Thematic Mapper characterization of lodgepole pine seral stages in Yellowstone National Park, USA

doi:10.1016/0034-4257(95)00228-6

Remote Sensing of Environment

Volume 56, Issue 2, May 1996, Pages 118-132

https://doi.org/10.1016/0034-4257(95)00228-6 Get rights and content

Abstract

Landsat Thematic Mapper multispectral data were used to identify the spectral reflectance characteristics of Yellowstone lodgepole pine (Pinus contorta var latifolia) successional stages, and to examine the relationships between spectral and biophysical factors. Ten spectrally defined forest cover types were created from unsupervised classification of the Landsat TM data, using a geographic information system to restrict data analysis to areas of similar slope, elevation, and surficial geology within the Central Plateau region of the park. Biotic data on forest overstory and understory conditions were collected from 69 sample sites within the 10 spectral cover classes. Field data were used to regroup the 69 sites into six biotically and spectrally distinct cover types, ranging from early postfire regeneration (LPO) to late-stage (LP3) subalpine fir succession. Increased absorption in the visible (TM 1, 2, and 3) and middle-infrared (TM 5 and 7) bands were related to the age and development of a stand. Changes in absorption were rapid during the initial stages of stand regeneration, but the rate of change slowed as stands progressed into later successional stages. Biotic factors relating to the physical structure of the forest canopy (height, basal area, biomass, and LAI) are correlated with the visible and middle-infrared bands of the Thematic Mapper. Understory factors were poorly correlated with spectral response, except soil and fireweed, which are dominant early in succession, but rapidly decrease in later stages. The spectral reflectance of a successional forest stand over time is a function of the combined effects of the overstory canopy, the amount of shadow within a canopy, and the condition of the forest understory. As a forest develops from a disturbance to old-growth, the spectral response of a stand progresses along a vector or vectors linking the three factors. Spectral response changes are nonlinear with respect to time, as large-magnitude changes are observed in the first 20–30 years following a disturbance, and the rate of change lessens as forests develop into old-growth.

References (50)

P.S. Chavez
An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data
Remote Sens. Environ.
(1988)
W.B. Cohen et al.
Estimating structural attributes of Douglas fir/western hemlock forest stands from Landsat and SPOT imagery
Remote Sens. Environ.
(1992)
W.B. Cohen et al.
Semivariograms of digital imagery for analysis of conifer canopy structure
Remote Sens. Environ.
(1990)
K.P. Price
Detection of soil erosion within pinyon-juniper woodlands using Thematic Mapper (TM) data
Remote Sens. Environ.
(1993)
D.B. Botkin et al.
Role of vegetation in the biosphere
W.B. Cohen et al.
Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, U.S.A.
Int. J. Remote Sens.
(1995)
R. Daubenmire
A canopy-coverage method of vegetational analysis
Northwest Sci.
(1959)
D.G. Despain
Major vegetation zones of Yellowstone National Park
D.G. Despain
Forest successional stages in Yellowstone National Park
D.G. Despain
Nonpyrogeneous climax lodgepole pine communities in Yellowstone National Park
Ecology
(1983)

D.G. Despain

Yellowstone Vegetation: Consequences of Environment and History in a Natural Setting

R.E. Dickenson

Water and energy exchange

H.L. Gholz et al.

Equations for estimating biomass and leaf area of plants in the Pacific Northwest

S.N. Goward

Experiences and perspective in compiling long-term remote sensing data sets on landscapes and biospheric processes

GeoJournal

(1990)

R.D. Graetz

Remote sensing of terrestrial ecosystem structure: An ecologist's pragmatic view

F.G. Hall et al.

Landscape patterns and successional dynamics in the boreal forest

F.G. Hall et al.

Linking knowledge among spatial and temporal scales: vegetation, atmosphere, climate, and remote sensing

Landscape Ecol.

(1988)

R.J. Hobbs et al.

Concluding remarks

D.N.H. Horler et al.

Forestry information content of Thematic Mapper data

Int. J. Remote Sens.

(1986)

M.E. Jakubauskas et al.

The Successional Tasseled Cap: a graphic description of the spectral-temporal development of a coniferous forest

J.R. Jensen

Biophysical remote sensing

Ann. Assoc. Am. Geogr.

(1983)

M.R. Kaufmann et al.

Leaf area determinations for subalpine tree species in the central Rocky Mountains

D.H. Knight et al.

The Yellowstone fires: issues in landscape ecology

BioScience

(1989)

D. Leckie

Advances in remote sensing technologies for forest surveys and management

Can. J. For. Res.

(1990)

B.L. Markham et al.

Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures

EOSAT Landsat Technical Notes No. 1

(1986)

Cited by (72)

Estimating Mediterranean forest parameters using multi seasonal Landsat 8 OLI imagery and an ensemble learning method
2017, Remote Sensing of Environment
Citation Excerpt :
The results obtained can be used to guide the selection of suitable Landsat 8 OLI bands for estimating relevant forest structural parameters in a regional setting approach. However, since several previous studies have emphasized the effect of ecological conditions, canopy senescence, canopy closure, understory vegetation, and soil background on the observed relationship between reflectance data and forest variables (De La Cueva, 2008; Jakubauskas, 1996), further research is required to determine the particular environmental conditions that affect the phenology of the forest, thereby altering the spectral response. This study assessed the utility of multi-seasonal time-series Landsat-8 OLI satellite imagery for estimating forest stand parameters in a Mediterranean mixed forest, using the RF regression algorithm.
The overall aim of this study was to evaluate the use of seasonal time-series Landsat 8 Operational Land Imager (OLI) satellite imagery in estimating forest stand parameters in a heterogeneous Mediterranean environment. Within this framework, the random forest regression algorithm was used to model the relationship between spectral information and tree density, basal area, and wood volume, based on single-date, single-season (dry, wet), and multi-temporal (May–December) imagery. The variable importance (VIMP) measure and the minimal depth (MD) order statistic were also investigated with regard to improved prediction accuracy and the identification of relevant variables. In general, the multi-temporal and dry-season models were more accurate than the single-date models. The models resulting from the MD variable selection from the dry season imagery were the most accurate with a coefficient of determination of up to 0.54 for tree density, 0.72 for basal area, and 0.68 for volume.
Evaluation of desert management and rehabilitation by petroleum mulch base on temporal spectral analysis and field study (case study: Ahvaz, Iran)
2012, Ecological Engineering
Citation Excerpt :
A few methods such as the dark object subtraction (DOS), also known as the histogram minimum method (Cheng and Lei, 2001) and the relative radiometric normalization method (Song et al., 2001) have been applied for the correction of atmospheric effect. In this research, Dark Object Subtraction (DOS) was used as an approach to atmospheric correction, which is perhaps the simplest, yet most widely used image-based absolute atmospheric correction approach for classification and change detection applications (Jakubauskas, 1996; Huguenin et al., 1997). This approach assumes the existence of dark objects (zero or small surface reflectance) throughout a Landsat TM scene and a horizontally homogeneous atmosphere.
In past decades, Iranian desert areas have undergone widespread land use transformation. Sand dune stabilizing projects and anti-desertification programs were initiated in 1965 and now cover about one million hectares of degraded land in Iran. This project aimed to stabilize sand dunes, to induce afforestation, to establish windbreaks and to apply mulch. Currently, more than three million hectares of arid land has been reclaimed across the country. In this study, maximum likelihood supervised classification and post-classification change detection techniques were applied to Landsat and TM/ETM images acquired in 1991 and 2002, respectively to map the anti-desertification process in southwestern Iran. A supervised classification was carried out for each of the two images. The results of this classification were refined using auxiliary data, visual interpretation and expert knowledge of the area. A post-classification change detection technique was used to produce change image through a cross module. Changes were assessed according to different classes. Observations made during the course of this study demonstrate that an obvious, positive change has taken place as a result of the petroleum mulch project. Results show that sand dune areas decreased from 66.4% to 56%, and in this case afforestation areas increased from 2.1% to 7.3%. However afforestation increased soil organic matter and recuperation of soil moisture storage conditions and chemical reactions such as dissolution of minerals and established more suitable conditions for crop growth.
Using Landsat-derived disturbance history (1972-2010) to predict current forest structure
2012, Remote Sensing of Environment
Lidar is currently the most accurate method for remote estimation of forest structure, but it has limited spatial and temporal coverage. Conversely, Landsat data are more widely available, but exhibit a weaker relationship with structure under medium to high leaf area conditions. One potentially valuable means of enhancing the relationship between Landsat reflectance and forest structure is to incorporate Landsat spectral trends prior to a date of interest. Because the condition of a forest stand at any point in time is linked to the stand's disturbance history, an approach that directly leverages the temporal information of Landsat time series should improve estimates of forest structure. The main objective of this study was to test and demonstrate the utility of disturbance and recovery metrics derived from spectral profiles of annual Landsat time series (LTS) to predict current forest structure attributes (as compared to more traditional approaches, including airborne, discrete return lidar and single-date Landsat). We estimated aboveground live biomass (AGB_live), dead woody biomass (AGB_dead), basal area (live and dead), and Lorey's mean stand height for a mixed-conifer forest in eastern Oregon, USA, and compared the results with estimates from lidar and single, current-date Landsat imagery. Annual time-series stacks for the entire Landsat record (1972–2010) were obtained to characterize all long-term (insect, growth) and short-term (fire, harvest) vegetation changes that occurred during that period. This required the additional objective of integrating Landsat data from MSS and TM/ETM + sensors, and we describe here our approach. To extract spectral trajectories and change metrics associated with forest disturbances and recovery we applied a temporal segmentation to the calibrated time series.
Lidar predicted forest structure of live trees most accurately (e.g. AGB_live: R² = 0.88, RMSE = 35.3 Mg ha^− 1). However, LTS metrics significantly improved model predictions (e.g. AGB_live: R² = 0.80, RMSE = 46.9 Mg ha^− 1) compared to single-date Landsat data (AGB_live, R² = 0.58, RMSE = 65.1 Mg ha^− 1). Conversely, distributions of AGB_dead were more strongly associated with disturbance history than current structure of live trees. As a result, LTS models performed significantly better in estimating AGB_dead (R² = 0.73, RMSE = 31.0 Mg ha^− 1), than lidar models (R² = 0.21, RMSE = 43.8 Mg ha^− 1); and single-date Landsat data failed completely (R² = 0, RMSE = 47.8 Mg ha^− 1). Further, LTS metrics that integrated disturbance and recovery history over the entire time series generally predicted AGB_dead better than metrics describing single events only (e.g. the greatest disturbance). This study demonstrates the unique value of the long, historic Landsat record, and suggests new potentials for mapping current forest structure with Landsat.
Using Landsat imagery to map forest change in southwest China in response to the national logging ban and ecotourism development
2012, Remote Sensing of Environment
Citation Excerpt :
Remote sensing is a crucial tool for forest cover change mapping and monitoring. Mapping old-growth forest distribution (Congalton et al., 1993) and post-disturbance forest succession (Cohen et al., 1995; Fiorella and Ripple, 1993; Jakubauskas, 1996) have long been recognized as essential components of forest biodiversity assessment, and in recent years, multiple forest classes have been mapped even in extremely complex and little-studied environments (Helmer et al., 2000; Liu et al., 2002; Schmook et al., 2011). However, detailed forest type classifications are usually performed for only a single time period, because there are formidable challenges associated with mapping change for multiple classes over several time-steps.
Forest cover change is one of the most important land cover change processes globally, and old-growth forests continue to disappear despite many efforts to protect them. At the same time, many countries are on a trajectory of increasing forest cover, and secondary, plantation, and scrub forests are a growing proportion of global forest cover. Remote sensing is a crucial tool for understanding how forests change in response to forest protection strategies and economic development, but most forest monitoring with satellite imagery does not distinguish old-growth forest from other forest types. Our goal was to measure changes in forest types, and especially old-growth forests, in the biodiversity hotspot of northwest Yunnan in southwest China. Northwest Yunnan is one of the poorest regions in China, and since the 1990s, the Chinese government has legislated strong forest protection and fostered the growth of ecotourism-based economic development. We used Landsat TM/ETM+ and MSS images, Support Vector Machines, and a multi-temporal composite classification technique to analyze change in forest types and the loss of old-growth forest in three distinct periods of forestry policy and ecotourism development from 1974 to 2009. Our analysis showed that logging rates decreased substantially from 1974 to 2009, and the proportion of forest cover increased from 62% in 1990 to 64% in 2009. However, clearing of high-diversity old-growth forest accelerated, from approximately 1100 hectares/year before the logging ban (1990 to 1999), to 1550 hectares/year after the logging ban (1999 to 2009). Paradoxically, old-growth forest clearing accelerated most rapidly where ecotourism was most prominent. Despite increasing overall forest cover, the proportion of old-growth forests declined from 26% in 1990, to 20% in 2009. The majority of forests cleared from 1974 to 1990 returned to either a non-forested land cover type (14%) or non-pine scrub forest (66%) in 2009, and our results suggest that most non-pine scrub forest was not on a successional trajectory towards high-diversity forest stands. That means that despite increasing forest cover, biodiversity likely continues to decline, a trend obscured by simple forest versus non-forest accounting. It also means that rapid development may pose inherent risks to biodiversity, since our study area arguably represents a “best-case scenario” for balancing development with maintenance of biodiversity, given strong forest protection policies and an emphasis on ecotourism development.
Snow-covered Landsat time series stacks improve automated disturbance mapping accuracy in forested landscapes
2011, Remote Sensing of Environment
Citation Excerpt :
Fortunately, the recent accomplishments and future interests of remote sensing scientists and ecologists appear to be rapidly converging (Rich et al., 2010), and the remote sensing community is poised to provide an abundance of invaluable data at landscape scales to fulfill the expanding needs of ecologists (Huang et al., 2010a; Kennedy et al., 2010). Passive satellite remote sensing platforms in the visible and near infrared (VNIR) and shortwave infrared (SWIR) ranges of the electromagnetic spectrum are well-suited for these purposes because of their typically dense temporal coverage spanning the last several decades, moderate spatial resolutions, and comprehensive global coverage (Epting et al., 2005; Goward et al., 2006; Jakubauskas, 1996; Soverel et al., 2010). Until recently, most disturbance mapping and change detection approaches only utilized imagery with limited temporal coverage (Lu et al., 2004; Singh, 1989).
Accurate landscape-scale maps of forests and associated disturbances are critical to augment studies on biodiversity, ecosystem services, and the carbon cycle, especially in terms of understanding how the spatial and temporal complexities of damage sustained from disturbances influence forest structure and function. Vegetation change tracker (VCT) is a highly automated algorithm that exploits the spectral–temporal properties of summer Landsat time series stacks (LTSSs) to generate spatially explicit maps of forest and recent forest disturbances. VCT performs well in contiguous forest landscapes with closed or nearly closed canopies, but often incorrectly classifies large patches of land as forest or forest disturbance in the complex and spatially heterogeneous environments that typify fragmented forest landscapes. We introduce an improved version of VCT (dubbed VCTw) that incorporates a nonforest mask derived from snow-covered winter Landsat time series stacks (LTSSw) and compare it with VCT across nearly 25 million ha of land in the Lake Superior (Canada, USA) and Lake Michigan (USA) drainage basins.
Accuracy assessments relying on 87 primary sampling units (PSUs) and 2640 secondary sampling units (SSUs) indicated that VCT performed with an overall accuracy of 86.3%. For persisting forest, the commission error was 14.7% and the omission error was 4.3%. Commission and omission errors for the two forest disturbance classes fluctuated around 50%. VCTw produced a statistically significant increase in overall accuracy to 91.2% and denoted about 1.115 million ha less forest (− .371 million ha disturbed and − 0.744 million ha persisting). For persisting forest, the commission error decreased to 9.3% and the omission error was relatively unchanged at 5.0%. Commission errors decreased considerably to near 22% and omission errors remained near 50% in both forest disturbance classes.
Dividing the assessments into three geographic strata demonstrated that the most dramatic improvement occurred across the southern half of the Lake Michigan basin, which contains a highly fragmented agricultural landscape and relatively sparse deciduous forest, although substantial improvements occurred in other geographic strata containing little agricultural land, abundant wetlands, and extensive coniferous forest. Unlike VCT, VCTw also generally corresponded well with field-based estimates of forest cover in each stratum. Snow-covered winter imagery appears to be a valuable resource for improving automated disturbance mapping accuracy. About 34% of the world's forests receive sufficient snowfall to cover the ground and are potentially suitable for VCTw; other season-based techniques may be worth pursuing for the remaining 66%.
Characterizing the state and processes of change in a dynamic forest environment using hierarchical spatio-temporal segmentation
2011, Remote Sensing of Environment
Citation Excerpt :
Less studied is the characterization of subtle, slow, continuous change related to partial harvest and natural regeneration or decay processes, which have less obvious effects on the landscape (Coops et al., 2006). Forest successional stages have been described (Cohen et al., 1995; Helmer et al., 2000; Jakubauskas, 1996), but studying the transitions between development stages is less common: Peterson and Nilson (1993) described trajectories of reflectance change in secondary succession of mono-specific birch and pine stands in Estonia; Schroeder et al. (2007) characterized patterns of recovery post-harvest in Western Oregon, and Vogelmann et al. (2009) characterized forest decline and mortality caused by persistent insect defoliation from 1988 to 2006 in New Mexico. Two images acquired at different dates may be sufficient for identifying landscape change (Coppin & Bauer, 1996); however, the use of more than two image dates is recognized as a superior technique when the objective is to characterize the rate of change (as opposed to just the presence or absence of change) (Goodwin et al., 2008).
Discrete changes in forest abundance, distribution, and productivity are readily detectable using a number of remotely sensed data sources; however, continuous changes such as growth and succession processes are more difficult to monitor. In this research we explore the potential of spectral trajectories generated from a 35-year (1973–2008) time-series of Landsat imagery to characterize change processes in a dynamic forest environment in northwestern Alberta, Canada. We propose a method of hierarchical spatio-temporal segmentation that enables the characterization of change processes that are spatially diffuse and temporally imprecise. Calibrated imagery from Landsat sensors are radiometrically normalized and two metrics derived from the Tasseled Cap Transformation components, greenness and brightness, are used to generate the Tasseled Cap Angle (TCA). The TCA is a measure of the proportion of vegetation to non-vegetation (the occupation state), and its derivative, the Process Indicator (PI), is a measure of change in this proportion through time. These indices condense information from the visible and near-infrared wavelengths, and facilitate lengthy time series analysis of forest landscape change using data from all Landsat sensors.
A combination of the original TCA and its derivative sequence are input to a three level hierarchical segmentation process with the highest and lowest levels defining homogeneous objects at the initial and final date, and the intermediate level identifying trajectories with similar change processes. The development through time of the TCA and PI are described, and the spatial and temporal associations of processes are statistically assessed using the Moran's Index.
A full range of change types were identified on the landscape, from stand replacing disturbances to more subtle growth and succession processes. Results indicate that the study area is in a constant state of change, and maintains a high average proportion of vegetation to non-vegetation. The amount of total landscape modified per decade increased from 18% and 14% in the 1970s and 1980s respectively, to more than 30% and 33% in the 1990s and 2000s. On average, the proportion of vegetation to non-vegetation was increasing prior to 1981, decreasing between 1981 and 1997, and increasing post-1997. There was a high degree of spatial autocorrelation amongst change processes, with a maximum Moran's I of 0.79 in 1973; landscape change became more spatially disperse and widespread after 1981. Temporal correlation of change processes was observed locally, with the period 1990–1995 having the most persistent change.

View all citing articles on Scopus

^☆: This research was supported by a National Aeronautics and Space Administration Global Climate Change Fellowship (NASA NGT-30062) and a grant from the University of Wyoming/National Parks Service Research Center. Field assistance was contributed by Todd Sutphin and Diane Debinski, while Jerry Whistler, Clayton Blodgett, Vicky Varner, and Terry Slocum provided technical advice. The support and interest of Dr. Mark Boyce and Dr. Henry Harlow (Directors, UW/NPS Research Station), Dr. John Varley (Yellowstone Center for Resources), and Yellowstone rangers Robert Siebert, Dennis Young, and Dennis Lojko is appreciated. Comments on the manuscript by Kevin P. Price and three anonymous reviewers greatly improved the manuscript.

View full text

Thematic Mapper characterization of lodgepole pine seral stages in Yellowstone National Park, USA☆

Abstract

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

Remote Sens. Environ.

Role of vegetation in the biosphere

Estimating the age and structure of forests in a multi-ownership landscape of western Oregon, U.S.A.

Int. J. Remote Sens.

A canopy-coverage method of vegetational analysis

Northwest Sci.

Major vegetation zones of Yellowstone National Park

Forest successional stages in Yellowstone National Park

Nonpyrogeneous climax lodgepole pine communities in Yellowstone National Park

Ecology

Yellowstone Vegetation: Consequences of Environment and History in a Natural Setting

Water and energy exchange

Equations for estimating biomass and leaf area of plants in the Pacific Northwest

Experiences and perspective in compiling long-term remote sensing data sets on landscapes and biospheric processes

GeoJournal

Remote sensing of terrestrial ecosystem structure: An ecologist's pragmatic view

Landscape patterns and successional dynamics in the boreal forest

Linking knowledge among spatial and temporal scales: vegetation, atmosphere, climate, and remote sensing

Landscape Ecol.

Concluding remarks

Forestry information content of Thematic Mapper data

Int. J. Remote Sens.

The Successional Tasseled Cap: a graphic description of the spectral-temporal development of a coniferous forest

Biophysical remote sensing

Ann. Assoc. Am. Geogr.

Leaf area determinations for subalpine tree species in the central Rocky Mountains

The Yellowstone fires: issues in landscape ecology

BioScience

Advances in remote sensing technologies for forest surveys and management

Can. J. For. Res.

Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures

EOSAT Landsat Technical Notes No. 1