Vegetation Mapping Research Articles

Long-term continuous changes of vegetation cover in desert oasis of a hyper-arid endorheic basin with LandTrendr algorithm

Ecological water diversion projects (EWDP) is one of the most important initiatives for oases restoration in arid endorheic basins. Fine monitoring of the long-term and continuous changes in vegetation cover before and after the EWDP is important to evaluate the effects of EWDP on ecosystem restoration. In this study, a trajectory-based change detection approach was applied with the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm on Google Earth Engine to map vegetation cover change trajectories in Ejina Oasis in the lower Heihe River Basin of China during 1990–2020. In addition to monotonic changes (i.e., continuous greening and continuous browning), LandTrendr also can flexibly capture the breaks of greening or browning events, including the greening with negative break, browning with positive break, browning to greening, and greening to browning. These changes mainly happened in shrub land and the area with land use transition. Approximately 82 % of the oasis ecosystem has been restored by the implementation of EWDP since 2000. However, in the past decade, the EWDP also caused some negative effects on vegetation cover in some areas. Both the categories of browning with positive break and greening to browning became widespread after 2010, with the cumulative proportions accounting for 79.4 % and 70.1 % of each category. The mean magnitude and slope of change in NDVI in these two categories after 2010 were −0.03 and −0.008 yr−1, −0.12 and −0.035 yr−1, respectively. This research demonstrated that the proposed approach was effective and useful for continuously monitoring the timing and extent of vegetation recovery processes in the hyper-arid endorheic basin. The EWDP plays an important role in vegetation restoration, but we also need to continuously monitor negative impacts which are crucial for environmental assessment and policy making in the arid endorheic basin.

Impact of greenery and waterbody on the cooling of city’s environment: a case of Rajshahi City

ABSTRACT Both plants and bodies of water are crucial to the health of the urban landscape. The degree to which these factors contribute to cooling appears, however, to change depending on factors such as location, time of year, and the mix of land use and land cover. The purpose of this study was to investigate the potential role of trees and water in Rajshahi City Corporation in reducing urban heat island effect. As a first step, satellite images are processed to generate the normalized difference vegetation index (NDVI) and the modified normalized difference water index (MNDWI). The second step is to produce maps of vegetation and water features by using a decision tree (DT) algorithm fed with threshold values from the indices. After that, thermal bands from the Landsat 8 OLI and MODIS Terra Earth observation data are used to create a map of the land surface temperature (LST). Finally, this study assessed the effect of vegetation and water coverage on global surface temperatures by analyzing their spatial distributions. Different kinds of land cover can be found in and around urban centers. Therefore, this study chose two types of water bodies (big and small) and two types of vegetation (sparse and attached/compact) for experiment. Water bodies produce cooler temperatures than other land cover types, as shown by the data. It has been found, however, that cooling rates differ across large and small bodies of water. The surface temperatures of large bodies of water are typically lower than those of smaller bodies of water. Accordingly, it appears that expansive areas significantly impact the cooling of metropolitan environments. The findings also demonstrate a difference in surface temperature between areas of dense and sparse vegetation. The cooling impact of the sparse vegetation is much less than that of the dense and widespread vegetation. There was a significant relationship between surface warming and vegetation cover in this study (R2 = 0.81). Contrarily, the presence of water and vegetation is strongly correlated (R2 = 0.88) with surface warming. The findings also show that surface warming differs depending on land use. According to a study of four typical RCC locations, business districts and industrial zones have a far greater impact on local surface temperatures than do residential developments. The experiment demonstrates that the residential area with a high proportion of vegetation had a lower average temperature by nearly 2°C. The study’s findings highlight the value of maintaining urban parks and water features.

The 30 m vegetation maps from 1990 to 2020 in the Tibetan Plateau

The Tibetan Plateau (TP) is crucial for global climate change and China’s ecological security. Given recent drastic changes in vegetation from climate change and human activities, long-term vegetation monitoring is urgently required. This study produced the vegetation maps of the TP from 1990 to 2020 every ten years using random forest classifier and Landsat imagery. We selected the same stable samples and features for mapping to reduce errors between years and proposed spatial filtering to further improve the accuracy. The overall accuracy surpassed 95.00%, with all Kappa coefficients exceeding 0.95. A further assessment based on sampling sites from literature and field survey was higher than 80%. The importance ranking results indicated that in the TP, climate factors and terrain factors are the most important factors in the vegetation mapping. This study provides a method for mapping vegetation in alpine areas and data support for researching the dynamic change of vegetation on the TP and evaluating its response to climate change.

Quantifying vegetation cover on coastal active dunes using nationwide aerial image analysis

AbstractCoastal active dunes provide vital biodiversity, habitat, and ecosystem services, yet they are one of the most endangered and understudied ecosystems worldwide. Therefore, monitoring the status of these systems is essential, but field vegetation surveys are time‐consuming and expensive. Remotely sensed aerial imagery offers spatially continuous, low‐cost, high‐resolution coverage, allowing for vegetation mapping across larger areas than traditional field surveys. Taking Aotearoa New Zealand as a case study, we used a nationally representative sample of coastal active dunes to classify vegetation from red‐green‐blue (RGB) high‐resolution (0.075–0.75 m) aerial imagery with object‐based image analysis. The mean overall accuracy was 0.76 across 21 beaches for aggregated classes, and key cover classes, such as sand, sandbinders, and woody vegetation, were discerned. However, differentiation among woody vegetation species on semi‐stable and stable dunes posed a challenge. We developed a national cover typology from the classification, comprising seven vegetation types. Classification tree models showed that where human activity was higher, it was more important than geomorphic factors in influencing the relative percent cover of the different active dune cover classes. Our methods provide a quantitative approach to characterizing the cover classes on active dunes at a national scale, which are relevant for conservation management, including habitat mapping, determining species occupancy, indigenous dominance, and the representativeness of remaining active dunes.

Identification and Mapping of Soil, Water and Vegetation of Hattikuni Watershed

Identification and Mapping of Soil, Water and Vegetation of Hattikuni Watershed

Interaction between old settlement areas and natural vegetation

Areas that have been considered as cultural environments since ancient times have been under the influence of anthropogenic activities. That's why vegetation; It has been destroyed as a result of field clearing, grazing, fire, cutting and recently increasing mining activities. Studies carried out to reduce the impact of destruction may cause the vegetation to diversify or the presence of alien species in the field. Studies conducted in these areas, especially pollen analyses, reveal the vegetation transformation that the areas have undergone. In order to reveal the causes, results and interaction of these transformations, the results of the study were shared as a result of the research in the sample of the Gökyurt (Kilistra) ancient settlement area and its surroundings, located within the borders of Meram district of Konya province. In order to achieve the aim of the study, studies on the vegetation characteristics of the field were evaluated and the vegetation map of the field was prepared and the evaluations in the conclusion were reached.

A coupled cartographic approach between bioclimatology and vegetation formations of Mexico

Aims: The task of classifying and naming Mexican vegetation types has been undertaken by previous botanists, ecologists, and mapping agencies. However, discrepancies remain due to the lack of criteria and joint efforts from a geographical and botanical perspective. We aim to unravel the complex interactions between climate and vegetation in Mexico using climatic data and advanced mapping techniques, display in maps the transition from land cover to vegetation maps and couple geobotanical and bioclimatological approaches to provide a sound, unified system for identifying Mexican bioclimatic physiognomic patterns. Methods: Bioclimatic mapping was developed from the Digital Climatic Atlas of Mexico data source. In addition, land cover and vegetation data were obtained from the National Institute of Statistics and Geography of Mexico regrouped as described by the Standardized Hierarchical Vegetation Classification. These data were analysed via standard map crossing technics using geographic information systems. Results and conclusions: The results revealed five ombrotypes and five thermotypes, leading to the identification of 13 different bioclimatic classes, which, when combined with physiognomic types, led us to recognize 11 forests, 3 shrublands and 3 herbaceous formations (at a scale of 1:4,000,000). The core outcome is a detailed bioclimatic/physiognomic vegetation map including forests, shrublands and areas dominated by Herbaceous/Non-Vascular formations. The map highlights the critical importance of harmonising methodologies to ensure comprehensive and accurate insights into Mexico’s bioclimatic diversity. Taxonomic reference: Villaseñor et al. (2005). Syntaxonomic reference: Velázquez et al. (2021). Abbreviations: INEGI = Instituto Nacional de Estadística y Geografía; SECLAVEMEX = Standardized Hierarchical Vegetation Classification; WBCS = Worldwide Bioclimatic Classification System.

Towards standardised large-scale monitoring of peatland habitats through fine-scale drone-derived vegetation mapping

Northern peatlands provide key climate regulating services by sequestering and storing atmospheric carbon as peat, but also provide habitat for specialised plant and animal species. Ecosystem-wide monitoring of the functions associated with these services is necessary to better inform policy and management and facilitate carbon financing schemes. Mapping peatland vegetation as an ecological indicator of their functions using drones has proven promising herein, yet the absence of standardised methods limits implementation.We developed drone-derived vegetation maps and compared them with two types of field-based ground-reference maps: 1) habitat distribution (ecotopes) and 2) habitat quality (status) for five Irish peatlands (35–124 ha). We also explored spatial transferability of our mapping approach across peatlands. First, orthomosaics and digital terrain models (DTM) were derived from drone imagery, after which plant functional types and microforms were separately classified. Second, ecotope and status maps were classified using the proportions of the fine-scale vegetation and the range in DTM within 20x20m grid cells as input predictor variables.Drone-derived ecotope and status maps captured the overall vegetation zonation of the conventional maps well, with the least mismatches for the peatlands displaying clear concentric zonation. Classification performance ranged between 88% for status and 72% for ecotope maps between peatlands. The lower classification performance for ecotopes was partly an artifact from gridding the conventional polygon-shaped ground-reference maps. Further classification errors resulted from artificial landscape features, variable plant phenology, and inaccuracies in the detrended DTM data at peatland-scale. Spatial transferability of the mapping approach was limited. Particularly, using pooled ground-reference data for classification decreased model performance with 5% for status and 10% for ecotope maps, largely because microform and plant functional type proportions associated with peatland habitat classes in the conventional maps varied between peatlands.Our findings highlight that both fine-scale vegetation patterns and habitats can be classified consistently on the peatland-scale using drone-derived imagery products and machine learning classifications. Yet, status is currently mapped notably more accurately than ecotopes. Also, peatland-specific ground-reference data is required until the conventional vegetation classes are more standardised across a wider variety of peatlands and peatland types.

Enhancing precision in coastal dunes vegetation mapping: ultra-high resolution hierarchical classification at the individual plant level

ABSTRACT The classification of ultra-high-resolution (UHR) imagery, characterized by spatial resolutions exceeding 10 cm, presents opportunities and challenges distinct from lower-resolution counterparts. Particularly, challenges are pronounced in some scenarios, such as mapping plant species in coastal environments, where similar vegetation responses and small plant sizes pose additional difficulties. The present work addressed such issues by developing a UHR vegetation cover classification model at the single plant level using data from uncrewed aerial systems (UASs) equipped with a multispectral optical sensor. The model was tested across the San Rossore Regional Park (Italy), where three pilot areas were defined as training-test-validation sites. The proposed solution consists of a hierarchical two-level-of-detail machine learning model based on object-based image analysis (OBIA) and random forest. This model considers spectral features and indices, elevation, and texture and can classify twelve plant species and two service classes (debris and sand) within the study areas. Train and test were carried out utilizing UAS flight data collected during two specific phenological periods and precise field data derived from in-situ vegetation surveys, which provided 937 herbaceous and shrub samples. The model performance was evaluated based on the error matrix and 50-fold stratified cross-validation method, obtaining an overall accuracy (OA) of 0.76 and a standard deviation of 0.08. Such assessment underscored the crucial role of texture information, in addition to radiometric and elevation. Finally, the model was tested against an unseen dataset, proving its transferability (OA equal to 0.62). Although the discussion highlights some aspects to be further improved and claims for future research, the first version of this hierarchical classification model demonstrated its potential for mapping and monitoring coastal sand dune ecosystems, providing data for understanding and, eventually, modeling ecological and biogeomorphological dynamics.

Leucojum vernum in Intorsurii Mountains (South- Eastern Carpathians)

This study investigates the population structure and ecology of the species Leucojum vernum (spring snowflake) in Întorsurii Mountains, South-Eastern Carpathians, where was supposed to be extinct. The research objectives were: (1) to identify the species in Popii Valley, (2) to estimate its population size, (3) to map the species extent, (4) to delineate the species ecological niche, (5) to identify and describe the vegetation types where the species grows, (6) to identify the co-occurring species as indicators of suitable habitat for L. vernum, and (7) to assess the species conservation status in Popii Valley and in Romania. Identification and description of the species population relied on an intensive survey in the whole watershed of Popii Valley. A detailed vegetation map of the Popii Valley watershed was created. For the description of the species habitat, 27 sampling plots were used. The species ecological preferences were drawn by means of the indicator species analysis, climate and geological maps. Climate suitability was checked by projecting the analyzed population in the bioclimatic envelope of L. vernum in Romania, using principal components analysis (PCA) ordination and classes of aridity index. The assessment of the conservation status followed the principles and methodological standards for monitoring protected species at the European Union and global level. In the studied area L. vernum was found in four forest associations, on moist and nutrient-rich soils. The indicator species of the suitable/unsuitable habitats were identified with the help of the phi coefficient of association between species. The population size and species habitat are in favorable conditions, although signs of bulbs collection for gardening were observed. These findings were discussed in a national and global context. An updated map for species extent in Romania was provided in order to support the assessment of the global and national IUCN category.

Integrated Assessment of the Runoff and Heat Mitigation Effects of Vegetation in an Urban Residential Area

Urban vegetation has an essential role in maintaining the hydrological and energy balance. These processes in urban areas have been long overlooked due to the fragmentation and uneven feature of land use and vegetation distribution. Recent advances in remote sensing and the ease of data acquisition have allowed a more precise mapping of vegetation and land cover, making it possible to simulate the above processes at micro scales. This research selects a small typical residential catchment in Japan as the study area and the purpose of this research is to investigate the impact of urban vegetation on mitigating urban runoff and the heat island effect. The remote-sensed Normalized Difference Vegetation Index (NDVI) data were used to represent vegetation spatial distribution and seasonal variation. A single layer canopy model and the Storm Water Management Model were coupled to simulate interception, evapotranspiration, and runoff generation processes. The effects of vegetation amount and landscape patterns on the above processes were also considered. The results showed that the coupled model had a satisfactory performance in the modeling of these processes. When the vegetation amount was set to 1.4 times its original value, the summer total runoff had a 10.7% reduction and the average surface temperature had a 2.5 °C reduction. While the vegetation amount was 0.8 times its original value, the total runoff increased by 6%, and the average surface temperature in summer increased by 1.5 °C. The combination of green roof and dense street trees showed the best mitigation performance among the different landscape patterns. The results of this study could be used as a reference for future green infrastructure development in areas with similar climate and vegetation characteristics.

U-Net Convolutional Neural Network for Mapping Natural Vegetation and Forest Types from Landsat Imagery in Southeastern Australia.

Accurate and comparable annual mapping is critical to understanding changing vegetation distribution and informing land use planning and management. A U-Net convolutional neural network (CNN) model was used to map natural vegetation and forest types based on annual Landsat geomedian reflectance composite images for a 500 km × 500 km study area in southeastern Australia. The CNN was developed using 2018 imagery. Label data were a ten-class natural vegetation and forest classification (i.e., Acacia, Callitris, Casuarina, Eucalyptus, Grassland, Mangrove, Melaleuca, Plantation, Rainforest and Non-Forest) derived by combining current best-available regional-scale maps of Australian forest types, natural vegetation and land use. The best CNN generated using six Landsat geomedian bands as input produced better results than a pixel-based random forest algorithm, with higher overall accuracy (OA) and weighted mean F1 score for all vegetation classes (93 vs. 87% in both cases) and a higher Kappa score (86 vs. 74%). The trained CNN was used to generate annual vegetation maps for 2000-2019 and evaluated for an independent test area of 100 km × 100 km using statistics describing accuracy regarding the label data and temporal stability. Seventy-six percent of pixels did not change over the 20 years (2000-2019), and year-on-year results were highly correlated (94-97% OA). The accuracy of the CNN model was further verified for the study area using 3456 independent vegetation survey plots where the species of interest had ≥ 50% crown cover. The CNN showed an 81% OA compared with the plot data. The model accuracy was also higher than the label data (76%), which suggests that imperfect training data may not be a major obstacle to CNN-based mapping. Applying the CNN to other regions would help to test the spatial transferability of these techniques and whether they can support the automated production of accurate and comparable annual maps of natural vegetation and forest types required for national reporting.

Numerical assessment of changes in land–atmosphere interactions during the rainy season in South America using an updated vegetation map

AbstractLand Use and Land Cover Change (LULCC) is a key driver of changes in land–atmosphere interactions, playing an essential role in climate and weather patterns in South America (SA). This study used a modelling approach to assess the land–atmosphere changes introduced by LULCC over the Brazilian territory. Numerical experiments of early (2006), neutral (2004) and late (2008) rainy season onset years were performed in the Integrated BIosphere Simulator (IBIS) to assess the land‐surface parameters, and in Brazilian Atmospheric Model (BAM) to assess the atmospheric variables. The models were run with a natural (NAT) vegetation map and an updated (UP) vegetation map, which incorporated realistic Brazil deforestation up to the early 2000s. The differences between the two maps were particularly larger over 25°–15°S and 50°–40°W, where the Atlantic Forest and Cerrado biomes were replaced by pasture. IBIS experiments showed that over the area of larger LULCC there was an increase in albedo of up to 8% while reductions in net radiation, surface heat fluxes, surface temperature and surface roughness were noticed. Land–atmosphere feedbacks assessed with BAM experiments showed that LULCC contributed to a drier and colder (stable) atmosphere over central‐east SA that opposes the necessary conditions for the rainy season onset. The changes included increases in surface pressure and low‐level wind associated with reductions in 2 m temperature and surface roughness. These changes contributed to decrease the cloud formation and less precipitation in all three rainy season onset years, but particularly in the late rainy season onset year during September–October–November (SON). The atmospheric changes induced by LULCC show a pattern of dry atmosphere (reduced precipitation), similar to those of late onset years, and indicate that LULCC enhances the late rainy season onset condition over central‐east SA. Additionally, this study highlights the importance of considering up‐to‐date vegetation maps because these changes significantly affect both surface and atmospheric variables.

Using Relationships Between Vegetation and Surface Soil Biogeochemical Properties to Assess Regional Soil Carbon Inventories for South Baffin Island, Nunavut, Canada

AbstractAs Arctic regions warm rapidly, it is unclear whether high‐latitude soil carbon (C) will decrease or increase. Predicting future dynamics of Arctic soil C stocks requires a better understanding of the quantities and controls of soil C. We explore the relationship between vegetation and surface soil C in an understudied region of the Arctic: Baffin Island, Nunavut, Canada. We combined soil C data for three vegetation types—polar desert, mesic tundra, and wet meadow—with a vegetation classification to upscale soil C stocks. Surface soil C differed significantly across vegetation types, and interactions existed between vegetation type and soil depth. Polar desert soils were consistently mineral, with relatively thin organic layers, low percent C, and high bulk density. Mesic soils exhibited an organic‐rich epipedon overlying mineral soil. Wet meadows were consistently organic soil with low bulk density and high percent C. For the top 20 cm, polar desert contained the least soil C (2.17 ± 0.48 kg m−2); mesic tundra had intermediate C (8.92 ± 0.74 kg m−2); wet meadow stored the most C (13.07 ± 0.69 kg m−2). Extrapolating to the top 30 cm, our results suggest that approximately 44 Tg C is stored in the study region with a mean landscape soil C stock of 2.75 kg m−2 for non‐water areas. Combining vegetation mapping with local soil C stocks considerably narrows the range of estimates from other upscaling approaches (27–189 Tg) for soil C on South Baffin Island.

Supervised NDVI Composite Thresholding for Arid Region Vegetation Mapping

Supervised NDVI Composite Thresholding for Arid Region Vegetation Mapping

Ecosystem functional types of the circumpolar Arctic tundra based on the seasonal dynamics of vegetation productivity

Biodiversity, when viewed through the combined lenses of compositional, structural, and functional attributes, provides for a holistic understanding of the complexities found within community assemblages and ecosystems. However, advancement in our understanding of how ecosystem functional diversity interacts with structural and compositional diversity metrics is lacking, in part because universally applied methodologies to derive ecosystem functional classifications are still under development and vary widely across scales, extents and biomes. This study presents a methodology to construct ecosystem functional types (EFTs), or areas of the land surface that function similarly, using the MODIS NDVI record, for the terrestrial circumpolar Arctic. EFTs were derived from the seasonal dynamics of NDVI, over the Arctic tundra at 250 m resolution and compared to bioclimate subzones and to structurally and compositionally defined vegetation units of the Circumpolar Arctic Vegetation Map (CAVM). Correspondence analyses of CAVM EFTs to previously delineated CAVM bioclimatic subzones, physiognomic (vegetation) units and floristic provinces revealed a general congruence, indicating convergence across composition, structure, and function; yet also demonstrated substantial functional variability even within bioclimate subzones and vegetation units. Strong latitudinal gradients in ecosystem function are present, with EFT richness ranging from low (34) in northernmost regions to high (45) in southernmost regions. Locally, the mountainous regions of northern Alaska, and eastern and western Siberia had high spatial variability in ecosystem functioning. Aside from these generalities, we found that EFTs varied widely within individual mapped vegetation units, successfully capturing the functional dimension of biodiversity across the circumpolar Arctic tundra.

Global Overview of the Application of the Braun-Blanquet Approach in Research

Environmental classifications are of paramount importance for assessing the impacts of land-use changes, for prioritizing conservation efforts, and for developing effective management strategies to mitigate the negative impacts of human activities. The aim of our research was to provide as complete an analysis as possible of the studies that have been carried out using the Braun-Blanquet approach. The global review of studies based on the Braun-Blanquet approach includes 1168 papers and was conducted using the PRISMA 2009 methodological recommendations, strict criteria for the selection/quality of papers, and modern methods of data analysis and visualization using VOSviewer software developed by Nees Jan van Eck and Ludo Waltman (Centre for Science and Technology Studies (CWTS) of Leiden University in the Netherlands) (version 1.6.18), which ensures a representative sample, minimization of subjective judgements, and reliability of conclusions. It was noted that the number of publications on Braun-Blanquet is growing exponentially. This is an indication of the scientific interest in this methodology and its continuous further development. Based on a detailed analysis of the keywords, the main research directions and challenges are identified. These include improving the conceptual and methodological foundations of the Braun-Blanquet approach; improvement in regional vegetation classifications, synthesizing them and producing a comprehensive classification for large areas as a basis for biodiversity conservation and sustainable land use; expansion of the geography; compilation and updating of databases of phytosociological data; management of dynamics and vegetation; discussion of the important problem of continuity and discreteness of vegetation in the context of ecological classifications; and vegetation mapping. The top 20 journals publishing the most cited articles were identified, as well as the top 20 most cited journals whose high citation rate is due to the large number of high-quality articles. The analysis of the bibliographic network of papers in dynamics has shown that the structure of relationships is not constant and has changed significantly. The analysis of the authors’ publication activity showed that the vast majority of researchers have a low publication activity and have published only one to three papers. A peculiarity also emerges: if all the most cited authors are concentrated in Eurasia, then most of the most actively published authors are outside Eurasia. The importance of the Braun-Blanquet approach for the study and classification of forest vegetation should be emphasized. In this case, the Braun-Blanquet approach is integrated into forest typologies, increasing their ecological validity and environmental relevance.

Machine Learning for Identifying Emergent and Floating Aquatic Vegetation from Space: A Case Study in the Dniester Delta, Ukraine

Monitoring aquatic vegetation, including both floating and emergent types, plays a crucial role in understanding the dynamics of freshwater ecosystems. Our research focused on the Lower Dniester Basin in Southern Ukraine, covering approximately 1800 square kilometers of steppe plains and wetlands. We applied traditional machine learning algorithms, specifically random forest and boosting trees, to analyze Sentinel-2 satellite imagery for segmenting aquatic vegetation into emergent and floating types. Our methodology was validated against detailed in-situ field measurements collected annually over a 5-year study period. The machine learning classifiers achieved an F1-score of 0.88 ± 0.03 in classifying floating vegetation, outperforming our previously suggested histogram-based thresholding methodology for the same task. While emergent vegetation and open water were easily identifiable from satellite imagery, the robustness and temporal transferability of our methodology included accurately delineating floating vegetation as well. Additionally, we explored the significance of various features through the Minimum Redundancy - Maximum Relevance algorithm. This study highlights advancements in aquatic vegetation mapping and demonstrates a valuable tool for ecological monitoring and future research endeavors.

Assessing hurricane impact on vegetation and endangered deer habitat using airborne lidar and multispectral images

In the lower Florida Keys, the endangered Florida Key deer and numerous other wildlife species inhabit a vulnerable island environment susceptible to storm surges and rising seawater due to low elevation and flat terrain. Timely and reliable assessment of vegetation damage from natural disasters, such as Hurricane Irma, is crucial for effective habitat management. The study’s overall objective is to examine Hurricane Irma’s impact on vegetation on No Name Key, Florida, using remote sensing. The study relates the area change in vegetation obtained from remote sensing analysis to Florida Key deer population changes following the storm. The methodology involved performing a thematic change detection analysis using the following data sources: (1) aerial multispectral images (for pre- and post-Hurricane), (2) airborne lidar data (for pre- and post-Hurricane), (3) an existing vegetation map, and (4) soil data. A Support Vector Machine (SVM) image classification algorithm was applied to pre- and post-storm input image stacks to create pre- and post-Hurricane Irma vegetation maps. We were then able to obtain the area change information (for various vegetation categories) by performing the change detection analysis of the 2 SVM-classified images. The differences in areas following the storm were calculated for 7 affected vegetation types. Using the area change information following Hurricane Irma, we estimated the number of deer supported by the storm-affected vegetation. These estimated deer numbers, based on the area differences in post-Hurricane Irma vegetation types, were compared to observed deer numbers collected during the post-Hurricane Irma Texas A&M Natural Resources Institute (NRI) deer field survey. The results showed the following: mangroves had the largest negative area changes (area loss), followed by pinelands, hardwoods/hammocks, developed areas, and buttonwoods. Freshwater marshes had the largest positive area changes (area gain). The deer's preferred vegetation areas had decreased post-Hurricane Irma, resulting in a reduced deer population compared to pre-storm numbers. The predicted number of the Key deer post-Hurricane Irma fell within a 95% confidence interval of the observed deer population from the post-storm field survey. The study findings and techniques could be applied to study climate change impact, especially sea level rise. This methodology can be valuable in assessing the impact of storms on other wildlife species in similar environments. The applications and methodology are especially relevant considering the increasing frequency and intensity of storm surges and the accelerating rate of sea level rise.

Mapping of Vegetation and Environmental Conditions

There are various methods of vegetation mapping which depend upon the scale of the work and the facts to be shown. Professor H. GAUSSEN’s method has proved as successful in Tropical India as in temperate and mediterranean regions. It aims at representing the vegetation types at medium and small scales.(1) The basic principle is the use of colours of an ecological value. Among the factors of the environment, only those which play a part in the water equilibrium of the vegetation will be shown and the combination of their colours will become representative of the ecology.High temperature and long dry period will increase the evaporation. Rainfall and relative humidity 'will be favourable to the plant growth. Then a conventional, but natural choice of blue and violet shades for humidity, and on the other hand, red or yellow shades for heat and drought will provide all the coloration possibilities.Six classes are defined for Temperature, Rainfall and Xerothermic index (length and intensity of the dry season). Each class has its conventional shade. In any place the climate is easily summarised into an ecological formula which will include for more precision the light factor, the vegetative period and the soil factor.Soil factor is taken into account:-either by its effect on the water balance. The colour is shaded accordingly, for instance, the mangrove, the vegetation of saline soils ;-or by its limiting effect on the floristic composition of the vegetation types. For example, the lateritic caps. These features are shown by the superimposition of a symbol.(2) Another originality of the method is the representation of "Series.of Vegetation". Various physiognomic stages of degradation of the same series are shown by means of lines, stripplings etc. But the same colour, representative of the general ecology of one series is used for all its serial stages.Floristic releves are made in the field. Their comparison brings out the relationship between the successive stages of the series and leads to the most developed type possible under the present ecological conditions. This maximum stage, the least disturbed by biotic factors, is the "plesioclimax". It is the maximum vegetation to be expected under present conditions if human interference it excluded. Its characteristic and dominant species axe used to name the series.The plesioclimax helps to appreciate the ecological-potentiality of the place and therefore will be of some guidance for the introduction of new species.Since similar colours are used for similar climates in the world, the colours show homo-bioclimates under which grow similar vegetation, types even if their floristic composition is different.Several examples are taken from vegetation maps of Tunisia, Algeria, and South India.