Geospatial analysis of Tamil Nadu Eastern Ghats forest types at landscape level with reference to fragmentation and species diversity

The present study has generated spatial databases on the vegetation type with plant biodiversity, forest fragmentation and disturbance regimes in Tamilnadu parts of Eastern Ghats (EG), India. These databases have been analysed geospatially with landscape ecology approach. The study also includes ground inventory of plant species based on Remote Sensing (RS) data stratification. The vegetation type map was generated from the visual interpretation of two season IRS LISS III data. The spatial landscape analysis of the remotely sensed interpreted images was carried out using customized software, SPLAM. This is first such study in Tamilnadu Eastern Ghats that provides a comprehensive spatial database on vegetation types, disturbance regime and plant species diversity. The study has shown that the dry deciduous and thorn forests have shown better resistance to disturbance compared to the most disturbed evergreen and semievergreen forests. The study outputs are being utilized by forest department and biodiversity boards for conservation action planning and compliance to Convention on Biological Diversity (CBD).     

Geo-spatial approach for phytodiversity characterization in Panna National Park (part), M.P. (India)

Panna National Park is situated in the north-central part of Madhya Pradesh, India. Landscape parameters like fragmentation, porosity, patchiness and jaxtaposition have been analysed for disturbance gradient characterization. Disturbance on biodiversity due to human activities has been studied both qualitatively and quantitatively. The species richness is highest in northern mixed dry deciduous forest followed by dry deciduous open scrub and southern tropical dry deciduous teak forest. Species richness of the open thorny dry deciduous forest with grasses is found to be the lowest. Disturbance analysis indicates that 22.02% of the southern slightly moist teak forests are highly disturbed whereas Anogeissus forest and Riverine forest have 17.04% and 12.41% of the area under high disturbance, respectively. A total of 88 field sample plots were laid to enumerate trees, shrubs, herbs, climbers, etc. Biological richness parameters such as Shannon-Wiener biodiversity index, biodiversity value, ecosystem uniqueness were derived from field data. High biological richness is found in northern mixed dry deciduous forest and mixed dry deciduous forest with bamboo. More than 99% of such areas are falling under medium to high biological richness. Nearly 55% of the gentle and flat to gentle, slope categories were found to have low biological richness. Phytosociological analysis of sampled field data indicated that the number of trees per unit area is the lowest in the Savannah. In inaccessible areas, the species richness and number of trees per unit area is very high. Main forces causing disturbance are search for diamonds, dams on river Ken, settlements in and around the park, grazing and resource utilization by villagers for fodder, animal grazing, fuel-wood, timber, etc.     

Studies on biodiversity using semivariogram approach from IRS-IC LISS-III data

Present study attempts to utilise the spatial information from IRS-IC LISS-III satellite data, through, tie use of Semivartogram apprqatfu for analyzing the overstorey. diversity In forest stands, Semivariance has been calculated for all the pixels in I5x15 pixel window for each of the forest stand using digital numbers from near infrared channel Variation in semivariance has been correlated to stand density and species composition. Analysis of averaged semivariance for overall diversity of forest stands suggested, decreasing semi variance in the order, mixed dry (feciduous forests, mixed scrub forests followed by dry deciduous forest Semivariance has been found to be highly Correlated to tree density (R² = 0.96) suggesting semivariograms as one of the measures for studies on tree density, canapy cover and diversity patterns.     

Landscape analysis in Fambong Lho wildlife sanctuary,East district,Sikkim, India using remote sensing and GIS techniques

The high biological diversity and rich genetic forest resources of the state of Sikkim are to be conserved due to their sensitiveness to environmental degradation through continued human interference. The conservation of bioprospecting zones in natural forests as well as in protected areas is considered important for up keeping genetic resources and their germplasm. Keeping this in view, ‘Fambong Lho’ wildlife sanctuary in East district, Sikkim, has been selected to characterize different communities in the dominant vegetation type of Tropical Broad Leaved Hill Forest and to assess biodiversity controls in respect of different topographical features, such as altitude, slope, and aspects and use these inputs for prioritizing the protected area into different Bioprospective zones for conservation and planning. An altitude above 2000 m, having a slope in the range of 1–5 percent facing south east aspect are the ideal environmental conditions conducive to have high species richness. The analysis of fragmentation indicates that the forests are under stress along the fringes as they are closely associated with the habitations. The biological richness is recorded reasonably within the limits in the core area of the park but further prioritizing of such areas for conservation is necessary in view of impending danger of frequent interaction of human habitation with in the sanctuary for forest resources.     

Multi-scale assessment of invasive plant species diversity using Pléiades 1A,RapidEye and Landsat-8 data

We used a full remote sensing-based approach to assess plant species diversity in large and inaccessible areas affected by Lantana camara L., a common invasive species within the deciduous forests of Western Himalayan region of India, using spectral heterogeneity information extracted from optical data. The spread of L. camara was precisely mapped by Pléiades 1A data, followed by comparing Pléiades 1A, RapidEye and Landsat-8 OLI – assessed plant species diversities in invaded areas. The single plant species analysis was improved by Pléiades 1A-based diversity analysis, and higher species diversity values were observed for mixed vegetation cover. Furthermore, lower Coefficient of Variation and Renyi diversity values were observed where L. camara was the only species, while higher variations were observed in areas with a mixed spectral reflectance. This study was concluded to add a crucial baseline to the previous studies on remote sensing-based solutions for rapid estimation of biodiversity attributes.     

Biodiversity Conservation in the REDD

Background

Forests occur across diverse biomes, each of which shows a specific composition of plant communities associated with the particular climate regimes. Predicted future climate change will have impacts on the vulnerability and productivity of forests; in some regions higher temperatures will extend the growing season and thus improve forest productivity, while changed annual precipitation patterns may show disadvantageous effects in areas, where water availability is restricted. While adaptation of forests to predicted future climate scenarios has been intensively studied, less attention was paid to mitigation strategies such as the introduction of tree species well adapted to changing environmental conditions.

Results

We simulated the development of managed forest ecosystems in Germany for the time period between 2000 and 2100 under different forest management regimes and climate change scenarios. The management regimes reflect different rotation periods, harvesting intensities and species selection for reforestations. The climate change scenarios were taken from the IPCC's Special Report on Emission Scenarios (SRES). We used the scenarios A1B (rapid and successful economic development) and B1 (high level of environmental and social consciousness combined with a globally coherent approach to a more sustainable development). Our results indicate that the effects of different climate change scenarios on the future productivity and species composition of German forests are minor compared to the effects of forest management.

Conclusions

The inherent natural adaptive capacity of forest ecosystems to changing environmental conditions is limited by the long life time of trees. Planting of adapted species and forest management will reduce the impact of predicted future climate change on forests.     

Designing a protected area network for conservation planning in <Emphasis Type="Italic">Jhum</Emphasis> landscapes of Garo Hills,Meghalaya

We studied vegetation and land cover characteristics within the existing array of protected areas (PAs) in South Garo Hills of Meghalaya, northeast India and introduce the concept of protected area network (PAN) and methods to determine linkages of forests among existing PAs. We describe and analyse potential elements of a PAN, including PAs, reserved forests, surrounding buffers as zones of influence, and connecting forest corridors, which collectively can provide old-forest habitat for wildlife species linked across a landscape dominated by jhum (shifting cultivation) agriculture. ANOVA and Chisquare analyses of patch characteristics and forest tree diversity suggested the presence of equally species-rich and diverse old forest cover (tropical evergreen, semi-evergreen and deciduous forest types) in portions of unprotected private and community owned land, which could be designated as additions to, and network linkages among, existing PAs. Such additions and linkages would help provide for conservation of elephants and existing native forest biodiversity and would constitute a PAN in the region. Most (80%) of the total forest cover of the region belongs to private or community owned land. Therefore, such additions could be formally recognized under the aegis of the 2003 amendments of the Wildlife (Protection) Act 1972, which include provisions to designate selected forest patches within private lands as Community Reserves.     

Quantifying Four Decades of Changes in Land Use and Land Cover in India’s Kailash Sacred Landscape: Suggested Option for Priority Based Patch Level Future Forest Conservation

Effective quantification of land cover changes remains a challenge in Himalayan hills and mountains, and has a colossal value addition for natural resource management. Here we present a new robust method for classifying land cover vegetation at physiognomic scale along steep elevational gradients from ~?200 to ~?7000 masl in the Kailash Sacred Landscape, Western Himalaya, India along with four decades of land use and land cover changes (1976–2011) using remote sensing techniques coupled with intensive ground surveys. Results show that forest cover loss was minimum ca 7.14% of existing forest in 1976; but, however forest fragmentation is high especially in montane broad-leaved and subtropical needle leaved forests. This change largely impacted the quality of valuable tree species such as Quercus spp. Post 1976, continuous migration forced conversion of high altitude agricultural lands into grasslands and scrublands. Human settlement expansion was high especially in low altitudinal range valleys between 1000 and 2000 masl and has increased 6.76 fold since 1976, leading to high forest fragmentation in spite of reduced agriculture area in the landscape. Our physiognomic level classified land cover map will be a key for forest managers to prioritize conservation zones for protecting this unique forest land.     

Forest fragmentation in Massachusetts,USA: a town-level assessment using Morphological spatial pattern analysis and affinity propagation

Forest fragmentation has been studied extensively with respect to biodiversity loss, disruption of ecosystem services, and edge effects although the relationship between forest fragmentation and human activities is still not well understood. We classified the pattern of forests in Massachusetts using fragmentation indicators to address these objectives: 1) characterize the spatial pattern of forest fragmentation in Massachusetts towns using Morphological Spatial Pattern Analysis (MSPA); and (2) identify regional trends using archetypal towns in relation to town history, geography and socioeconomic characteristics. Six fragmentation indicators were calculated using MSPA for each town to represent patterns and processes of fragmentation. We then used these indicators and the proportion of forested land to group towns across Massachusetts with similar patterns of fragmentation. Six representative towns typify different types of forest fragmentation, and illustrate the commonalities and differences between different fragmentation types. The objective selection of representative towns suggests that they might be used as the target of future studies, both in retrospective studies that seek to explain current patterns and in analyses that predict future fragmentation trends.     

Tropical rain forest mapping and community analysis of South Andaman Islands (India)

Present study deals with the vegetation type mapping, structure and composition analysis of the tropical forests, spread over 1,294 km² area in South Andaman Islands. Seventeen vegetation classes spreading over 89.92% forested area of the islands were mapped with the overall accuracy of 88.89%. Evergreen, semi-evergreen and mangrove forests were reasonably well distributed forests, while moist deciduous and littoral evergreen were narrowly restricted. The stocking was quite variable across the forest types. 60.04% of forested area was under medium to high canopy density. Secondary and degraded forest types were mapped. Information on floristic composition, structure and diversity of various forest types were obtained from 84 field sample plots. An inventory of 423 species of plants from 101 families included 155 trees, 84 shrubs, 150 herbs and 84 climbers. Tree density and mean basal area ranged from 517 to 900 stems ha⁻¹ and 36.15 to 53.58 m² ha⁻¹ respectively. Evergreen forests accounted for highest diversity followed almost equally by semi-evergreen and moist deciduous forests.     

Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots,UAV-LiDAR data and Sentinel-2 imagery

The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha⁻¹. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R² = 0.62 > 0.52, RMSE = 50.36 Mg ha⁻¹<56.63 Mg ha⁻¹). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R² = 0.62, RMSE = 51.03 Mg ha⁻¹). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.     

西双版纳橡胶种植特点及其对热带森林景观影响的遥感研究

利用遥感技术研究西双版纳橡胶分布格局、时空动态及其对热带森林景观的影响。研究表明： 西双版纳橡胶种植既有明显的地域分布和地带分布差异特征，也有很高的时空动态度； 橡胶种植对土壤侵蚀作用不明显，但是使周边环境破碎度加大，热带森林异质性自然景观向单一人工斑块化景观发展； 西双版纳橡胶种植获得了巨大的社会效益和经济效益，也对热带森林的稳定性和多样性造成威胁。     

Effects of climate change on forests of the eastern United States

A multi‐phased approach was used to estimate potential impacts of climate change on forests of the eastern United States. Phase I was at community‐level and Phase II examined selected species, both using three 2 x CO₂ climate scenarios. Geographic information systems (GIS) and statistical modeling techniques were used to manipulate and analyze climate and vegetation data, and model vegetation responses to climate change. The first two stages of the study indicated possible large‐scale alteration of forest communities by future climate change. Although results varied among climate models, several trends were apparent. In northern states of the study area, ranges of several conifers declined significantly and ranges of oaks and hickories moved northward. In central states, ranges of sugar maple and tulip poplar became much smaller, with concomitant increases in ranges of southern oaks and loblolly pine. In . southern states, American beech declined and ranges of southern oaks increased northward. This paper discusses results of the first two phases and current progress of the third phase.     

Assessing the Priorities for Sustainable Forest Management in the Sikkim Himalaya,India: A Remote Sensing Based Approach

Sikkim is a small, mountainous, Indian state (7,096 km²) located in the eastern Himalayan region. Though a global biodiversity hotspot, it has been relatively less studied. A detailed forest type, density and change dynamics study was undertaken, using SATELLITE remote sensing data and intensive field verification. The landscape was found to be dominated by alpine and nival ecosystems, with a large portion above the tree line, considerable snow cover, and a sizeable area under forest cover (72%, 5,094 km²). A total of 18 landscape components including 14 vegetation classes were delineated, with the major ones being oak forest, alpine meadow, alpine scrub, conifer forest and alder-cardamom agro-forestry. Of the 3,154 km² of forests below the tree line, 40% were found to be dense (>40% tree canopy cover). A sizeable portion of the non dense forests below the tree line was contributed by the degradation of oak forests, which was confirmed by change detection analysis. However on a positive front over the past decade, ban on grazing and felling of trees in forests has been implemented. In order to expand the extent of dense forests, further efforts are needed for the restoration of oak forests such as fire protection, providing alternatives to firewood use, promotion of alder-cardamom agro-forestry in the private lands and protection of the small-sized, fragmented forest patches in the subtropical belt.     

Quantification of carbon stock to understand two different forest management regimes in Kayar Khola watershed,Chitwan, Nepal

The impact of forest management activities on the ability of forest ecosystems to sequester and store atmospheric carbon is of increasing scientific and social concern. This is because a quantitative understanding of how forest management enhances carbon storage is lacking in most forest management regimes. In this paper two forest regimes, government and community-managed, in Kayar Khola watershed, Chitwan, Nepal were evaluated based on field data, very high resolution (VHR) GeoEye-1 satellite image and airborne LiDAR data. Individual tree crowns were generated using multi-resolution segmentation, which was followed by two tree species classification (Shorea robusta and Other species). Species allometric equations were used in both forest regimes for above ground biomass (AGB) estimation, mapping and comparison. The image objects generated were classified per species and resulted in 70 and 82 % accuracy for community and government forests, respectively. Development of the relationship between crown projection area (CPA), height, and AGB resulted in accuracies of R² range from 0.62 to 0.81, and RMSE range from 10 to 25 % for Shorea robusta and other species respectively. The average carbon stock was found to be 244 and 140 tC/ha for community and government forests respectively. The synergistic use of optical and LiDAR data has been successful in this study in understanding the forest management systems.     

Ecosystem functional assessment based on the “optical type” concept and self-similarity patterns: An application using MODIS-NDVI time series autocorrelation

Remote sensing (RS) time series are an excellent operative source for information about the land surface across several scales and different levels of landscape heterogeneity. Ustin and Gamon (2010) proposed the new concept of “optical types” (OT), meaning “optically distinguishable functional types”, as a way to better understand remote sensing signals related to the actual functional behavior of species that share common physiognomic forms but differ in functionality. Whereas the OT approach seems to be promising and consistent with ecological theory as a way to monitor vegetation derived from RS, it received little implementation.This work presents a method for implementing the OT concept for efficient monitoring of ecosystems based on RS time series. We propose relying on an ecosystem's repetitive pattern in the temporal domain (self-similarity) to assess its dynamics. Based on this approach, our main hypothesis is that distinct dynamics are intrinsic to a specific OT. Self-similarity level in the temporal domain within a broadleaf forest class was quantitatively assessed using the auto-correlation function (ACF), from statistical time series analysis. A vector comparison classification method, spectral angle mapper, and principal component analysis were used to identify general patterns related to forest dynamics. Phenological metrics derived from MODIS NDVI time series using the TIMESAT software, together with information from the National Forest Map were used to explain the different dynamics found.Results showed significant and highly stable self-similarity patterns in OTs that corresponded to forests under non-moisture-limited environments with an adaptation strategy based on a strong phenological synchrony with climate seasonality. These forests are characterized by dense closed canopy deciduous forests associated with high productivity and low biodiversity in terms of dominant species. Forests in transitional areas were associated with patterns of less temporal stability probably due to mixtures of different adaptation strategies (i.e., deciduous, marcescent and evergreen species) and higher functional diversity related to climate variability at long and short terms. A less distinct seasonality and even a double season appear in the OT of the broadleaf Mediterranean forest characterized by an open canopy dominated by evergreen-sclerophyllous formations. Within this forest, understory and overstory dynamics maximize functional diversity resulting in contrasting traits adapted to summer drought, winter frosts, and high precipitation variability.     

Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data

Information on tree species composition is crucial in forest management and can be obtained using remote sensing. While the topic has been addressed frequently over the last years, the remote sensing-based identification of tree species across wide and complex forest areas is still sparse in the literature. Our study presents a tree species classification of a large fraction of the Białowieża Forest in Poland covering 62 000 ha and being subject to diverse management regimes. Key objectives were to obtain an accurate tree species map and to examine if the prevalent management strategy influences the classification results. Tree species classification was conducted based on airborne hyperspectral HySpex data. We applied an iterative Support Vector Machine classification and obtained a thematic map of 7 individual tree species (birch, oak, hornbeam, lime, alder, pine, spruce) and an additional class containing other broadleaves. Generally, the more heterogeneous the area was, the more errors we observed in the classification results. Managed forests were classified more accurately than reserves. Our findings indicate that mapping dominant tree species with airborne hyperspectral data can be accomplished also over large areas and that forest management and its effects on forest structure has an influence on classification accuracies and should be actively considered when progressing towards operational mapping of tree species composition.     

Vegetation cover type mapping in mouling national park in Arunachal Pradesh, Eastern Himalayas- an integrated geospatial approach

Improving image classification and its techniques have been of interest while handling satellite data especially in hilly regions with evergreen forests particularly with indistinct ecotones. In the present study an attempt has been made to classify evergreen forests/vegetation in Moulirig National Park of Arunachal Pradesh in Eastern Himalayas using conventional unsupervised classification algorithms in conjunction with DEM. The study area represents climax vegetation and can be broadly classified into tropical, subtropical, temperate and sub-alpine forests. Vegetation pattern in the study area is influenced strongly by altitude, slope, aspect and other climatic factors. The forests are mature, undisturbed and intermixed with close canopy. Rugged terrain and elevation also affect the reflectance. Because of these discrimination among the various forest/vegetation types is restrained on satellite data. Therefore, satellite data in optical region have limitations in pattern recognition due to similarity in spectral response caused by several factors. Since vegetation is controlled by elevation among other factors, digital elevation model (DEM) was integrated with the LISS III multiband data. The overall accuracy improved from 40.81 to 83.67%. Maximum-forested area (252.80 km2) in national park is covered by sub-tropical evergreen forest followed by temperate broad-leaved forest (147.09 km²). This is probably first attempt where detailed survey of remote and inhospitable areas of Semang sub-watershed, in and around western part of Mouling Peak and adjacent areas above Bomdo-Egum and Ramsingh from eastern and southern side have been accessed for detailed ground truth collection for vegetation mapping (on 1:50,000 scale) and characterization. The occurrence of temperate conifer forests and Rhododendron Scrub in this region is reported here for the first time. The approach of DEM integrated with satellite data can be useful for vegetation and land cover mapping in rugged terrains like in Himalayas.     

Modeling Mediterranean forest structure using airborne laser scanning data

The conservation of biological diversity is recognized as a fundamental component of sustainable development, and forests contribute greatly to its preservation. Structural complexity increases the potential biological diversity of a forest by creating multiple niches that can host a wide variety of species. To facilitate greater understanding of the contributions of forest structure to forest biological diversity, we modeled relationships between 14 forest structure variables and airborne laser scanning (ALS) data for two Italian study areas representing two common Mediterranean forests, conifer plantations and coppice oaks subjected to irregular intervals of unplanned and non-standard silvicultural interventions. The objectives were twofold: (i) to compare model prediction accuracies when using two types of ALS metrics, echo-based metrics and canopy height model (CHM)-based metrics, and (ii) to construct inferences in the form of confidence intervals for large area structural complexity parameters.Our results showed that the effects of the two study areas on accuracies were greater than the effects of the two types of ALS metrics. In particular, accuracies were less for the more complex study area in terms of species composition and forest structure. However, accuracies achieved using the echo-based metrics were only slightly greater than when using the CHM-based metrics, thus demonstrating that both options yield reliable and comparable results. Accuracies were greatest for dominant height (Hd) (R² = 0.91; RMSE% = 8.2%) and mean height weighted by basal area (R² = 0.83; RMSE% = 10.5%) when using the echo-based metrics, 99th percentile of the echo height distribution and interquantile distance. For the forested area, the generalized regression (GREG) estimate of mean Hd was similar to the simple random sampling (SRS) estimate, 15.5 m for GREG and 16.2 m SRS. Further, the GREG estimator with standard error of 0.10 m was considerable more precise than the SRS estimator with standard error of 0.69 m.     

Identification of understory invasive exotic plants with remote sensing in urban forests

Invasive exotic plants (IEP) pose a significant threat to many ecosystems. To effectively manage IEP, it is important to efficiently detect their presences and determine their distribution patterns. Remote sensing has been a useful tool to map IEP but its application is limited in urban forests, which are often the sources and sinks for IEP. In this study, we examined the feasibility and tradeoffs of species level IEP mapping using multiple remote sensing techniques in a highly complex urban forest setting. Bush honeysuckle (Lonicera maackii), a pervasive IEP in eastern North America, was used as our modeling species. Both medium spatial resolution (MSR) and high spatial resolution (HSR) imagery were employed in bush honeysuckle mapping. The importance of spatial scale was also examined using an up-scaling simulation from the HSR object based classification. Analysis using both MSR and HSR imagery provided viable results for IEP distribution mapping in urban forests. Overall mapping accuracy ranged from 89.8% to 94.9% for HSR techniques and from 74.6% to 79.7% for MSR techniques. As anticipated, classification accuracy reduces as pixel size increases. HSR based techniques produced the most desirable results, therefore is preferred for precise management of IEP in heterogeneous environment. However, the use of MSR techniques should not be ruled out given their wide availability and moderate accuracy.