Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS

Deforestation and fragmentation are important concerns in managing and conserving tropical forests and have global significance. In the Indian context, in the last one century, the forests have undergone significant changes due to several policies undertaken by government as well as increased population pressure. The present study has brought out spatiotemporal changes in forest cover and variation in forest type in the state of Odisha (Orissa), India, during the last 75 years period. The mapping for the period of 1924–1935, 1975, 1985, 1995 and 2010 indicates that the forest cover accounts for 81,785.6 km² (52.5 %), 56,661.1 km² (36.4 %), 51,642.3 km² (33.2 %), 49,773 km² (32 %) and 48,669.4 km² (31.3 %) of the study area, respectively. The study found the net forest cover decline as 40.5 % of the total forest and mean annual rate of deforestation as 0.69 %?year^?1 during 1935 to 2010. There is a decline in annual rate of deforestation during 1995 to 2010 which was estimated as 0.15 %. Forest type-wise quantitative loss of forest cover reveals large scale deforestation of dry deciduous forests. The landscape analysis shows that the number of forest patches (per 1,000) are 2.463 in 1935, 10.390 in 1975, 11.899 in 1985, 12.193 in 1995 and 15.102 in 2010, which indicates high anthropogenic pressure on the forests. The mean patch size (km²) of forest decreased from 33.2 in 1935 to 5.5 in 1975 and reached to 3.2 by 2010. The study demonstrated that monitoring of long term forest changes, quantitative loss of forest types and landscape metrics provides critical inputs for management of forest resources.     

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.     

Characterization of Solang valley watershed in western Himalaya for bio-resource conservation using remote sensing techniques

The development activities in mountainous region though provide comfort to the human being and enhance the socioeconomic status of the people but create pressure on the bio-resources. In this paper, the current status of land use/landcover and the vegetation communities of the Solang valley watershed in Himachal Pradesh of Indian western Himalaya has been mapped and presented using remote sensing. This watershed area was dominated by alpine and sub-alpine pastures (30.34%) followed by scree slopes (22.34%) and forests (21.06%). Many tree, shrub, and herb species identified in the study area are among the prioritized species for conservation in the Indian Himalayan Region. Thus, scientific interventions and preparation of action plans based on ecological survey are required for conservation of the Solang valley watershed.     

Decadal time-scale monitoring of forest fires in Similipal Biosphere Reserve,India using remote sensing and GIS

Analyzing the spatial extent and distribution of forest fires is essential for sustainable forest resource management. There is no comprehensive data existing on forest fires on a regular basis in Biosphere Reserves of India. The present work have been carried out to locate and estimate the spatial extent of forest burnt areas using Resourcesat-1 data and fire frequency covering decadal fire events (2004–2013) in Similipal Biosphere Reserve. The anomalous quantity of forest burnt area was recorded during 2009 as 1,014.7 km². There was inconsistency in the fire susceptibility across the different vegetation types. The spatial analysis of burnt area shows that an area of 34.2 % of dry deciduous forests, followed by tree savannah, shrub savannah, and grasslands affected by fires in 2013. The analysis based on decadal time scale satellite data reveals that an area of 2,175.9 km² (59.6 % of total vegetation cover) has been affected by varied rate of frequency of forest fires. Fire density pattern indicates low count of burnt area patches in 2013 estimated at 1,017 and high count at 1,916 in 2004. An estimate of fire risk area over a decade identifies 12.2 km² is experiencing an annual fire damage. Summing the fire frequency data across the grids (each 1 km²) indicates 1,211 (26 %) grids are having very high disturbance regimes due to repeated fires in all the 10 years, followed by 711 grids in 9 years and 418 in 8 years and 382 in 7 years. The spatial database offers excellent opportunities to understand the ecological impact of fires on biodiversity and is helpful in formulating conservation action plans.     

Land desertification monitoring and assessment in Yulin of Northwest China using remote sensing and geographic information systems (GIS)

The objective of this study is to develop techniques for assessing and analysing land desertification in Yulin of Northwest China, as a typical monitoring region through the use of remotely sensed data and geographic information systems (GIS). The methodology included the use of Landsat TM data from 1987, 1996 and 2006, supplemented by aerial photos in 1960, topographic maps, field work and use of other existing data. From this, land cover, the Normalised Difference Vegetation Index (NDVI), farmland, woodland and grassland maps at 1:100,000 were prepared for land desertification monitoring in the area. In the study, all data was entered into a GIS using ILWIS software to perform land desertification monitoring. The results indicate that land desertification in the area has been developing rapidly during the past 40 years. Although land desertification has to some extent been controlled in the area by planting grasses and trees, the issue of land desertification is still serious. The study also demonstrates an example of why the integration of remote sensing with GIS is critical for the monitoring of environmental changes in arid and semi-arid regions, e.g. in land desertification monitoring in the Yulin pilot area. However, land desertification monitoring using remote sensing and GIS still needs to be continued and also refined for the purpose of long-term monitoring and the management of fragile ecosystems in the area.     

Detecting vegetation cover change on the summit of Cadillac Mountain using multi-temporal remote sensing datasets: 1979, 2001, and 2007

This study examines the efficacy of management strategies implemented in 2000 to reduce visitor-induced vegetation impact and enhance vegetation recovery at the summit loop trail on Cadillac Mountain at Acadia National Park, Maine. Using single-spectral high-resolution remote sensing datasets captured in 1979, 2001, and 2007, pre-classification change detection analysis techniques were applied to measure fractional vegetation cover changes between the time periods. This popular sub-alpine summit with low-lying vegetation and attractive granite outcroppings experiences dispersed visitor use away from the designated trail, so three pre-defined spatial scales (small, 0-30 m; medium, 0-60 m; and large, 0-90 m) were examined in the vicinity of the summit loop trail with visitor use (experimental site) and a site chosen nearby in a relatively pristine undisturbed area (control site) with similar spatial scales. Results reveal significant changes in terms of rates of vegetation impact between 1979 and 2001 extending out to 90 m from the summit loop trail with no management at the site. No significant differences were detected among three spatial zones (inner, 0-30 m; middle, 30-60 m; and outer, 60-90 m) at the experimental site, but all were significantly higher rates of impact compared to similar spatial scales at the control site (all p?< 0.001). In contrast, significant changes in rates of recovery between 2001 and 2007 were observed in the medium and large spatial scales at the experimental site under management as compared to the control site (all p?< 0.05). Also during this later period a higher rate of recovery was observed in the outer zone as compared to the inner zone at the experimental site (p?< 0.05). The overall study results suggest a trend in the desired direction for the site and visitor management strategies designed to reduce vegetation impact and enhance vegetation recovery at the summit loop trail of Cadillac Mountain since 2000. However, the vegetation recovery has been rather minimal and did not reach the level of cover observed during the 1979 time period. In addition, the advantages and some limitations of using remote sensing technologies are discussed in detecting vegetation change in this setting and potential application to other recreation settings.     

Assessment of regional climatic changes in the Eastern Himalayan region: a study using multi-satellite remote sensing data sets

In this study, an attempt has been made to capture the sensitivity of a mountainous region to elevation-dependent warming and the response of a glacier-laden surface to increasing greenhouse gases (GHGs) and aerosol concentration. Some of the changes Sikkim has undergone due to urban sprawl are as follows: an increase of ~0.7?±?0.46 °C temperature in the past 40 years at an altitude of 5.5 km; a 2.21 km²/year rate of loss of glacierised area in the past 33 years; an increase in absorbed longwave radiation (6?±?2.41 W/m²); an increase in heat fluxes (2?±?0.97 W/m²); a decrease in albedo during the last 30 years; an increase in the concentrations of carbon dioxide (4.42 %), methane (0.61 %), ozone (0.67 %) and black carbon column optical depth (7.19 %); a decrease in carbon monoxide (2.61 %) and an increase in aerosol optical depth (19.16 %) during the last decade; a decrease in precipitation, water yield, discharge and groundwater; and an increase in evapotranspiration during 1971–2005. Detection of three climate signals (1976, 1997 and 2005) in the entire analysis is the quantification of the fact that the climate of Sikkim is moving away from its inter-annual variability. An increase in temperature (0.23 °C/decade) at higher altitude (~5.5 km), suppression of precipitation, decreasing water availability and rapid loss of glacierised area are the evidences of the fact that air pollution is playing a significant role in bringing about regional climatic changes in Sikkim. In this study, change detection method has been used for the first time for the estimation of change in a glacierised area of the region.     

Water quality monitoring of Al-Habbaniyah Lake using remote sensing and in situ measurements

Environmental Monitoring and Assessment - A good quality drinking water is essential for human survival. However, a large percentage of the populations in most developing countries (Nigeria...     

Mapping aboveground woody biomass using forest inventory,remote sensing and geostatistical techniques

Mapping forest biomass is fundamental for estimating CO₂ emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m?×?31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10–40, 40–70 and >70 % of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m³ ha^?1. The total growing stock of the forest was found to be 2,024,652.88 m³. The AGWB ranged from 143 to 421 Mgha^?1. Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE)?=?42.25 Mgha^?1) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha^?1 respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha^?1. The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping.     

Monitoring the local distribution of striped hyenas (Hyaena hyaena L.) in the Eastern Mediterranean Region of Turkey (Hatay) by using GIS and remote sensing technologies

Striped hyenas (Hyaena hyaena L.) are one of the large carnivores whose numbers have rapidly decreased in Turkey. To monitor and assess the distribution of striped hyenas in Mediterranean region of Turkey, geographical information systems (GIS) and remote sensing technologies were implemented. For this purpose, the GIS database was generated and digital maps were produced in ArcGIS 9.2 program, considering some of the main factors including signs of striped hyenas, elevation, slope, land use types, feeding sources, and road network. The land use types in the distribution area of striped hyenas were classified by using ERDAS Imagine program. The results from the land use classification indicated that the signs of striped hyenas mostly distributed over the agricultural areas especially with olive groves, and followed by maquis. It was found that there was a spatial relationship between the signs of striped hyenas and feeding sources such as organic waste centers and a chicken farm in the region.     

Monitoring and identification of spatiotemporal landscape changes in multiple remote sensing images by using a stratified conditional Latin hypercube sampling approach and geostatistical simulation

This study develops a stratified conditional Latin hypercube sampling (scLHS) approach for multiple, remotely sensed, normalized difference vegetation index (NDVI) images. The objective is to sample, monitor, and delineate spatiotemporal landscape changes, including spatial heterogeneity and variability, in a given area. The scLHS approach, which is based on the variance quadtree technique (VQT) and the conditional Latin hypercube sampling (cLHS) method, selects samples in order to delineate landscape changes from multiple NDVI images. The images are then mapped for calibration and validation by using sequential Gaussian simulation (SGS) with the scLHS selected samples. Spatial statistical results indicate that in terms of their statistical distribution, spatial distribution, and spatial variation, the statistics and variograms of the scLHS samples resemble those of multiple NDVI images more closely than those of cLHS and VQT samples. Moreover, the accuracy of simulated NDVI images based on SGS with scLHS samples is significantly better than that of simulated NDVI images based on SGS with cLHS samples and VQT samples, respectively. However, the proposed approach efficiently monitors the spatial characteristics of landscape changes, including the statistics, spatial variability, and heterogeneity of NDVI images. In addition, SGS with the scLHS samples effectively reproduces spatial patterns and landscape changes in multiple NDVI images.     

Remote sensing of aquatic vegetation: theory and applications

Aquatic vegetation is an important component of wetland and coastal ecosystems, playing a key role in the ecological functions of these environments. Surveys of macrophyte communities are commonly hindered by logistic problems, and remote sensing represents a powerful alternative, allowing comprehensive assessment and monitoring. Also, many vegetation characteristics can be estimated from reflectance measurements, such as species composition, vegetation structure, biomass, and plant physiological parameters. However, proper use of these methods requires an understanding of the physical processes behind the interaction between electromagnetic radiation and vegetation, and remote sensing of aquatic plants have some particular difficulties that have to be properly addressed in order to obtain successful results. The present paper reviews the theoretical background and possible applications of remote sensing techniques to the study of aquatic vegetation.     

Sediment grain size estimation using airborne remote sensing, field sampling, and robust statistic

Remote sensing has been used since the 1980s to study parameters in relation with coastal zones. It was not until the beginning of the twenty-first century that it started to acquire imagery with good temporal and spectral resolution. This has encouraged the development of reliable imagery acquisition systems that consider remote sensing as a water management tool. Nevertheless, the spatial resolution that it provides is not adapted to carry out coastal studies. This article introduces a new methodology for estimating the most fundamental physical property of intertidal sediment, the grain size, in coastal zones. The study combines hyperspectral information (CASI-2 flight), robust statistic, and simultaneous field work (chemical and radiometric sampling), performed over Santander Bay, Spain. Field data acquisition was used to build a spectral library in order to study different atmospheric correction algorithms for CASI-2 data and to develop algorithms to estimate grain size in an estuary. Two robust estimation techniques (MVE and MCD multivariate M-estimators of location and scale) were applied to CASI-2 imagery, and the results showed that robust adjustments give acceptable and meaningful algorithms. These adjustments have given the following R(2) estimated results: 0.93 in the case of sandy loam contribution, 0.94 for the silty loam, and 0.67 for clay loam. The robust statistic is a powerful tool for large dataset.     

Determining the impact of urban components on land surface temperature of Istanbul by using remote sensing indices

For the past 60 years, Istanbul has been experiencing an accelerated urban expansion. This urban expansion is leading to the replacement of natural surfaces by various artificial materials. This situation has a critical impact on the environment due to the alteration of heat energy balance. In this study, the effect upon the urban heat island (UHI) of Istanbul was analyzed using 2009 dated Landsat 5 Thematic Mapper (TM) data. An Index Based Built-up Index (IBI) was used to derive artificial surfaces in the study area. To produce the IBI index, Soil-Adjusted Vegetation Index, Normalized Difference Built-up Index, and Modified Normalized Difference Water Index were calculated. Land surface temperature (LST) distribution was derived from Landsat 5 TM images using a mono-window algorithm. In addition, 24 transects were selected, and different regression models were applied to explore the correlation between LST and IBI index. The results show that artificial surfaces have a positive exponential relationship with LST rather than a simple linear one. An ecological evaluation index of the region was calculated to explore the impact of both the vegetated land and the artificial surfaces on the UHI. Therefore, the quantitative relationship of urban components (artificial surfaces, vegetation, and water) and LST was examined using multivariate statistical analysis, and the correlation coefficient was obtained as 0.829. This suggested that the areas with a high rate of urbanization will accelerate the rise of LST and UHI in Istanbul.     

Assessing impact of industrialization in terms of LULC in a dry tropical region (Chhattisgarh), India using remote sensing data and GIS over a period of 30 years

The main focus of the paper is to assess the land use/ land cover (LULC) change in northern Chhattisgarh due to industrialization using remote sensing and Geographical Information System (GIS). The impact was assessed using an information extraction method applied to temporal satellite data (LANDSAT and IRS scenes) in GIS domain. For assessing the impact on natural resources, the classification scheme was restricted to (1) Forest patches ((a) completely cleared, (b) partially cleared, (c) least affected), (2) Non-Forest ((d) completely changed, (e) least changed), (3) Industrial/Mining area, and (4) River. Over the three decades 22.22% of forests have been completely cleared and converted to industrial setup. Another 25% is completely cleared and 10% is degraded. Around 4% of agricultural area is totally affected due to industrial activity. Random assessment of plant distribution (Trees, Shrubs and Herbs) indicates significant changes in the herb distribution directly related to distance gradient form the industrial/mining setup. Visual recording, socio-economic survey and satellite data also helped in delineation of extent of environmental pollution in forest and non-forest areas. The paper presents methodology for the environmental impact assessment.     

Assessing regional environmental quality by integrated use of remote sensing,GIS, and spatial multi-criteria evaluation for prioritization of environmental restoration

This study was an attempt to analyse the regional environmental quality with the application of remote sensing, geographical information system, and spatial multiple criteria decision analysis and, to project a quantitative method applicable to identify the status of the regional environment of the study area. Using spatial multi-criteria evaluation (SMCE) approach with expert knowledge in this study, an integrated regional environmental quality index (REQI) was computed and classified into five levels of regional environment quality viz. worse, poor, moderate, good, and very good. During the process, a set of spatial criteria were selected (here, 15 criterions) together with the degree of importance of criteria in sustainability of the regional environment. Integrated remote sensing and GIS technique and models were applied to generate the necessary factors (criterions) maps for the SMCE approach. The ranking, along with expected value method, was used to standardize the factors and on the other hand, an analytical hierarchy process (AHP) was applied for calculating factor weights. The entire process was executed in the integrated land and water information system (ILWIS) software tool that supports SMCE. The analysis showed that the overall regional environmental quality of the area was at moderate level and was partly determined by elevation. Areas under worse and poor quality of environment indicated that the regional environmental status showed decline in these parts of the county. The study also revealed that the human activities, vegetation condition, soil erosion, topography, climate, and soil conditions have serious influence on the regional environment condition of the area. Considering the regional characteristics of environmental quality, priority, and practical needs for environmental restoration, the study area was further regionalized into four priority areas which may serve as base areas of decision making for the recovery, rebuilding, and protection of the environment.     

Application of remote sensing data and GIS for landslide risk assessment as an environmental threat to Izmir city (west Turkey)

In this study, landslide risk assessment for Izmir city (west Turkey) was carried out, and the environmental effects of landslides on further urban development were evaluated using geographical information systems and remote sensing techniques. For this purpose, two different data groups, namely conditioning and triggering data, were produced. With the help of conditioning data such as lithology, slope gradient, slope aspect, distance from roads, distance from faults and distance from drainage lines, a landslide susceptibility model was constructed by using logistic regression modelling approach. The accuracy assessment of the susceptibility map was carried out by the area under curvature (AUC) approach, and a 0.810 AUC value was obtained. This value shows that the map obtained is successful. Due to the fact that the study area is located in an active seismic region, earthquake data were considered as primary triggering factor contributing to landslide occurrence. In addition to this, precipitation data were also taken into account as a secondary triggering factor. Considering the susceptibility data and triggering factors, a landslide hazard index was obtained. Furthermore, using the Aster data, a land-cover map was produced with an overall kappa value of 0.94. From this map, settlement areas were extracted, and these extracted data were assessed as elements at risk in the study area. Next, a vulnerability index was created by using these data. Finally, the hazard index and the vulnerability index were combined, and a landslide risk map for Izmir city was obtained. Based on this final risk map, it was observed that especially south and north parts of the Izmir Bay, where urbanization is dense, are threatened to future landsliding. This result can be used for preliminary land use planning by local governmental authorities.     

Monitoring the urban green spaces and landscape fragmentation using remote sensing: a case study in Osmaniye,Turkey

Urban green spaces play a significant role in management of physical activity, psychological well-being, and public health of urban residents. With the expansion of urban areas in Turkey during the past decades, urban green spaces have been fragmented and dispersed causing impairment and environmental degradation. The purpose of this study is to model urban green space distribution by focusing on the landscape fragmentation in city of Osmaniye using remote sensing and geographic information system technology. Normalized difference vegetation index (NDVI) and urban landscape ratio (ULR) were used to assess the proximity and spatial arrangement of urban green spaces within the neighbor landscapes to quantify the urban land use effect. The geospatial analysis results showed that increase in total built-up area and population has significantly decreased the urban green space cover because of high levels of landscape fragmentation in urban city center. Also, due to high levels of landscape fragmentation, approximately 45% of the Osmaniye city is estimated to become urbanized by 2030. This study demonstrated the benefits of directional vegetation index application with geospatial analyses in characterizing the environmental quality for planning and management of urban green spaces. This approach could be used for determining the future urban land development scenarios correlating with regional planning procedures.