Landsat Thematic Mapper Scenes Research Articles

Monitoring deforestation and forest degradation using multi-temporal fraction images derived from Landsat sensor data in the Brazilian Amazon

ABSTRACT Deforestation is the replacement of forest by other land use while degradation is a reduction of long-term canopy cover and/or forest stock. Forest degradation in the Brazilian Amazon is mainly due to selective logging of intact/un-managed forests and to uncontrolled fires. The deforestation contribution to carbon emission is already known but determining the contribution of forest degradation remains a challenge. Discrimination of logging from fires, both of which produce different levels of forest damage, is important for the UNFCCC (United Nations Framework Convention on Climate Change) REDD+ (Reducing Emissions from Deforestation and Forest Degradation) program. This work presents a semi-automated procedure for monitoring deforestation and forest degradation in the Brazilian Amazon using fraction images derived from Linear Spectral Mixing Model (LSMM). Part of a Landsat Thematic Mapper (TM) scene (path/row 226/068) covering part of Mato Grosso State in the Brazilian Amazon, was selected to develop the proposed method. First, the approach consisted of mapping deforested areas and mapping forest degraded by fires using image segmentation. Next, degraded areas due to selective logging activities were mapped using a pixel-based classifier. The results showed that the vegetation, soil, and shade fraction images allowed deforested areas to be mapped and monitored and to separate degraded forest areas caused by selective logging and by fires. The comparison of Landsat Operational Land Imager (OLI) and RapidEye results for the year 2013 showed an overall accuracy of 94%. We concluded that spatial resolution plays an important role for mapping selective logging features due to their characteristics. Therefore, when compared to Landsat data, the current availability of higher spatial and temporal resolution data, such as provided by Sentinel-2, is expected to improve the assessment of deforestation and forest degradation, especially caused by selective logging. This will facilitate the implementation of actions for forest protection.

Landscape Mapping: Gaining “Sense of Place” for Conservation in the Manaslu Conservation Area, Nepal

Abstract Satellite mapping contributes significantly to ecological studies of landscape composition and structure but needs to better engage local populations in more culturally sensitive assessments of diversity patterns. This study examines how participatory mapping can contribute to land-cover analyses derived from satellite data of landscapes in the Manaslu Conservation Area (MCA), Nepal. Landscapes were mapped by local residents, first on paper and again on a geo-registered Landsat Thematic Mapper (TM) scene, for April 7, 2010. Local landscape knowledge from men and women was transferred from participatory maps to the classified satellite image to create an integrated landscape map for the MCA. Participatory maps added the significance of “place” to the evaluation of landscape diversity (n = 276 named places for women and 315 for men). The classification of the TM image resulted in seven land cover types with only 10% of the land classified as forests, which are highly fragmented. The final map combi...

Enhancement of the double flexible pace search threshold determination for change vector analysis

Abstract. Remote sensing is one of the most reliable ways to monitor land use and land cover change of large areas. On the other hand, satellite images from different agencies are becoming accessible due to the new user dissemination policies. For that reason, interpretation of remotely sensed data in a spatiotemporal context is becoming a valuable research topic. In the present day, a map of change has a great significant for scientific purposes or planning and management applications. However, it is difficult to extract useful visual information from the large collection of available satellite images. For that reason, automatic or semi-automatic exploration is needed. One of the key stages in the change detection methods is threshold selection. This threshold determination problem has been addressed by several recent techniques based on Change Vector Analysis (CVA). Thus, this work provides a simple semi-automatic procedure that defines the change/no change condition and a comparative study will be involved together with the previous existing method called Double Flexible Pace Search (DFPS). This study uses Landsat Thematic Mapper scenes acquired on different dates in an Algerian region. First, some training data sets containing all possible classes of change are required and their respective supervised posterior probability maps for each scene are obtained. The selected supervised classifier is based on the Maximum Likelihood method. Then four training sets (two sets from each date) are chosen from their corresponding probability maps based on their spatial location in the original images. The optimal average will be obtained as an average of the thresholds obtained at every set. This work verifies that the proposed approach is effective on the selected area, providing improved change map results.

The spectral changes of deforestation in the Brazilian tropical savanna.

The Cerrado is a biome in Brazil that is experiencing the most rapid loss in natural vegetation. The objective of this study was to analyze the changes in the spectral response in the red, near infrared (NIR), middle infrared (MIR), and normalized difference vegetation index (NDVI) when native vegetation in the Cerrado is deforested. The test sites were regions of the Cerrado located in the states of Bahia, Minas Gerais, and Mato Grosso. For each region, a pair of Landsat Thematic Mapper (TM) scenes from 2008 (before deforestation) and 2009 (after deforestation) was compared. A set of 1,380 samples of deforested polygons and an equal number of samples of native vegetation have their spectral properties statistically analyzed. The accuracy of deforestation detections was also evaluated using high spatial resolution imagery. Results showed that the spectral data of deforested areas and their corresponding native vegetation were statistically different. The red band showed the highest difference between the reflectance data from deforested areas and native vegetation, while the NIR band showed the lowest difference. A consistent pattern of spectral change when native vegetation in the Cerrado is deforested was identified regardless of the location in the biome. The overall accuracy of deforestation detections was 97.75%. Considering both the marked pattern of spectral changes and the high deforestation detection accuracy, this study suggests that deforestation in Cerrado can be accurately monitored, but a strong seasonal and spatial variability of spectral changes might be expected.

Monitoring land changes in an urban area using satellite imagery, GIS and landscape metrics

Monitoring land changes is an important activity in landscape planning and resource management. In this study, we analyze urban land changes in Atlanta metropolitan area through the combined use of satellite imagery, geographic information systems (GIS), and landscape metrics. The study site is a fast-growing large metropolis in the United States, which contains a mosaic of complex landscape types. Our method consisted of two major components: remote sensing-based land classification and GIS-based land change analysis. Specifically, we adopted a stratified image classification strategy combined with a GIS-based spatial reclassification procedure to map land classes from Landsat Thematic Mapper (TM) scenes acquired in two different years. Then, we analyzed the spatial variation and expansion of urban land changes across the entire metropolitan area through post classification change detection and a variety of GIS-based operations. We further examined the size, pattern, and nature of land changes using landscape metrics to examine the size, pattern, and nature of land changes. This study has demonstrated the usefulness of integrating remote sensing with GIS and landscape metrics in land change analysis that allows the characterization of spatial patterns and helps reveal the underlying processes of urban land changes. Our results indicate a transition of urbanization patterns in the study site with a limited outward expansion despite the dominant suburbanization process.

Using atmospherically-corrected Landsat imagery to measure glacier area change in the Cordillera Blanca, Peru from 1987 to 2010

The dynamic, tropical glaciers of the Peruvian Cordillera Blanca are rapidly changing and these changes are expected to affect water availability, especially during the dry season. In this study, we quantify recent changes to these water reservoirs, providing estimates of glacier area in the Cordillera Blanca and sub-watersheds of the Rio Santa for the following years 1987, 1996, 2004, and 2010. We explore the effects of atmospheric and topographic corrections by comparing debris-free glacier area estimates generated using raw scenes and corrected scenes. Our results suggest that these corrections can have a significant impact on debris-free glacier area estimates when the same threshold is applied. Debris-free glacier area estimates derived from uncorrected scenes are approximately 5% less than debris-free glacier area estimates derived from atmospherically-corrected scenes. We determined that debris-free glacier area estimates are most sensitive to the choice of threshold and topographic effects. To map glacier area change, we used high-resolution satellite imagery to calibrate our selection of a single threshold for the Normalized Difference Snow Index (NDSI). This threshold value was applied to all NDSI images, which were derived from four carefully selected and atmospherically-corrected Landsat Thematic Mapper (TM) scenes acquired at the end of the dry season. In order to calculate total glacier area, we manually mapped debris-covered glaciers, because automated methods were unsuccessful in this region. As of August 2010, the Cordillera Blanca had a total glacier area of 482km2, which amounts to a 25% decrease since 1987. Glaciers in the southern portions of the Cordillera Blanca, which have lower median elevations on average, lost a greater percentage of their area from 1987 to 2010, relative to their northern counterparts. Overall, glacier area change in the Cordillera Blanca appears to be accelerating. Between 2004 and 2010, glaciers in the Cordillera Blanca lost area at a rate that was approximately 3.5 times the average rate of area loss from 1970 to 2003.

Integration of urban growth modelling products with image-based urban change analysis

Urban change detection using remotely sensed data has been extensively studied. One current application of detection products is the formulation of calibration data for urban change prediction models. As multi-temporal scenes become available and urban growth prediction models increase in popularity and accuracy, it is natural to envisage a bi-directional relationship where, in addition to detection products assisting prediction models, the prediction information acts as ancillary input to enhance spectral-based change detection products. This closed feedback loop has the potential to significantly increase the accuracy of both detection and prediction efforts. Consequently, our objective is to evaluate the integration of prediction information with spectral data for urban change monitoring. A case study was carried out in the Denver, Colorado metropolitan area. Probabilities of urban change generated from two existing urban prediction models (based on decision trees and logistic regression) are combined as additional information content with a Landsat Thematic Mapper (TM) scene. Detailed assessments at the pixel and block scales are implemented to evaluate urban change classification accuracy using different input data and training sample sizes. Results show that in pixel-based assessments, the fusion of decision tree change probabilities and TM spectral bands with sufficient training samples leads to improvements. In terms of overall accuracies, the improvement is 2.0–2.4%, from 87.3% (spectral-only model) and 87.7% (prediction probability model) to 89.7% for the fused model. Similarly, the corresponding kappa coefficients show increases of 0.07–0.08, from 0.60 for the spectral model and 0.61 for the urban prediction model to 0.68 for the fused model. Accuracies aggregated at the block scale present an approximate 2.1–4.3% increase when the fusion-based model is employed compared with the exclusive use of either spectral or prediction probability data, namely 87.6% (fused) vs 83.4% (spectral) and 85.7% (prediction). It is also important to state that the standard deviation of accuracies between blocks is significantly reduced by more than 3% (11.5% vs 14.9% and 14.7%), suggesting higher consistency in classification performance. This is a desirable attribute for subsequent use of these products, for example by the urban planning community. Statistical tests at the block scale also demonstrate that such improvements are significant. It is also observed that to receive the integration benefits, the remote sensing classifier needs a large but reasonable training data set size, while the prediction model should be based on advanced modelling methods. Further assessments on block accuracy with respect to urbanization conditions (i.e. urban presence and change sizes) indicate the ability of the fusion to address spectral limitations, especially in blocks with high relative change. These initial results encourage the expansion of spectral/prediction data fusion to other sites, modelling techniques, and input data.

A MAXIMUM ENTROPY MODEL OF THE BEARDED CAPUCHIN MONKEY HABITAT INCORPORATING TOPOGRAPHY AND SPECTRAL UNMIXING ANALYSIS

Abstract. Movement patterns of bearded capuchin monkeys (Cebus (Sapajus) libidinosus) in northeastern Brazil are likely impacted by environmental features such as elevation, vegetation density, or vegetation type. Habitat preferences of these monkeys provide insights regarding the impact of environmental features on species ecology and the degree to which they incorporate these features in movement decisions. In order to evaluate environmental features influencing movement patterns and predict areas suitable for movement, we employed a maximum entropy modelling approach, using observation points along capuchin monkey daily routes as species presence points. We combined these presence points with spatial data on important environmental features from remotely sensed data on land cover and topography. A spectral mixing analysis procedure was used to generate fraction images that represent green vegetation, shade and soil of the study area. A Landsat Thematic Mapper scene of the area of study was geometrically and atmospherically corrected and used as input in a Minimum Noise Fraction (MNF) procedure and a linear spectral unmixing approach was used to generate the fraction images. These fraction images and elevation were the environmental layer inputs for our logistic MaxEnt model of capuchin movement. Our models' predictive power (test AUC) was 0.775. Areas of high elevation (>450 m) showed low probabilities of presence, and percent green vegetation was the greatest overall contributor to model AUC. This work has implications for predicting daily movement patterns of capuchins in our field site, as suitability values from our model may relate to habitat preference and facility of movement.

Assessing post-fire vegetation recovery using red–near infrared vegetation indices: Accounting for background and vegetation variability

Post-fire vegetation cover is a crucial parameter in rangeland management. This study aims to assess the post-fire vegetation recovery 3years after the large fires on the Peloponnese peninsula in southern Greece. In this context, 13 red–near infrared (R–NIR) vegetation indices (VIs) were evaluated. Some of these indices, the so called Soil-Adjusted VIs (SAVIs), attempt to minimize the influence of background variability, however, so far the impact of the variability in spectral response between different vegetation species on index performance has not yet been rigorously assessed. Using a combination of field and simulation techniques this study accounts for the impact of both background and vegetation variability on index performance. The field data included a spectral library (59 vegetation and 29 substrate signals) and 78 line transect plots. One Landsat Thematic Mapper (TM) scene of July 2010, 3years after the fire event, was employed in the study. Results based on simulated mixtures of in situ measured reflectance showed that (i) SAVIs outperformed the Normalized Difference Vegetation Index (NDVI) in environments with a single vegetation type, (ii) the NDVI more accurately estimated vegetation cover in environments with heterogeneous vegetation layers and a single soil type and (iii) overall, when both vegetation and background variability is incorporated in the model, the NDVI was the most optimal index. Findings from the simulation experiment corroborated with the results from the Landsat application. The Landsat NDVI showed the highest correlation with the line transect field data of recovery (R2=0.68) and the rank in performance of the Landsat-based indices was similar to that of the simulation experiment in which both vegetation and substrate variability was introduced. Results depend on the initial variability present in the study area, however, some trends can be generalized. Firstly, results support the use of SAVIs in environments with a single vegetation type. Secondly, for applications in environments to which natural vegetation variability is inherent, such as the post-fire recovery landscape of this study, we, however, recommend the use of the NDVI because its normalizing capacity minimizes the impact of vegetation variability on fractional cover estimates.

WATER BALANCE OF GLACIERIZED CATCHMENTS IN TROPICS: A CASE STUDY IN BOLIVIAN ANDES

On outer-tropical glaciers, mostly in Peru and Bolivia, ablation and accumulation occur all year round whereas maximum in wet seasons. This study investigated glacier mass balance and catchment scale water balance of Glacier Tuni and Huayna Potosi, which locate at the west side of Bolivian Andes. Remote sensing and hydrological methods have been applied to conduct glacier mass balances monthly and annually in recent years. With remote sensing method, glacier volume change has been derived based on glacier area change obtained utilizing cloud free Landsat Thematic Mapper (TM) scenes. Results of commonly negative glacier mass balance have revealed that glaciers in this region are retreating. Despite of interests on glaciological aspects, water balances of glacierized catchments have been carried out. Except for in a few months glaciers got positive mass balance, glaciers continuously contributed to discharge.

Deforestation in the Miombo woodlands: a pixel-based semi-automated change detection method

Most methods of change detection require a considerable amount of effort and expertise. The procedures of change detection are visual-, classification-, object- or vector-based. The target of this research was to develop an automated and generally unsupervised combination of methods to quantify deforestation on a per pixel basis. The study area was the Gutu district in Zimbabwe. In the first step, Landsat Thematic Mapper (TM) scenes were spectrally unmixed by the Spectral Mixture Analysis (SMA). The calculation of the necessary endmembers was performed by means of the N-FINDR algorithm. After the unmixing process, the data were analysed with change vector analysis (CVA) utilizing spherical statistics. Thereafter, a combination of constraints, including a Bayesian threshold and spherical angles, was applied to identify deforestation. The combination of these methods provided an accurate idea of the state of deforestation and enabled attribution to ‘fire-induced’ and ‘non fire-induced’ classes.

Spectral mixture analysis to assess post-fire vegetation regeneration using Landsat Thematic Mapper imagery: Accounting for soil brightness variation

Post-fire vegetation cover is a crucial parameter in rangeland management. This study aims to assess the post-fire vegetation recovery 3 years after the large 2007 Peloponnese (Greece) wildfires. Post-fire recovery landscapes typically are mixed vegetation–substrate environments which makes spectral mixture analysis (SMA) a very effective tool to derive fractional vegetation cover maps. Using a combination of field and simulation techniques this study aimed to account for the impact of background brightness variability on SMA model performance. The field data consisted out of a spectral library of in situ measured reflectance signals of vegetation and substrate and 78 line transect plots. In addition, a Landsat Thematic Mapper (TM) scene was employed in the study. A simple SMA, in which each constituting terrain feature is represented by its mean spectral signature, a multiple endmember SMA (MESMA) and a segmented SMA, which accounts for soil brightness variations by forcing the substrate endmember choice based on ancillary data (lithological map), were applied. In the study area two main spectrally different lithological units were present: relatively bright limestone and relatively dark flysch (sand-siltstone). Although the simple SMA model resulted in reasonable regression fits for the flysch and limestones subsets separately (coefficient of determination R2 of respectively 0.67 and 0.72 between field and TM data), the performance of the regression model on the pooled dataset was considerably weaker (R2=0.65). Moreover, the regression lines significantly diverged among the different subsets leading to systematic over-or underestimations of the vegetative fraction depending on the substrate type. MESMA did not solve the endmember variability issue. The MESMA model did not manage to select the proper substrate spectrum on a reliable basis due to the lack of shape differences between the flysch and limestone spectra,. The segmented SMA model which accounts for soil brightness variations minimized the variability problems. Compared to the simple SMA and MESMA models, the segmented SMA resulted in a higher overall correlation (R2=0.70), its regression slope and intercept were more similar among the different substrate types and its resulting regression lines more closely resembled the expected one-one line. This paper demonstrates the improvement of a segmented approach in accounting for soil brightness variations in estimating vegetative cover using SMA. However, further research is required to evaluate the model's performance for other soil types, with other image data and at different post-fire timings.

Crop classification in rain-fed and irrigated agricultural areas using Landsat TM and ALOS/PALSAR data

Multispectral optical remote-sensing systems have been the base for crop identification and monitoring for many years. However, cloud cover limits and even prevents the use of optical data for this activity. Synthetic aperture radar (SAR) sensors provide an interesting alternative to conventional multispectral classification schemes. Radar sensors can acquire data regardless of cloud cover, and their observations provide complementary information to that captured by optical sensors. The main objective of this paper is to evaluate whether the incorporation of polarimetric SAR observations to a multispectral classification scheme enhances classification results using the proposed method. With this aim, one Landsat thematic mapper scene and two ALOS/PALSAR quadrature polarization scenes acquired in 2007 were processed. The results demonstrated the ability of SAR data to improve the classification based on optical images. However, improvements were slight (an increase of around 2% in the classification's overall accuracy). The results improved significantly when barley, wheat, and oats were considered a single class, called cereals. The best results were achieved using Landsat and the two ALOS/PALSAR scenes together, obtaining an overall Kappa coefficient and accuracy of 0.67 and 90%, respectively. Probably, scenes acquired on other dates (June and July) would have yielded clearer classification enhancements.

A new glacier inventory for the European Alps from Landsat TM scenes of 2003: challenges and results

AbstractMeltwater from glaciers in the European Alps plays an important role in hydropower production, and future glacier development is thus of economic interest. However, an up-to-date and alpine-wide inventory for accurate assessment of glacier changes or modelling of future glacier development has not hitherto been available. Here we present a new alpine-wide inventory (covering Austria, France, Italy and Switzerland) derived from ten Landsat Thematic Mapper (TM) scenes acquired within 7 weeks in 2003. Combined with the globally available digital elevation model from the Shuttle Radar Topography Mission, topographic inventory parameters were derived for each of the 3770 mapped glaciers, covering 2050 km2. The area-class frequency distribution is very similar in all countries, and a mean northerly aspect (NW, N, NE) is clearly favoured (arithmetic counting). Mean glacier elevation is ~2900 m, with a small dependence on aspect. The total area loss since the previous glacier inventory (acquired around 1970±15 years) is roughly one-third, yielding a current area loss rate of ~2%a–1. Digital overlay of the outlines from the latest Austrian glacier inventory revealed differences in the interpretation of glacier extents that prohibit change assessment. A comparison of TM-derived outlines with manually digitized extents on a high-resolution IKONOS image returned 1.5% smaller glaciers with TM.

A new glacier inventory for the Jostedalsbreen region, Norway, from Landsat TM scenes of 2006 and changes since 1966

AbstractPronounced changes in glacier mass and length were observed for the monitored glaciers in the Jostedalsbreen region, Norway, since the last glacier inventories were compiled in the 1960s and 1980s. However, the current overall extent of the glaciers in the region is not well known. To obtain this information, we have compiled a new inventory from two mosaicked Landsat Thematic Mapper (TM) scenes acquired in 2006 that have excellent snow conditions for glacier mapping, the first suitable scenes for this purpose after 22 years of imaging with TM. Drainage divides and topographic inventory parameters were derived from a 25 m national digital elevation model for 1450 glaciers. By digitizing glacier outlines from 1 : 50 000 scale topographic maps of 1966, we calculated changes in glacier area for ~300 glaciers. Cumulative length changes for the 1997–2006 period were derived from an additional TM scene and compared with field measurements for nine glaciers. Overall, we find a 9% area loss since 1966, with a clear dependence on glacier size; however, seasonal snow in 1966 in some regions made area determination challenging. The satellite-derived length changes confirmed the observed high spatial variability and were in good agreement with field data (±1 pixel), apart from glacier tongues in cast shadow. The new inventory will be freely available from the Global Land Ice Measurements from Space (GLIMS) glacier database.

A new satellite-derived glacier inventory for western Alaska

AbstractGlacier inventories provide the baseline data to perform climate-change impact assessment on a regional scale in a consistent and spatially representative manner. In particular, a more accurate calculation of the current and future contribution to global sea-level rise from heavily glacierized regions such as Alaska is much needed. We present a new glacier inventory for a large part of western Alaska (including Kenai Peninsula and the Tordrillo, Chigmit and Chugach mountains), derived from nine Landsat Thematic Mapper scenes acquired between 2005 and 2009 using well-established automated glacier-mapping techniques (band ratio). Because many glaciers are covered by optically thick debris or volcanic ash and partly calve intowater, outlineswere manually edited in these wrongly classified regions during post-processing. In total we mapped ~8830 glaciers (>0.02 km2) with a total area of ~16 250 km2. Large parts of the area (47%) are covered by a few (31) large (>100 km2) glaciers, while glaciers less than 1 km2 constitute only 7.5% of the total area but 86% of the total number. We found a strong dependence of mean glacier elevation on distance from the ocean and only a weak one on aspect. Glacier area changes were calculated for a subset of 347 selected glaciers by comparison with the Digital Line Graph outlines from the US Geological Survey. The overall shrinkage was ~23% between 1948–57 and 2005–09.

Utilizing image texture to detect land-cover change in Mediterranean coastal wetlands

Land-use/cover change dynamics were investigated in a Mediterranean coastal wetland. Change Vector Analysis (CVA) without and with image texture derived from the co-occurrence matrix and variogram were evaluated for detecting land-use/cover change. Three Landsat Thematic Mapper (TM) scenes recorded on July 1985, 1993 and 2005 were used, minimizing change detection error caused by seasonal differences. Images were geometrically, atmospherically and radiometrically corrected. CVA without and with texture measures were implemented and assessed using reference images generated by object-based supervised classification. These outputs were used for cross-classification to determine the ‘from–to’ change used to compare between techniques. The Landsat TM image bands together with the variogram yielded the most accurate change detection results, with Kappa statistics of 0.7619 and 0.7637 for the 1985–1993 and 1993–2005 image pairs, respectively.

Landsat-based inventory of glaciers in western Canada, 1985–2005

We report on a glacier inventory for the Canadian Cordillera south of 60°N, across the two western provinces of British Columbia and Alberta, containing ~ 30,000 km 2 of glacierized terrain. Our semi-automated method extracted glacier extents from Landsat Thematic Mapper (TM) scenes for 2005 and 2000 using a band ratio (TM3/TM5). We compared these extents with glacier cover for the mid-1980s from high-altitude, aerial photography for British Columbia and from Landsat TM imagery for Alberta. A 25 m digital elevation model (DEM) helped to identify debris-covered ice and to split the glaciers into their respective drainage basins. The estimated mapping errors are 3–4% and arise primarily from seasonal snow cover. Glaciers in British Columbia and Alberta respectively lost − 10.8 ± 3.8% and − 25.4% ± 4.1% of their area over the period 1985–2005. The region-wide annual shrinkage rate of − 0.55% a − 1 is comparable to rates reported for other mountain ranges in the late twentieth century. Least glacierized mountain ranges with smaller glaciers lost the largest fraction of ice cover: the highest relative ice loss in British Columbia (− 24.0 ± 4.6%) occurred in the northern Interior Ranges, while glaciers in the northern Coast Mountains declined least (− 7.7 ± 3.4%).

Monitoring forest changes in the southwestern United States using multitemporal Landsat data

Landsat time series data sets were acquired for the Santa Fe National Forest in New Mexico. This area includes the San Pedro Parks Wilderness area, which was designated as an official wilderness in 1964. Eight autumnal Landsat Thematic Mapper (TM) scenes acquired from 1988 to 2006 were analyzed to determine whether significant changes have occurred throughout the region during the past 18 years and, if so, to assess whether the changes are long-term and gradual or short-term and abrupt. It was found that, starting in about 1995, many of the conifer stands within the Wilderness area showed consistently gradual and marked increases in the Shortwave Infrared/Near Infrared Index. These trends generally imply decreases in canopy greenness or increases in mortality. Other high-elevation conifer forests located outside of the Wilderness area showed similar spectral trends, indicating that changes are potentially widespread. The spatial patterns of forest damage as inferred from the image analyses were very similar to the general patterns of insect defoliation damage mapped via aerial sketch mapping by the United States Department of Agriculture Forest Service Forest Health Monitoring Program. A field visit indicated that zones of spectral change are associated with high levels of forest damage and mortality, likely caused by a combination of insects and drought. The study demonstrates the effectiveness of using historical Landsat data for providing objective and consistent long-term assessments of the gradual ecosystem changes that are occurring within the western United States.

Assessing rates of forest change and fragmentation in Alabama, USA, using the vegetation change tracker model

Forest change is of great concern for land use decision makers and conservation communities. Quantitative and spatial forest change information is critical for addressing many pressing issues, including global climate change, carbon budgets, and sustainability. In this study, our analysis focuses on the differences in geospatial patterns and their changes between federal forests and nonfederal forests in Alabama over the time period 1987–2005, by interpreting 163 Landsat Thematic Mapper (TM) scenes using a vegetation change tracker (VCT) model. Our analysis revealed that for the most part of 1990 s and between 2000 and 2005, Alabama lost about 2% of its forest on an annual basis due to disturbances, but much of the losses were balanced by forest regeneration from previous disturbances. The disturbance maps revealed that federal forests were reasonably well protected, with the fragmentation remaining relatively stable over time. In contrast, nonfederal forests, which are predominant in area share (about 95%), were heavily disturbed, clearly demonstrating decreasing levels of fragmentation during the time period 1987–1993 giving way to a subsequent accelerating fragmentation during the time period 1994–2005. Additionally, the identification of the statistical relationships between forest fragmentation status and forest loss rate and forest net change rate in relation to land ownership implied the distinct differences in forest cutting rate and cutting patterns between federal forests and nonfederal forests. The forest spatial change information derived from the model has provided valuable insights regarding regional forest management practices and disturbance regimes, which are closely associated with regional economics and environmental concerns.