vegetation classifications and maps. Many existing techniques rely entirely or in part on random sampling, modelling against environmental variables, or on assumptions that photo-patterns detected through aerial photographic interpretation or physical landscape features can be attributed to a specific vegetation type. D-iSM uses ground data to inform both classification and mapping phases of a project. The approach is particularly suited to local- and regional-scale situations where disputes between conservation and development often lead to poor planning decisions, as well as in circumstances where highly restricted vegetation types occur within a wider mosaic of more common communities. Benefits of the D-iSM approach include more efficient and more representative floristic sampling, more realistic and repeatable classifications, increased user accuracy in vegetation mapping and increased ability to detect and map rare vegetation communities. Case studies are presented to illustrate the method in real-world classification and mapping projects.

Efficacy of multi-season Sentinel-2 imagery for compositional vegetation classification

Vegetation maps are essential tools for the conservation and management of landscapes as they contain essential information for informing conservation decisions. Traditionally, maps have been created using field-based approaches which, due to limitations in costs and time, restrict the size of the area for which they can be created and frequency at which they can be updated. With the increasing availability of satellite sensors providing multi-spectral imagery with high temporal frequency, new methods for efficient and accurate vegetation mapping have been developed. The objective of this study was to investigate to what extent multi-seasonal Sentinel-2 imagery can assist in mapping complex compositional classifications at fine spatial scales. We deliberately chose a challenging case study, namely a visually and structurally homogenous scrub vegetation (known as kwongan) of Western Australia. The classification scheme consists of 24 target classes and a random 60/40 split was used for model building and validation. We compared several multi-temporal (seasonal) feature sets, consisting of numerous combinations of spectral bands, vegetation indices as well as principal component and tasselled cap transformations, as input to four machine learning classifiers (Support Vector Machines; SVM, Nearest Neighbour; NN, Random Forests; RF, and Classification Trees; CT) to separate target classes. The results show that a multi-temporal feature set combining autumn and spring images sufficiently captured the phenological differences between the classes and produced the best results, with SVM (74%) and NN (72%) classifiers returning statistically superior results compared to RF (65%) and CT (50%). The SWIR spectral bands captured during spring, the greenness indices captured during spring and the tasselled cap transformations derived from the autumn image emerged as most informative, which suggests that ecological factors (e.g. shared species, patch dynamics) occurring at a sub-pixel level likely had the biggest impact on class confusion. However, despite these challenges, the results are auspicious and suggest that seasonal Sentinel-2 imagery has the potential to predict compositional vegetation classes with high accuracy. Further work is needed to determine whether these results are replicable in other vegetation types and regions.

High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features

Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially in conditions of intensive human activities and accelerating global changes. However, it is still challenging to produce high-resolution multiclass vegetation map in high accuracy, due to the incapacity of traditional mapping techniques in distinguishing mosaic vegetation classes with subtle differences and the paucity of fieldwork data. This study created a workflow by adopting a promising classifier, extreme gradient boosting (XGBoost), to produce accurate vegetation maps of two strikingly different cases (the Dzungarian Basin in China and New Zealand) based on extensive features and abundant vegetation data. For the Dzungarian Basin, a vegetation map with seven vegetation types, 17 subtypes, and 43 associations was produced with an overall accuracy of 0.907, 0.801, and 0.748, respectively. For New Zealand, a map of 10 habitats and a map of 41 vegetation classes were produced with 0.946, and 0.703 overall accuracy, respectively. The workflow incorporating simplified field survey procedures outperformed conventional field survey and remote sensing based methods in terms of accuracy and efficiency. In addition, it opens a possibility of building large-scale, high-resolution, and timely vegetation monitoring platforms for most terrestrial ecosystems worldwide with the aid of Google Earth Engine and citizen science programs.

High-Resolution Forest Mapping from TanDEM-X Interferometric Data Exploiting Nonlocal Filtering

In this paper, we discuss the potential and limitations of high-resolution single-pass interferometric synthetic aperture radar (InSAR) data for forest mapping. In particular, we present forest/non-forest classification mosaics of the State of Pennsylvania, USA, generated using TanDEM-X data at ground resolutions down to 6 m. The investigated data set was acquired between 2011 in bistatic stripmap single polarization (HH) mode. Among the different factors affecting the quality of InSAR data, the so-called volume correlation factor quantifies the coherence loss due to volume scattering, which typically occurs in the presence of vegetation, and is a very sensitive indicator for the discrimination of forested from non-forested areas. For this reason, it has been chosen as input observable for performing the classification. In this framework, both standard boxcar and nonlocal filtering methods have been considered for the estimation of the volume correlation factor. The resulting forest/non-forest mosaics have been validated using an accurate vegetation map of the region derived from Lidar-Optic data as external independent reference. Thanks to their outstanding performance in terms of noise reduction, together with spatial features preservation, nonlocal filters show a level of agreement of about 80.5% and we observed a systematic improvement in terms of accuracy with respect to the boxcar filtering at the same resolution of about 4.5 percent points. This approach is therefore of primary importance to achieve a reliable classification at such fine resolution. Finally, the high-resolution forest/non-forest classification product of the State of Pennsylvania presented in this paper demonstrates once again the outstanding capabilities of the TanDEM-X system for a wide spectrum of commercial services and scientific applications in the field of the biosphere.

WeedMap: A Large-Scale Semantic Weed Mapping Framework Using Aerial Multispectral Imaging and Deep Neural Network for Precision Farming

The ability to automatically monitor agricultural fields is an important capability in precision farming, enabling steps towards more sustainable agriculture. Precise, high-resolution monitoring is a key prerequisite for targeted intervention and the selective application of agro-chemicals. The main goal of this paper is developing a novel crop/weed segmentation and mapping framework that processes multispectral images obtained from an unmanned aerial vehicle (UAV) using a deep neural network (DNN). Most studies on crop/weed semantic segmentation only consider single images for processing and classification. Images taken by UAVs often cover only a few hundred square meters with either color only or color and near-infrared (NIR) channels. Although a map can be generated by processing single segmented images incrementally, this requires additional complex information fusion techniques which struggle to handle high fidelity maps due to their computational costs and problems in ensuring global consistency. Moreover, computing a single large and accurate vegetation map (e.g., crop/weed) using a DNN is non-trivial due to difficulties arising from: (1) limited ground sample distances (GSDs) in high-altitude datasets, (2) sacrificed resolution resulting from downsampling high-fidelity images, and (3) multispectral image alignment. To address these issues, we adopt a stand sliding window approach that operates on only small portions of multispectral orthomosaic maps (tiles), which are channel-wise aligned and calibrated radiometrically across the entire map. We define the tile size to be the same as that of the DNN input to avoid resolution loss. Compared to our baseline model (i.e., SegNet with 3 channel RGB (red, green, and blue) inputs) yielding an area under the curve (AUC) of [background=0.607, crop=0.681, weed=0.576], our proposed model with 9 input channels achieves [0.839, 0.863, 0.782]. Additionally, we provide an extensive analysis of 20 trained models, both qualitatively and quantitatively, in order to evaluate the effects of varying input channels and tunable network hyperparameters. Furthermore, we release a large sugar beet/weed aerial dataset with expertly guided annotations for further research in the fields of remote sensing, precision agriculture, and agricultural robotics.

EXPLORING CAPABILITIES OF SENTINEL-2 FOR VEGETATION MAPPING USING RANDOM FOREST

Abstract. Accurate vegetation mapping is essential for monitoring crop and sustainable agricultural practice. This study aims to explore the capabilities of Sentinel-2 data over Landsat-8 Operational Land Imager (OLI) data for vegetation mapping. Two combination of Sentinel-2 dataset have been considered, first combination is 4-band dataset at 10m resolution which consists of NIR, R, G and B bands, while second combination is generated by stacking 4 bands having 10 m resolution along with other six sharpened bands using Gram-Schmidt algorithm. For Landsat-8 OLI dataset, six multispectral bands have been pan-sharpened to have a spatial resolution of 15 m using Gram-Schmidt algorithm. Random Forest (RF) and Maximum Likelihood classifier (MLC) have been selected for classification of images. It is found that, overall accuracy achieved by RF for 4-band, 10-band dataset of Sentinel-2 and Landsat-8 OLI are 88.38 %, 90.05 % and 86.68 % respectively. While, MLC give an overall accuracy of 85.12 %, 87.14 % and 83.56 % for 4-band, 10-band Sentinel and Landsat-8 OLI respectively. Results shown that 10-band Sentinel-2 dataset gives highest accuracy and shows a rise of 3.37 % for RF and 3.58 % for MLC compared to Landsat-8 OLI. However, all the classes show significant improvement in accuracy but a major rise in accuracy is observed for Sugarcane, Wheat and Fodder for Sentinel 10-band imagery. This study substantiates the fact that Sentinel-2 data can be utilized for mapping of vegetation with a good degree of accuracy when compared to Landsat-8 OLI specifically when objective is to map a sub class of vegetation.

Influence of Pansharpening in Obtaining Accurate Vegetation Maps

ABSTRACTIn recent decades, there has been a decline in ecosystem services. Thus, the development of reliable methodologies to monitor ecosystems is becoming important. In this context, the availability of very high resolution sensors offer practical and cost-effective means for good environmental management. However, improvements in the data received are becoming necessary to obtain higher quality information in order to get reliable thematic maps. One improvement is pansharpening, which enhances the spatial resolution of the multispectral bands by incorporating information from a panchromatic image. The main goal of this work was to assess the influence of pansharpening techniques in obtaining precise vegetation maps. Thus, pixel- and object-based classification techniques were implemented and applied to fused imagery using different pansharpening algorithms. Worldview-2 high resolution imagery was used due to its excellent spatial and spectral characteristics. The Teide National Park, in The Canary Islands (Spain), was chosen as the study area since it is a vulnerable heterogeneous ecosystem. The vegetation classes of interest considered were established by the National Park conservation managers. Weighted Wavelet ‘à trous’ through Fractal Dimension Maps pansharpening algorithm demonstrated a superior performance in the image fusion preprocessing step, while the most appropriate classifier to generate accurate vegetation thematic maps in heterogenic and mixed ecosystems was the Bayes method after the segmentation stage, even though Support Vector Machine achieved the highest overall accuracy.

Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA

The Arctic is currently undergoing intense changes in climate; vegetation composition and productivity are expected to respond to such changes. To understand the impacts of climate change on the function of Arctic tundra ecosystems within the global carbon cycle, it is crucial to improve the understanding of vegetation distribution and heterogeneity at multiple scales. Information detailing the fine-scale spatial distribution of tundra communities provided by high resolution vegetation mapping, is needed to understand the relative contributions of and relationships between single vegetation community measurements of greenhouse gas fluxes (e.g., ~1 m chamber flux) and those encompassing multiple vegetation communities (e.g., ~300 m eddy covariance measurements). The objectives of this study were: (1) to determine whether dominant Arctic tundra vegetation communities found in different locations are spectrally distinct and distinguishable using field spectroscopy methods; and (2) to test which combination of raw reflectance and vegetation indices retrieved from field and satellite data resulted in accurate vegetation maps and whether these were transferable across locations to develop a systematic method to map dominant vegetation communities within larger eddy covariance tower footprints distributed along a 300 km transect in northern Alaska. We showed vegetation community separability primarily in the 450–510 nm, 630–690 nm and 705–745 nm regions of the spectrum with the field spectroscopy data. This is line with the different traits of these arctic tundra communities, with the drier, often non-vascular plant dominated communities having much higher reflectance in the 450–510 nm and 630–690 nm regions due to the lack of photosynthetic material, whereas the low reflectance values of the vascular plant dominated communities highlight the strong light absorption found here. High classification accuracies of 92% to 96% were achieved using linear discriminant analysis with raw and rescaled spectroscopy reflectance data and derived vegetation indices. However, lower classification accuracies (~70%) resulted when using the coarser 2.0 m WorldView-2 data inputs. The results from this study suggest that tundra vegetation communities are separable using plot-level spectroscopy with hand-held sensors. These results also show that tundra vegetation mapping can be scaled from the plot level (<1 m) to patch level (<500 m) using spectroscopy data rescaled to match the wavebands of the multispectral satellite remote sensing. We find that developing a consistent method for classification of vegetation communities across the flux tower sites is a challenging process, given the spatial variability in vegetation communities and the need for detailed vegetation survey data for training and validating classification algorithms. This study highlights the benefits of using fine-scale field spectroscopy measurements to obtain tundra vegetation classifications for landscape analyses and use in carbon flux scaling studies. Improved understanding of tundra vegetation distributions will also provide necessary insight into the ecological processes driving plant community assemblages in Arctic environments.

A Framework to Combine Three Remotely Sensed Data Sources for Vegetation Mapping in the Central Florida Everglades

A framework was designed to integrate three complimentary remotely sensed data sources (aerial photography, hyperspectral imagery, and LiDAR) for mapping vegetation in the Florida Everglades. An object-based pixel/feature-level fusion scheme was developed to combine the three data sources, and a decision-level fusion strategy was applied to produce the final vegetation map by ensemble analysis of three classifiers k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), and Random Forest (RF). The framework was tested to map 11 land-use/land-cover level vegetation types in a portion of the central Florida Everglades. An informative and accurate vegetation map was produced with an overall accuracy of 91.1 % and Kappa value of 0.89. A combination of the three data sources achieved the best result compared with applying aerial photography alone, or a synergy of two data sources. Ensemble analysis of three classifiers not only increased the classification accuracy, but also generated a complementary uncertainty map for the final classified vegetation map. This uncertainty map was able to identify regions with a high robust classification, as well as areas where classification errors were most likely to occur.

A Vegetation Mapping Strategy for Conifer Forests by Combining Airborne LiDAR Data and Aerial Imagery

. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure characteristics, which are critical for differentiating vegetation communities in compositionally homogeneous forests. Light detection and ranging (LiDAR) can accurately measure the forest vertical and horizontal structures and provide a great opportunity for solving this problem. This study introduces a strategy using both multispectral aerial imagery and LiDAR data to map vegetation composition and structure over large spatial scales. Our approach included the use of a Bayesian information criterion algorithm to determine the optimized number of vegetation groups within mixed conifer forests in two study areas in the Sierra Nevada, California, and an unsupervised classification technique and post hoc analysis to map these vegetation groups across both study areas. The results show that the proposed strategy can recognize four and seven vegetation groups at the two study areas, respectively. Each vegetation group has its unique vegetation structure characteristics or vegetation species composition. The overall accuracy and kappa coefficient of the vegetation mapping results are over 78% and 0.64 for both study sites.Résumé. La cartographie précise de la végétation est essentielle entre autres pour la gestion des ressources naturelles, l’analyse écologique, et la modélisation hydrologique. Les approches d’imagerie multispectrale et hyperspectrale par télédétection se sont avérées de précieuses contributions au processus de la cartographie de la végétation, mais elles ne peuvent fournir qu’un nombre limité de caractéristiques sur la structure de la végétation, qui sont essentielles pour différencier les communautés végétales dans les forêts de composition homogènes. La télédétection par laser «light detection and ranging» (LiDAR) peut mesurer avec précision les structures verticales et horizontales de la forêt, et fournit une formidable opportunité de résoudre ce problème. Cette étude présente une stratégie qui utilise à la fois l’imagerie multispectrale aérienne et des données LiDAR pour cartographier la composition et la structure de la végétation à grandes échelles spatiales. Notre approche comprenait l’utilisation d’un algorithme du critère d’information Bayésien pour déterminer le nombre optimal de groupes de végétation dans les forêts mixtes de conifères sur deux zones d’étude dans les Sierra Nevada, en Californie, ainsi qu’une technique de classification non supervisée et une analyse post hoc pour cartographier ces groupes de végétation dans les deux zones d’étude. Les résultats montrent que la stratégie proposée peut reconnaitre quatre et sept groupes de végétation dans les deux zones d’étude respectivement. Chaque groupe de végétation a des caractéristiques uniques de structure de la végétation ou de composition des espèces de la végétation. La précision globale et le coefficient kappa des résultats de la cartographie de la végétation sont de plus de 78% et 0,64 pour les deux sites d’étude.

Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV

Mapping vegetation in crop fields is an important step in remote sensing applications for precision agriculture. Traditional aerial platforms such as planes and satellites are not suitable for these applications due to their low spatial and temporal resolutions. In this article, a UAV equipped with a commercial camera (visible spectrum) was used for ultra-high resolution image acquisition over a wheat field in the early-season period. From these images, six visible spectral indices (CIVE, ExG, ExGR, Woebbecke Index, NGRDI, VEG) and two combinations of these indices were calculated and evaluated for vegetation fraction mapping, to study the influence of flight altitude (30 and 60m) and days after sowing (DAS) from 35 to 75 DAS on the classification accuracy. The ExG and VEG indices achieved the best accuracy in the vegetation fraction mapping, with values ranging from 87.73% to 91.99% at a 30m flight altitude and from 83.74% to 87.82% at a 60m flight altitude. These indices were also spatially and temporally consistent, allowing accurate vegetation mapping over the entire wheat field at any date. This provides evidence that visible spectral indices derived from images acquired using a low-cost camera onboard a UAV flying at low altitudes are a suitable tool to use to discriminate vegetation in wheat fields in the early season. This opens the doors for the utilisation of this technology in precision agriculture applications such as early site specific weed management in which accurate vegetation fraction mapping is essential for crop-weed classification.

Object-based Vegetation Mapping in the Kissimmee River Watershed Using HyMap Data and Machine Learning Techniques

Accurate and informative vegetation maps are in urgent demand to support the Kissimmee-Okeechobee-Everglades ecosystem restoration project in South Florida. In this study, we evaluated the applicability of fine spatial resolution hyperspectral data collected from the HyMap sensor for both community- and species-level vegetation mapping. Informative and accurate vegetation maps were produced by combining machine learning methods (Support Vector Machines (SVM) and Random Forest (RF)), object-based image analysis techniques, and Minimum Noise Fraction (MNF) data transformation. An overall accuracy of 90% was obtained in discriminating 14 vegetation communities. Classification of a large number of species is also promising. An overall accuracy of 85% was achieved in identifying 55 species using a SVM model. The results indicate that fine spatial resolution hyperspectral data classification using such automated procedure has great potential to replace the manual interpretation of aerial photos for vegetation mapping in heterogeneous wetland ecosystems.

Optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa

The classification of tropical fragmented landscapes and moist forested areas is a challenge due to the presence of a continuum of vegetation successional stages, persistent cloud cover and the presence of small patches of different land cover types. To classify one such study area in West Africa we integrated the optical sensors Landsat Thematic Mapper (TM) and the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) with the Phased Arrayed L-band SAR (PALSAR) sensor, the latter two on-board the Advanced Land Observation Satellite (ALOS), using traditional Maximum Likelihood (MLC) and Neural Networks (NN) classifiers. The impact of texture variables and the use of SAR to cope with optical data unavailability were also investigated. SAR and optical integrated data produced the best classification overall accuracies using both MLC and NN, respectively equal to 91.1% and 92.7% for TM and 95.6% and 97.5% for AVNIR-2. Texture information derived from optical images was critical, improving results between 10.1% and 13.2%. In our study area, PALSAR alone was able to provide valuable information over the entire area: when the three forest classes were aggregated, it achieved 75.7% (with MCL) and 78.1% (with NN) overall classification accuracies. The selected classification and processing methods resulted in fine and accurate vegetation mapping in a previously untested region, exploiting all available sensors synergies and highlighting the advantages of each dataset.

Use of Landsat and SRTM Data to Detect Broad-Scale Biodiversity Patterns in Northwestern Amazonia

Vegetation maps are the starting point for the design of protected areas and regional conservation plans. Accurate vegetation maps are missing for much of Amazonia, preventing the development of effective and compelling conservation strategies. Here we used a network of 160 inventories across northwestern Amazonia to evaluate the use of Landsat and Shuttle Radar Topography Mission (SRTM) data to identify floristic and edaphic patterns in Amazonian forests. We first calculated the strength of the relationship between these remotely-sensed data, and edaphic and floristic patterns in these forests, and asked how sensitive these results are to image processing and enhancement. We additionally asked if SRTM data can be used to model patterns in plant species composition in our study areas. We find that variations in Landsat and SRTM data are strongly correlated with variations in soils and plant species composition, and that these patterns can be mapped solely on the basis of SRTM data over limited areas. Using these data, we furthermore identified widespread patch-matrix floristic patterns across northwestern Amazonia, with implications for conservation planning and study. Our findings provide further evidence that Landsat and SRTM data can provide a cost-effective means for mapping these forests, and we recommend that maps generated from a combination of remotely-sensed and field data be used as the basis for conservation prioritization and planning in these vast and remote forests.