Monitoring global change: Comparison of forest cover estimates using remote sensing and inventory approaches

Satellite-based remote sensing offers great potential for frequent assessment of forest cover over broad spatial scales, however, calibration and validation using ground-based surveys are needed. In this study, forest cover estimates for the United States from a recently developed land surface cover map generated from satellite remote sensing data were compared to state-level inventory data from the U.S. National Resources Planning Act Timber Database. The land cover map was produced at the U.S. Geological Survey EROS Data Center and is based on imagery from the AVHRR sensor (spatial resolution 1.1 km). Vegetation type was classified using the temporal signal in the Normalized Difference Vegetation Index derived from AVHRR data. Comparisons revealed close agreement in the estimate of forest cover for extensively forested states with large polygons of relatively similar vegetation such as Oregon. Larger forest cover differences were observed in other states with some regional patterns in the level of agreement apparent.Comparisons in inventory- and remote sensing-based estimates of current forested area with potential vegetation maps indicated the magnitude of past land use change and the potential for future changes. The remote sensing approach appears to hold promise for conducting surveys of forest cover where inventory data are limited or where rates of vegetation change, due to human or climatic factors, are rapid.     

Vegetation dynamics, and land use and land cover change in the Bale Mountains, Ethiopia

Shifts in biological communities are occurring at rapid rates as human activities induced global climate change increases. Understanding the effects of the change on biodiversity is important to reduce loss of biodiversity and mass extinction, and to insure the long-term persistence of natural resources and natures’ services. Especially in remote landscapes of developing countries, precise knowledge about on-going processes is scarce. Here we apply satellite imagery to assess spatio-temporal land use and land cover change (LULCC) in the Bale Mountains for a period of four decades. This study aims to identify the main drivers of change in vegetation patterns and to discuss the implications of LULCC on spatial arrangements and trajectories of floral communities. Remote sensing data acquired from Landsat MSS, Landsat ETM + and SPOT for four time steps (1973, 1987, 2000, and 2008) were analyzed using 11 LULC units defined based on the dominant plant taxa and cover types of the habitat. Change detection matrices revealed that over the last 40?years, the area has changed from a quite natural to a more cultural landscape. Within a representative subset of the study area (7,957.5?km^?2), agricultural fields have increased from 1.71% to 9.34% of the total study area since 1973. Natural habitats such as upper montane forest, afroalpine grasslands, afromontane dwarf shrubs and herbaceous formations, and water bodies also increased. Conversely, afromontane grasslands have decreased in size by more than half (going from 19.3% to 8.77%). Closed Erica forest also shrank from 15.0% to 12.37%, and isolated Erica shrubs have decreased from 6.86% to 5.55%, and afroalpine dwarf shrubs and herbaceous formations reduced from 5.2% to 1.56%. Despite fluctuations the afromontane rainforest (Harenna forest), located south of the Bale Mountains, has remained relatively stable. In conclusion this study documents a rapid and ecosystem-specific change of this biodiversity hotspot due to intensified human activities (e.g., deforestation, agriculture, infrastructure expansion). Specifically, the ecotone between the afromontane and the afroalpine area represent a “hotspot of biodiversity loss” today. Taking into consideration the projections of regional climate warming and modified precipitation regimes, LULCC can be expected to become even more intensive in the near future. This is likely to impose unprecedented pressures on the largely endemic biota of the area.     

Spatial Distribution of Forest Fires and Controlling Factors in Andhra Pradesh, India Using Spot Satellite Datasets

Fires are one of the major causes of forest disturbance and destruction in several dry deciduous forests of southern India. In this study, we use remote sensing data sets in conjunction with topographic, vegetation, climate and socioeconomic factors for determining the potential causes of forest fires in Andhra Pradesh, India. Spatial patterns in fire characteristics were analyzed using SPOT satellite remote sensing datasets. We then used nineteen different metrics in concurrence with fire count datasets in a robust statistical framework to arrive at a predictive model that best explained the variation in fire counts across diverse geographical and climatic gradients. Results suggested that, of all the states in India, fires in Andhra Pradesh constituted nearly 13.53% of total fires. District wise estimates of fire counts for Andhra Pradesh suggested that, Adilabad, Cuddapah, Kurnool, Prakasham and Mehbubnagar had relatively highest number of fires compared to others. Results from statistical analysis suggested that of the nineteen parameters, population density, demand of metabolic energy (DME), compound topographic index, slope, aspect, average temperature of the warmest quarter (ATWQ) along with literacy rate explained 61.1% of total variation in fire datasets. Among these, DME and literacy rate were found to be negative predictors of forest fires. In overall, this study represents the first statewide effort that evaluated the causative factors of fire at district level using biophysical and socioeconomic datasets. Results from this study identify important biophysical and socioeconomic factors for assessing ‘forest fire danger’ in the study area. Our results also identify potential ‘hotspots’ of fire risk, where fire protection measures can be taken in advance. Further this study also demonstrate the usefulness of best-subset regression approach integrated with GIS, as an effective method to assess ‘where and when’ forest fires will most likely occur.     

Seasonal variations in the relationship between landscape pattern and land surface temperature in Indianapolis, USA

This paper intended to examine the seasonal variations in the relationship between landscape pattern and land surface temperature based on a case study of Indianapolis, United States. The integration of remote sensing, GIS, and landscape ecology methods was used in this study. Four Terra's ASTER images were used to derive the landscape patterns and land surface temperatures (LST) in four seasons in the study area. The spatial and ecological characteristics of landscape patterns and LSTs were examined by the use of landscape metrics. The impact of each land use and land cover type on LST was analyzed based on the measurements of landscape metrics. The results show that the landscape and LST patterns in the winter were unique. The rest of three seasons apparently had more agreeable landscape and LST patterns. The spatial configuration of each LST zone conformed to that of each land use and land cover type with more than 50% of overlap in area for all seasons. This paper may provide useful information for urban planers and environmental managers for assessing and monitoring urban thermal environments as result of urbanization.     

The potential of remote sensing for monitoring land cover changes and effects on physical geography in the area of Kayisdagi Mountain and its surroundings (Istanbul)

The effect of land cover change, from natural to anthropogenic, on physical geography conditions has been studied in Kayisdagi Mountain. Land degradation is the most important environmental issue involved in this study. Most forms of land degradation are natural processes accelerated by human activity. Land degradation is a human induced or natural process that negatively affects the ability of land to function effectively within an ecosystem. Environmental degradation from human pressure and land use has become a major problem in the study area because of high population growth, urbanization rate, and the associated rapid depletion of natural resources. When studying the cost of land degradation, it is not possible to ignore the role of urbanization. In particular, a major cause of deforestation is conversion to urban land. The paper reviews the principles of current remote sensing techniques considered particularly suitable for monitoring Kayisdagi Mountain and its surrounding land cover changes and their effects on physical geography conditions. In addition, this paper addresses the problem of how spatially explicit information about degradation processes in the study area rangelands can be derived from different time series of satellite data. The monitoring approach comprises the time period between 1990 and 2005. Satellite remote sensing techniques have proven to be cost effective in widespread land cover changes. Physical geography and particularly natural geomorphologic processes like erosion, mass movement, physical weathering, and chemical weathering features etc. have faced significant unnatural variation.     

The application of satellite data for the quantification of mangrove loss and coastal management in the Godavari estuary, East Coast of India

The mangrove formations of Godavari estuary are due to silting over many centuries. The estuary covers an area of 62,000 ha of which dense Coringa mangrove forest spread in 6,600 ha. Satellite sensor data was used to detect change in the mangrove cover for a period of 12 years (1992-2004). It was found that an area of about 1,250 ha of mangroves was destroyed by anthropogenic interference like aquaculture, and tree felling etc. It was found that mangrove's spectral response/digital number (DN) value is much lower than non-mangrove vegetation such as plantation and paddy fields in SWIR band. By taking this as an advantage, spectral data was utilized for clear demarcation of mangroves from nearby paddy fields and other vegetation. Simpson's diversity index, which is a measure of biodiversity, was found to be 0.09, showing mangroves dominance. Ecological parameters like mud-flats/swamps, mangrove cover alterations, and biodiversity status are studied in detail for a period of 12 years. The increase in mangrove front towards coast was delineated using remote sensing data. The major advantages of remote sensing data is monitoring of change periodically. The combination of moderate and high-resolution data provided detailed coastal land use maps for implementing coastal regulation measures. The classification accuracy has been achieved is 90%. Overall, simple and viable measures are suggested based on multi-spectral data to sustain this sensitive coastal ecology.     

Land quality assessment and sustainable land use in Salt Lake (Tuz Gölü) specially protected area

Land quality assessment is of great important for decisions on sustainable land uses and the conservation of ecosystems of high biodiversity value. The main objective of this study was to determine properties of land quality of specially protected area of the Salt Lake using CORINE land quality assessment methodology and to propose a revision for this model. In addition to field study and laboratory analysis, digital soil map, digital elevation model, satellite image and climatic data were used and analyzed with remote sensing and geographic information systems techniques to generate actual land quality map and to form database for study area. Results show that 42.1% of the study area has high quality, 28.7% was classified as moderate quality and 4.0% was determined as low quality. On the other hand, some places classified as moderate quality, were not accurately classified. These places should be low land quality due to content of high salt concentration. Therefore, to resolve this insufficient situation, some soil parameters such as chemical properties should be collaborated with this model. In this case, moderate land quality level of the study area decreased from 28.7 to 12.3%, whereas 4.0% of low quality land of the study area increased to 20.8%. In addition, this research was also carried out to provide guidance for planner or decision makers about specially protected area of the Salt Lake.     

Identification of erosional and inundation hazard zones in Ken?CBetwa river linking area, India, using remote sensing and GIS

Ken-Betwa river link is one of the pilot projects of the Inter Linking of Rivers program of Government of India in Bundelkhand Region. It will connect the Ken and Betwa rivers through a system of dams, reservoirs, and canals to provide storage for excess rainfall during the monsoon season and avoid floods. The main objective of this study is to identify erosional and inundation prone zones of Ken-Betwa river linking site in India using remote sensing and geographic information system tools. In this study, Landsat Thematic Mapper data of year 2005, digital elevation model from the Shuttle Radar Topographic Mission, and other ancillary data were analyzed to create various thematic maps viz. geomorphology, land use/land cover, NDVI, geology, soil, drainage density, elevation, slope, and rainfall. The integrated thematic maps were used for hazard zonation. This is based on categorizing the different hydrological and geomorphological processes influencing the inundation and erosion intensity. Result shows that the southern part of the study area which lies in Panna district of Madhya Pradesh, India, is more vulnerable than the other areas.     

Use of object-oriented classification and fragmentation analysis (1985�C2008) to identify important areas for conservation in Cockpit Country, Jamaica

Forest fragmentation is one of the most important threats to global biodiversity, particularly in tropical developing countries. Identifying priority areas for conservation within these forests is essential to their effective management. However, this requires current, accurate environmental information that is often lacking in developing countries. The Cockpit Country, Jamaica, contains forests of international importance in terms of levels of endemism and overall diversity. These forests are under severe threat from the prospect of bauxite mining and other anthropogenic disturbances. In the absence of adequate, up-to-date ecological information, we used satellite remote sensing data and fragmentation analysis to identify interior forested areas that have experienced little or no change as priority conservation sites. We classified Landsat images from 1985, 1989, 1995, 2002, and 2008, using an object-oriented method, which allowed for the inclusion of roads. We conducted our fragmentation analysis using metrics to quantify changes in forest patch number, area, shape, and aggregation. Deforestation and fragmentation fluctuated within the 23-year period but were mostly confined to the periphery of the forest, close to roads and access trails. An area of core forest that remained intact over the period of study was identified within the largest forest patch, most of which was located within the boundaries of a forest reserve and included the last remaining patches of closed-broadleaf forest. These areas should be given highest priority for conservation, as they constitute important refuges for endemic or threatened biodiversity. Minimizing and controlling access will be important in maintaining this core.     

Land use patterns and urbanization in the holy city of Varanasi, India: a scenario

Rapid urbanization and increasing land use changes due to population and economic growth in selected landscapes is being witnessed of late in India and other developing countries. The cities are expanding in all directions resulting in large-scale urban sprawl and changes in urban land use. The spatial pattern of such changes is clearly noticed on the urban fringes or city peripheral rural areas than in the city center. In fact, this is reflected in changing urban land use patterns. There is an urgent need to accurately describe land use changes for planning and sustainable management. In the recent times, remote sensing is gaining importance as vital tool in the analysis and integration of spatial data. This study intends to estimate land use pattern in a planned and unplanned urban setup and also to analyze the impact of change in land use pattern in the Varanasi urban environment. The results indicate that the planned urban setup had a higher tree cover to that of unplanned area in the Varanasi City, although a considerable disparity existed within the planned urban setups. The results emphasize the need to critically review concepts of urban planning and give more consideration to the preservation and management of urban tree cover/greenspace.     

Using landsat data to determine land use/land cover changes in Samsun, Turkey

The rapid industrialization and urbanization of an area require quick preparation of actual land use/land cover (LU/LC) maps in order to detect and avoid overuse and damage of the landscape beyond sustainable development limits. Remote sensing technology fits well for long-term monitoring and assessment of such effects. The aim of this study was to analyze LU/LC changes between 1980 and 1999 in Samsun, Turkey, using satellite images. Three Landsat images from 1980, 1987 and 1999 were used to determine changes. A post classification technique was used based on a hybrid classification approach (unsupervised and supervised). Images were classified into six LU/LC types; urban, agriculture, dense forest, open forest-hazelnut, barren land and water area. It is found that significant changes in land cover occurred over the study period. The results showed an increase in urban, open forest/hazelnut, barren land and water area and a decrease in agriculture and dense forest in between 1980 and 1999. In this period, urban land increased from 0.77% to 2.47% of the total area, primarily due to conversions from agricultural land and forest to a lesser degree. While the area of dense forest decreased from 41.09% to 29.64% of the total area, the area of open forest and hazelnut increased from 6.73% to 11.88%.     

Developmental strategies for sustainable ecosystem on mine spoil dumps: a case of study

An important goal of ecological rehabilitation is to accelerate natural successional processes to increase biological productivity, soil fertility and biotic control over biogeochemical fluxes within the recovering ecosystems. A new approach called Microbe Assisted Green Technology (MAGT) is an integrated biotechnological approach developed at National Environmental Engineering Research Institute (NEERI) through exhaustive laboratory as well as field studies and serve as a model for land reclamation and development of lush green vegetation on mine overburdens. One year old seedlings of native tree species were planted on 6.3 ha area of manganese mine overburden at Gumgaon under Manganese Ore India Ltd., Maharashtra, India. Continuous efforts resulted in nutrient rich soil with high N, P, K and organic carbon; well developed biodiversity, including bacteria, fungi, higher plants (more than 350 species) and different classes of animals. Planted trees accumulated 698 t ha( - 1) above ground biomass and 143 t ha( - 1) below ground mass. This was achieved in 18 years by MAGT, which otherwise takes hundreds of years.     

Using Resource Economics to Anticipate Forest Land Use Change in the U.S. Mid-Atlantic Region

Demands for forest, farm, and developed land are evolving in the U.S. mid-Atlantic region. The demand for land in developed uses, as well as demands for various forest and farm products are changing in response to population growth, demographic shifts, and market forces. As demand factors change so do relative land values. Land area in future forest, farm, and developed uses may shift as landowners re-evaluate relative net benefits from land use alternatives. This study examines the effects of various land demand and supply factors on the determination of land use patterns in the mid-Atlantic region. Driving variables include costs and benefits from various uses, population density, and measures of land quality. Model parameters are estimated using a binomial logit procedure. Results from the study are used to estimate proportions of forest area on a county by county basis. Simulated forest landscapes under hypothetical future conditions are prepared and illustrated using geographic information system (GIS) techniques.     

Landscape approach for quantifying land use land cover change (1972–2006) and habitat diversity in a mining area in Central India (Bokaro, Jharkhand)

The rate and intensity of land use land cover (LULC) change has increased considerably during the past couple of decades. Mining brings significant alterations in LULC specifically due to its impact on forests. Parts of Central India are well endowed with both forests and minerals. Here, the conflict between human interests and nature has intensified over time. Monitoring and assessment of such conflicts are important for land management and policy making. Remote sensing and Geographical Information System have the potential to serve as accurate tools for environmental monitoring. Understanding the importance of landscape metrics in land use planning is challenging but important. These metrics calculated at landscape, class, and patch level provide an insight into changing spatiotemporal distribution of LULC and ecological connectedness. In the present study, geospatial tools in conjunction with landscape metrics have been used to assess the impact of coal mining on habitat diversity. LULC maps, change detection analysis, and landscape metrics have been computed for the four time periods (1972, 1992, 2001, and 2006). There has been a significant decline in forest cover especially of the Sal-mixed forests, both in area as well as quality, due to flouted mining regulations. Reclamation of mined lands has also been observed in some of the areas since 2001.     

Multi-objective optimization of air quality monitoring

A new method for multi-objective optimization of air quality monitoring systems based on satellite remote sensing of the troposphere is described in this work. The technique uses atmospheric turbidity as surrogate for air pollution loading. Through inverse chemical modeling and ancillary information the respective patterns of primary gaseous and particle pollutants are inferred. The optimization algorithm uses the resulting maps of ambient air pollution as input. It focuses on the gain of information with regard to human exposure to high pollution, potential impact on cultural heritage, compliance to ambient air quality standards, monitoring key point and area source emissions, as well as on the associated cost. Application of the method in Brescia, Italy showed its significant potential for improving the cost-effectiveness of air quality monitoring networks at the urban and regional scales.     

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.     

Hurricane Katrina-induced forest damage in relation to ecological factors at landscape scale

Forest stand stability to strong winds such as hurricanes has been found to be associated with a number of forest, soil and topography factors. In this study, through applying geographic information system (GIS) and logit regression, we assessed effects of forest characteristics and site conditions on pattern, severity and probability of Hurricane Katrina disturbance to forests in the Lower Pearl River Valley, USA. The factors included forest type, forest coverage, stand density, soil great group, elevation, slope, aspect, and stream buffer zone. Results showed that Hurricane Katrina damaged 60% of the total forested land in the region. The distribution and intensity of the hurricane disturbance varied across the landscape, with the bottomland hardwood forests on river floodplains most severely affected. All these factors had a variety of effects on vulnerability of the forests to the hurricane disturbance and thereby spatial patterns of the disturbance. Soil groups and stand factors including forest types, forest coverage and stand density contributed to 85% of accuracy in modeling the probability of the hurricane disturbance to forests in this region. Besides assessment of Katrina's damage, this study elucidates the great usefulness of remote sensing and GIS techniques combined with statistics modeling in assessment of large-scale risks of hurricane damage to coastal forests.     

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.     

Estimation of Plant Diversity at Landscape Level: A Methodological Approach Applied to Three Spanish Rural Areas

Approaches linking biodiversity assessment with landscape structure are necessary in the framework of sustainable rural development. The present paper describes a methodology to estimate plant diversity involving landscape structure as a proportional weight associated with different plant communities found in the landscape mosaic. The area occupied by a plant community, its patch number or its spatial distribution of patches are variables that could be expressed in gamma plant diversity of a territory. The methodology applies (1) remote sensing information, to identify land cover and land use types; (2) aspect, to discriminate composition of plant communities in each land cover type; (3) multi-scale field techniques, to asses plant diversity; (4) affinity analysis of plant community composition, to validate the stratified random sampling design and (5) the additive model that partitions gamma diversity into its alpha and beta components. The method was applied to three Spanish rural areas and was able to record 150-260 species per ha. Species richness, Shannon information index and Simpson concentration index were used to measure diversity in each area. The estimation using Shannon diversity index and the product of patch number and patch interspersion as weighting of plant community diversity was found to be the most appropriate method of measuring plant diversity at the landscape level.