int. j. remote sensing, 2000, vol. 21, no. 13&14, 2589± 2611
Evaluating temporal changes using Multi-Spectral Scanner and Thematic Mapper data on the landscape of a natural reserve: the New Jersey Pine Barrens, a case study S. S. LUQUE† Center for Remote Sensing and Spatial Analysis, Rutgers University, New Brunswick, New Jersey, USA; e-mail:
[email protected] (Received 22 May 1997; in nal form 6 March 1998) Abstract. Natural disturbance suppression and anthropogenic perturbations have altered the composition and structure of the New Jersey Pinelands National Reserve (NJPNR). The combination of satellite remote sensing imagery and GIS provided the means to map and monitor land cover change at landscape level scales in the NJPNR. The Pinelands has experienced a change in landcover, with the mixed deciduous forest replacing the pine forest community.
1.
Introduction The frontiers of a new Earth System science are beginning to shift attention to larger scales, from the traditional stand level studies to landscape, regional and even global concerns. Remote sensing technology provides a means to measure changes in landscape pattern as well as changes in condition over time (Peterson and Running 1989 ). Because landscape types are constantly changing, studies of landscape dynamics at large spatial extent would have been di cult without the development of remote sensing techniques during the last two decades. Such developments, in combination with the increasing availability of remotely sensed data and new methods in spatial modelling and GIS, have increased the extent and accuracy of assessing rates, patterns, and direction of regional change. Signi cant methodological progress has been made since multidate maps were used to illustrate changes in forest cover over time. The advantage s in applying remote sensing methods are evident in the evaluation and management of forest resources. Satellite remote sensing data can be useful in delineating structural and functional characteristics of forest at a variety of geographical scales (Iverson et al. 1989). Applications of remotely sensed data to illustrate changes in forest over time have been reported by many investigators (e.g. Hall et al. 1988, 1991, Sader and Joyce 1988, Iverson et al. 1989, Fearnside et al. 1990, Green This paper was not presented at the Terra 3 Conference but has been included in this special issue in view of its relevance to the topic. †Visiting scholar. Department of Geography, Downing Place, University of Cambridge, Cambridge CB2 3EN, England, UK; tel: 1 44 (0)1223 572407; fax: 1 44 (0)1223 333392; e-mail:
[email protected]. Internationa l Journal of Remote Sensing ISSN 0143-116 1 print/ISSN 1366-590 1 online © 2000 Taylor & Francis Ltd http://www.tandf.co.uk/journals
2590
S. S. L uque
and Sussman 1990 ). These studies have focused on rates and patterns of land conversion. However, few studies have documented changes in spatial landscape characteristics in terms of patch size and edge eŒect (Turner and Ruscher 1988, Skole and Tucker 1993, Spies et al. 1994 ). Also, most studies of landscape change are focused on tropical forests while relatively few studies have been reported for temperate forest (e.g. Stearns 1990, Hall et al. 1991, Ripple et al. 1991, Fiorella and Ripple 1993, Spies et. al 1994, White and MladenoŒ1994 ). The purpose of this study is to present the monitoring of a large area, the New Jersey Pinelands, (450 000 ha) in a near real time/cost eŒective manner combining information from the US Landsat Thematic Mapper (TM), and the US Landsat Multi-spectral Scanner (MSS). The methodology used allowed the integration of satellite data into land planning and management activities. This study evaluates landscape level changes in the New Jersey Pinelands National Reserve (NJPNR), in order to examine the e cacy of the management policies that have been implemented in the area. The analysis focuses on the fragmentation of the landscape and its relationship with the current management practices. The supervised land cover classi cation in conjunction with change detection techniques made possible the quanti cation of changes between dates, as well as the characterization of the Pinelands landscape. Given the complexity of digital classi cation, particular attention was given to assess the reliability of the results. The Pinelands Biosphere Reserve serves as an international model for regional land use planning. Thus, the methodology used to monitor the existing management in the Pinelands, may be useful for other regions worldwide in order to obtain an eŒective evaluation of the impact of human activities in relation to natural resources. Indeed, this case study is intended to set an example of practical application that can be of large utility for studies of forested areas undergoing changes. 2.
The study site The New Jersey Pine Barrens comprise a mosaic of upland, aquatic, and wetland environments occupying a contiguous area of approximatel y 450 000 ha of sandy, acid, coastal plain soils in the southern portion of the state ( gure 1). It supports more than 500 species of animals and 800 species and varieties of plants (McCormick 1979, Buchholz and Good 1982, Good and Good 1984). At the present, this relatively undeveloped region, is near the heart of the major Washington-to-Bosto n metropolitan corridor of North America, one of the major urbanized regions in the world. Centred at about 39 ß 40¾ N latitude and 74 ß 40¾ W longitude, the Pine Barrens experience the pressures emanating from the major urban centres that border it (McCormick and Forman 1979 ). In the last 30 years surrounding areas became increasingly more developed, resulting in a marked contrast between the Pinelands and adjacent areas (Good 1982, Collins 1988a, Luque et al. 1994 ). In order to protect its unique resources from the pressures of development, the Pine Barrens was declared by the US Congress a New Jersey Pinelands National Reserve (NJPNR) in 1978. In January 1981 a Comprehensive Management Plan (CMP) for the Pinelands National Reserve became eŒective. The plan sets forth land-use regulations for about 373 600 ha of land ( gure 2), considering a strategy, standards, and regulatory mechanisms to govern land-use by legislative mandate (Collins et al. 1988). The Central Preservation Area constitutes only 149 251 ha surrounded by a buŒer zone of multiple use and limited regional growth areas ( gure 2). Agriculture and recreation are encouraged under this plan. New residential,
Figure 1.
Location of the study area (373 600 ha). This region represents one of the largest tracts of ‘natural’ landscapes remaining in the US Eastern seaboard. The area is constrained between two of New Jersey’s major transportation corridors.
L andscape pattern and change 2591
S. S. L uque
2592
Figure 2. New Jersey Pinelands Land Management Area. The Central Preservation Area District constitutes 149 251 ha surrounded by a buŒer zone of multiple use and limited regional growth areas. 1. 2. 3. 4. 5.
Preservation area district Forest Agricultural production Rural development Regulated growth
6. 7. 8. 9. 10.
Pinelands towns Military/federal installation Pinelands village In ll Special agriculture
L andscape pattern and change
2593
commercial, and industrial developments are permitted only if they can be accommodated at no destructive risk to critical land and water resources (Collins 1988a, 1988b) . Municipalities, counties, and various state agencies (e.g. New Jersey Department of Environmental Protection) have important assignments for implementation of speci c programs in the Pinelands CMP. Overall, responsibility for assuring eŒective implementation of the CMP, however, is vested in the Pinelands Commission, the state-created regional agency that prepared the plan. Thus, the Pinelands Commission has the responsibility to make this intergovernmental partnership work eŒectively, particularly on the local level (Collins 1988b). At the present, the Pinelands Reserve is recognized as a key model in UNESCO’s International Network of Biosphere Reserves, illustrating the biosphere concept of combining conservation with accommodation of a wide range of human resource needs. While previous studies have documented the local or site level impact of human activities on native Pine Barrens ora composition and structure (Ehrenfeld 1983, Roman and Good 1983, Good and Matlack 1987, Gibson et al. 1988, Stolzfus 1990, Matlack et al. 1993), the scienti c and resource management community still do not fully understand the cumulative impact of human pressures at the landscape level. Despite the eŒorts of the Pinelands Commission to limit development in the Pinelands, particularly in the Preservation Area, there are intrusions into the interior that have dissected the natural communities into smaller patches (Good and Matlack 1987, Luque et al 1994, Ehrenfeld et al. 1997 ). Inappropriate development already had begun to pollute the Pinelands pristine waters, fragment its unique ecosystem, and destroy its plant and animal diversity (Collins 1988b) . Despite the widespread support given to the idea that the Pine Barrens should be saved, there is continuing disagreement on what portion, and to what extent the area should be protected (Collins et al. 1988). Of particular concern are the direct impacts of suburban/ exurban development and natural resource management activities (i.e. timber harvesting and re protection/control activities) on the size and frequency of wild re disturbance, which has long played a key role in structuring the Pine Barrens landscape. 3. Methods 3.1. Image analysis Data processing and photo-interpretatio n for this project were performed at the Center for Remote Sensing and Spatial Analysis at Rutgers University (New Brunswick, New Jersey, USA). ERDAS 7.5, ERDAS Imagine software, and GRASS 4.1 were the main software packages used for the image processing. The rst step was to obtain a land cover classi cation for each point in time through an Image Analysis Process. The images used for this study were acquired by the Landsat-1 Multi-Spectral Scanner (MSS) and Landsat-5 Thematic Mapper (TM). The diŒerent images used in the present study were MSS (Path15/Row 32, 33) for 1972 and 1976 and TM (Path 14/Row 32, 33) for 1984 and 1988. Also, a June 1991 TM image data was used to analyse specially, the Preservation Area and a subsequent image of 1994 was used for model validation purposes (Luque 2000). Summer and early fall image data were used for this study, in order to separate the deciduous and coniferous stands through leaf-on canopy diŒerences rather than through the strong seasonal diŒerences in re ection found during leaf-oŒseasons.
2594
S. S. L uque
In addition, leaf-on image helped to distinguish other land-use types, specially agricultural lands from developed lands. Landsat Thematic Mapper data were selected in order to take advantage of the better spectral and spatial resolution for the available dates in the study area. The integration of data coming from diŒerent sources proved useful, providing longer historical series and better data quality for comparison. Finally, the data selected were the best cloud free images available for a temporal study in the Pinelands area. TM bands -3, -4, -5, and -6 were found to provide adequate discrimination among the major Pinelands land cover/vegetation types (Lathrop 1994). However, for the present study, channel 6 is too diŒerent from any of the MSS channels and therefore was not appropriate in conjunction with MSS data. All four MSS bands were used for the study. A subset of bands -2, -3 and -4 was selected for TM, since these channels are the more closely related to the electromagnetic spectrum of MSS bands -1, -2, -3, and -4. In addition band-5 TM was also used. Although Benson and DeGloria (1985) state that TM bands -4, -3 and -2 have approximatel y the same spectral sensitivity as MSS bands -4, -2, and -1; a series of preliminary tests run for the Pinelands area showed that the discrimination between vegetation community types obtained with TM bands -2, -3, and -4 was lower than that obtained using all MSS bands. The red and infrared bands (MSS-3, -4; TM-3, -4 and -5) were those most likely to show key vegetation features. The post-classi cation comparison method used has already been tested (Luque et al. 1994), and was found useful for the purpose of the present study for several reasons: (1) It is considered to be the best method when anniversary images do not correspond to the same month, thus the results are based on the accuracy obtained on each image data, (2) when the image data are classi ed, the eŒect of haze and other distortions are eliminated or minimized, (3) the land cover classi cation allowed the execution of the spatial analysis for each point in time, so that, the results from the spatial and the temporal analysis can be compared. One of the complications that arose was the need to use adjacent scenes of image data in order to be able to analyse the whole Pinelands area ( gure 1). It was necessary to perform all the classi cations independently for each scene. The stitch procedure, to mosaic the les, was done after the classi cations and after geographically referencing each data set. A ow chart ( gure 3) gives an overview of the diŒerent methodological steps in the image analysis process. L inear contrast stretch was applied as a way to improve the visible contrast of each image. Also, non-linear spectral enhancement was applied interactively to search for features or areas with speci c characteristics within a given range. Then, reference polygons were outlined for the spectrally ‘homogeneous’ landscape units in each subscene. Thus, training sites representative of the existing cover types were obtained. Note, that the training sites (geographical locations) were consistently delineated across dates in order to minimize classi cation errors when performing change detection later in the analysis. The training sites were then located in a digital image display of the scene data and their spectral responses in all four bands were examined. The signatures obtained were evaluated statistically, to determine if the cover types represented by the various polygons were separable and/or contained multiple ‘spectral classes’. In addition, SEED (image processing ) was used to identify other training samples when needed, and to perform alarm evaluations. The generation of the signatures from the samples was an iterative process that involved intense manipulation and analysis in order to accurately represent the classes desired.
L andscape pattern and change
Figure 3.
2595
Flow chart of the diŒerent methodological steps in the image analysis process.
3.2. Supervised classi cation A supervised maximum-likelihood classi cation was conducted using all four bands for each subscene. One pass of a ‘majority rule’ smoothing algorithm using a 3 by 3 window was applied to the nal classi cation result prior to the accuracy assessment. The classi cation was based on the vegetation scheme used by Andropogon
S. S. L uque
2596
Associates (1980, 1981) for the forest vegetation of the Pinelands. The major vegetation community types delineated correspond to: (1) Oak/pine forest, (2) Hardwood swamps, (3) Coniferous forest, (4) Non-forested, (5) Agriculture, (6) Water and (7) Non-forested wetlands (table 1). Due to limitations of the spatial and spectral resolution of the Landsat-MSS data, certain vegetation communities of interest (e.g. see Andropogon Associates 1980, 1981) could not be properly classi ed and mapped. Therefore, these categories were aggregated to form a Coniferous forest category and a non-forested category. Also, the US Fish and Wildlife Service National Wetland Inventory digital wetland GIS data (NWI data) were used to better discriminate the areas classi ed as Hardwood swamps and Non-forested wetlands. This rule-based classi cation was performed in GRASS 4.1 as a post-classi cation procedure ( gure 3). In order to improve the quality of the classi cation results T hresholding, using a probability image le to screen out misclassi ed pixels, was performed. The threshold was computed in two ways: (a) by inputting a con dence level interactively with the pointing device, while the original image le was displayed on the screen, and the threshold pixels were alarmed; (b) by inputting into a chi-square parameter ( x2), so that the threshold was calculated statistically. In both cases, thresholding has the eŒect of cutting the ‘tail’ oŒ the histogram of the probability image le, which represents the pixels with the highest distance values (i.e. lowest probability of correct classi cation). It is important to note that the x2 statistic used to perform the thresholding is an approximation , because x2 statistics are generally applied to independent variables (having no covariance), and this is not usually true of image data. After this procedure was completed a new classi cation was performed with the improved signature le iteratively until satisfactory results were obtained for each image date considered ( gure 3). Table 1. Key 1
2 3
4 5 6 7
Category
Land cover categories used in the image interpretation. Description
Upland forest dominated by oak (> 50% Quercus spp.) species, including white (Quercus alba), black (Quercus velutina), post (Quercus stellata), and scarlet oak (Quercus coccinea), with lesser amounts of pine forest Hardwood swamps Lowland forest dominated by red maple (Acer rubrum), black gum (Nyssa sylvatica), etc. Coniferous forest (a) Upland forest dominated by Pitch pine (> 50% Pinus rigida) forest, and other pines like shortleaf (Pinus echinata), Virginia (Pinus virginiana); with lesser amounts of upland oak species. The Pine Plains are the extreme expression of this type of forest. (b) Lowland forest dominated by Pitch Pine with diverse understory. (c) Lowland forest dominated by Atlantic white cedar (Chamaecyparis thyoides), found along stream courses. Non-forested Town housing developments, major transportation routes, military installations, quarry, clear cut areas and bare soil. Agriculture Agricultural land and cranberry bogs. Water Natural or man-made water bodies including rivers, lakes, and ooded cranberry bogs. Non-forested wetlands All types of marshes and associated vegetation. Oak/pine forest
L andscape pattern and change
2597
3.3. Recti cation of classi ed image data Landsat-MSS and TM digital data were geographically referenced to the Universal Transverse Mercator (UTM) coordinate system. Ground control points (GCP) were used to develop the mapping equations for resampling the data. The 1:24 000 topographic maps produced by the US Geological Survey were used for scene registration as sources of control point coordinates. In the case of 1991 image data for the Preservation Area, a GPS (Global Positioning System), was also used. The system used was a real-time DGPS with an external antenna and software to enter positions (X, Y and Z) into ERDAS. GPS was found specially useful under forest canopy in the Pinelands, when relatively static features in the terrain were di cult to locate. Computation of the root mean square error (RMS ) was performed for each error of the GCPs in order to reduce geometric distortion. Given the extent of the study area and the di culty registering MSS data, at least 50 points were needed for each image, in order to obtain a standard precision of RMS < 0.6 pixel. Nearesterror neighbour interpolation was used for the rst order co-ordinate transformation . 3.4. Accuracy assessment The classi cation accuracy assessment was based on a random sampling of approximatel y 500 random pixel comparisons between each classi ed image and independent photo-interpretatio n of aerial photographs and high-altitude colour infrared photographs . Also, the existing Pinelands Commission vegetation maps were used to check each point for correctness of classi cation. Given the complexity of digital classi cation, particular attention was given to assess the reliability of the results, and an error matrix was determined. The error matrix was found to be a very eŒective tool to represent the accuracies of each category along with both the errors of inclusion (commission errors) and the errors of exclusion (omission errors) always present in a classi cation (Congalton 1991). Real time GPS data, in conjunction with established vegetation plots in the study area were also used to verify classi cation success based on the 1991 data. Special attention was given to the areas that experienced changes during the time period considered; in particular the ones with discrimination problems during the classi cation procedure. Field visits were performed and ancillary data as well as aerial photographs from New Jersey Department of Environmental Protection (DEP) archives were used to follow speci c areas that showed particular changes. 3.5. Pixel-by-pixel comparison through time In order to perform a pixel-by-pixel comparison of all the data sets through time, the TM data sets were resampled from a 30 m to an 80 m pixel. This algorithm aggregates the pixels using majority rule. Classi cation agreement between TM and MSS data, in relation with the resolution and the results obtained from the Spatial Analysis, was tested in a previous study (Luque 1992). Analysis of covariance was used, in the previous work, to determine the eŒect of changing pixel size on landscape metrics. The results indicated that landscape metrics were not signi catively aŒected ( p > 0.001, ANOVA) by the change in pixel size up to 80 m. These results were consistent with Wickham and Riitters (1995) who found also that when working with Landsat-TM and MSS, the resultant landscape metric values were not dramatically aŒected by the diŒerence in spatial resolution up to 80 m pixel size (MSS resolution).
S. S. L uque
2598
The land cover classi cations were used to compute the spatial patterns for the Pinelands Landscape. A land use le of the Pinelands area from the New Jersey DEP (Department of Environmental Protection) was used as the reference study area ( gure 2), so that all data sets had the same number of cells and an exact registration. 3.6. L and cover changes DiŒerences between each land cover through time were computed, based on the change detection technique explained previously, for each time-period. Grass 4.1, in conjunction with the change masks produced in ERDAS, were used to generate the output raster maps showing the changes. The new maps were developed representing all combinations of category values resulting from the cross tabulation of the input maps. A reclassi cation was then used to obtain the nal map showing the changes that occurred in the area. Field visits to the areas that showed greatest change, and work with ancillary photographs and archives for the early years were undertaken in order to evaluate the reliability of the change-maps. 3.7. Radiometric normalization of multi-date images to detect change Image normalization reduces pixel BV (brightness value) variation caused by non-surface factors so that variations in pixel BVs between dates can be related to actual changes in surface conditions. Normalization enables the use of image analysis logic developed for a base year scene to be applied to other scenes (Eckhardt et al. 1990 ). Normalization targets are assumed to be constant re ectors, therefore any changes in their brightness values are attributed to detector calibration, astronomic, atmospheric, and phase angle diŒerences. Once these variations are removed, changes in BV may be related to changes in surface conditions. For the present analysis quarry areas, reservoirs and other clear water bodies, airports, and military training sites were selected to represent radiometrically bright, dark, and medium BVs. In general, the target areas were located on relatively at terrain, so that incremental changes in Sun angle between dates had the same proportional increase or decrease in direct beam sunlight for all normalization targets. About 30 training sites were considered in each date. The mean BVs of the base image targets (Date i) were regressed against the mean BVs of the Date i 1 1 targets for the bands in the red and in the near-IR of the image data. This regression model produced an NDVI (Normalized DiŒerence Vegetation Index) image of Date i 1 1 into Date i (converted NDVI). Then Image Algebra Change Detection was performed, according to the following expression: D
ijk
5
converted NDV I Date i NDVI
(1)
NearIR Õ Red and D 5 change pixel value. ijk NearIR 1 Red Besides image rationing, image diŒerencing was also performed. The subtraction resulted in positive and negative values in areas of radiance change, and zero values in areas of ‘no-change’. In the new ‘change image’, a new scaling range was applied in order to eliminate the negative values. The spectral change image produced was then recoded into a binary mask le, consisting of pixels between the two dates, and these were viewed as candidate pixels for categorical change. The change mask was then overlaid onto Date 2 of the where, NDVI 5
Figure 4. Mean and standard deviation for the signature of original data from the same training site for the diŒerent years. Graphs a and b represent signature values for Oak/pine, while c and d represent the signature for Pitch Pine Lowlands. The RED band corresponds to: 0.60–0.79 mm for MSS, and 0.63–0.69 mm for TM; the near- infrared band is: 0.70–0.80 mm for MSS, and 0.76–0.90 mm for TM.
L andscape pattern and change 2599
S. S. L uque
2600 Table 2.
Accuracy assessment.
(a) 1972 Reference data Classi ed data
MD
CF
NF
Ag
W
M
Row totals
Number Producer’s User’s correct accuracy accuracy
Mixed 132 deciduous forest (MD) Coniferous 4 forest (CF) Non-forested 3 (NF) Agriculture 0 (Ag) Water (W) 1 Marshes (M) 1 Column 141 totals
4
8
4
1
0
149
132
92.95
68.58
104
4
2
1
1
116
104
93.06
60.65
1
49
5
0
2
60
49
79.24
61.66
1
4
34
1
2
42
34
73.81
80.85
0 0 110
0 1 55
1 0 46
57 1 61
2 17 24
51 20 449
57 17 393
83.44 70.83
93.44 80.1
Overall classi cation accuracy 5 87.53% (b) 1976 Reference data Classi ed data
MD
CF
NF
Ag
W
M
Row totals
Number Producer’s User’s correct accuracy accuracy
Mixed 125 deciduous forest (MD) Coniferous 2 forest (CF) Non-forested 2 (NF) Agriculture 0 (Ag) Water (W) 0 Marshes (M) 0 Column 129 totals
5
2
1
0
0
133
125
90.69
89.86
86
3
1
0
1
83
66
81.13
94.33
13
163
12
1
1
192
163
94.76
84.89
2
4
63
2
0
71
63
81.82
88.73
0 0 109
0 0 180
0 0 86
10 0 13
1 3 6
11 3 503
10 3 450
84.61 50
90.91 100
Overall classi cation accuracy 5 89.46%
analysis and only those pixels which were detected as having changed were classi ed in the Date 2 imagery. 4. Results and discussion 4.1. Classi cation results Forest stands of oak presented a much higher re ectance in the red and near-IR as compared to the coniferous forest stands ( gure 4). Thus, in the image, Pine forest presented a signature darker than the bright oak forest. Lathrop (1994) reported the same trends in the spectral responses of forest stands performing a spectroradiometric
L andscape pattern and change Table 2.
2601
(Cont ’d ).
(c) 1984 Reference data Classi ed data
MD
CF
NF
Ag
W
M
Row totals
Number Producer’s User’s correct accuracy accuracy
Mixed 138 deciduous forest (MD) Coniferous 3 forest (CF) Non-forested 2 (NF) Agriculture 0 (Ag) Water (W) 0 Marshes (M) 0 Column 143 totals
5
6
4
0
0
153
136
96.5
80.18
94
3
3
0
1
104
94
91.26
90.38
4
109
6
4
3
128
109
90.83
83.16
0
1
17
0
0
18
17
56.86
94.44
0 0 103
1 0 120
0 0 30
83 0 97
1 2 7
95 2 500
83 2 453
85.9 28.6
97.9 100
Overall classi cation accuracy 5 90.6% (d) 1988 Reference data Classi ed data
MD
CF
NF
Ag
W
M
Row totals
Number Producer’s User’s correct accuracy accuracy
Mixed 145 deciduous forest (MD) Coniferous 2 forest (CF) Non-forested 2 (NF) Agriculture 0 (Ag) Water (W) 0 Marshes (M) 0 Column 147 totals
4
3
2
1
0
156
145
98.63
93.55
106
3
1
1
1
114
106
93.58
82.96
3
119
6
2
3
135
119
95.86
88.15
0
1
18
0
0
19
18
62.07
90.73
0 0 113
0 0 124
2 0 28
36 0 42
1 3 8
38 3 465
36 3 427
92.86 81.8
92.31 100
Overall classi cation accuracy 5 91.83%
analysis of foliage in the Pinelands. This trend can be readily seen when the signatures from the same training site for each year of the original data are compared in the red and in the near-IR bands. Figure 4, shows an example for an oak/pine training site versus a Pitch Pine lowlands training site, this last one has a response intermediate to that of pine uplands and white cedar swamps (Lathrop 1994 ). The other consideration is that the diŒerences between the Landsat-MSS and TM sensors are greater for the near-IR than for the red band, this is mostly due to the diŒerent regions covered by the spectrum. The MSS near-IR band-3 covers the 0.70–0.80 mm while the TM spectral range in band-4 covers between 0.76–0.90 mm. In order to
S. S. L uque
2602
nd a good discrimination between mixed deciduous forest and mixed coniferous forest, it was found that image types TM-5, -4, and -3; MSS- 4, -2, and -1; and TM-4, -3, and -2 were more interpretable when displayed than TM-5, -3, and -2. With these band combinations, both the re ective and absorptive properties of the diverse cover types were exploited. Results of the computer assisted classi cation and the accuracy assessment performed showed that some of the major Pinelands land cover types can be discriminated and mapped with Landsat-MSS and TM data with a reasonable degree of accuracy. The comparison of the automated classi cation with a visual interpretation of the imagery at approximatel y 500 random points for each scene is reported in table 2. The classi cation error matrices show the misclassi ed pixels for each class based on the random sample. The overall classi cation accuracy was estimated to be 87.5% for 1972, 89.5% for 1976, 90.6% for 1984, and 91.3% for 1988. As expected TM generally showed better results than MSS. Non-forested wetlands were misrepresented in the random sample because of the small area covered; this led to a very poor producer’s accuracy in 1976 and 1984. On the other hand, the user’s accuracy for this category was always high, since this coverage was obtained after postclassi cation procedures with the NWI data of 1986. Note that for accuracy assessment purposes hardwood swamps were incorporated with oak/pine forest into a mixed deciduous forest category. Both, the TM and MSS supervised classi cations produced good results in discriminating between coniferous and deciduous forests, and between cultural features and forest. When the spectral responses in all four bands used were examined before the classi cation, MSS showed poor discrimination between bare soils and non-forested wetlands. However, it showed good results in discriminating cranberry bogs while TM failed. Overall, the integration of the images into a GIS database, and the post-classi cation analysis were the key for the production of reliable results. 4.2. L and cover changes The regression models used for the radiometric normalization procedure, are presented in table 3. The mean BVs of the base image targets (Date i ) were regressed against the mean BVs of the Date i 1 1 targets for the red and the near-IR bands of the image data. These regression models (table 3) were used to ’normalize’ the brightness values between image dates in order to produce the binary mask presented in gure 5. It is interesting to note that MSS 72–76 regression explained close to 100% of the variation observed, this was also the case for the TM 84–88 regression. Although the regression for the MSS 76– TM 84 explained a large amount of the variation, this percentage (table 3) was not as high as that observed for the two other Table 3. nir 72 5
Regression models obtained from the radiometric process performed in order to produce the binary change mask.
red 72 5 nir 76 5 red 76 5 nir 84 5 red 84 5
Õ
1
0.400179 Õ Õ Õ Õ Õ
1.86073 0.371167 2.35023 0.201068 1.23564
1
0.939600 nir 76
0.881904 red 76 0.827501 nir 84 1 0.763821 red 84 1 0.638534 nir 88 1 0.981620 red 88 1
r2 5 0.98 r2 5 r2 5 r2 5 r2 5 r2 5
0.95 0.81 0.85 0.99 0.97
L andscape pattern and change
Figure 5.
2603
Change/no-change binary masks for 1972–76, 1976–84, 1984 –88.
regressions. This is likely to be due to the change of sensors (MSS for the 1976 and TM for the 1984 image data). The critical step in the process followed to produce the nal binary masks ( gure 5) was the choice of the threshold boundaries between change and no-change pixels displayed in the histogram of the change image. The optimum thresholds that were identi ed to produce the nal change/ no-change masks ( gure 5) are presented below in terms of standard deviations, CLASS CHANGE
Mask 72–76 0.62
Mask 76–84 0.68
Mask 84–88 0.83
Each ‘change/no-change’ mask ( gure 5) was then overlaid onto the earlier date of classi ed image and only those pixels which were detected as having changed were viewed as candidate pixels for categorical change. The use of vegetation indices to compare data from diŒerent sensors was extremely useful, because the diŒerent bands of each sensor were reduced to a single number per pixel. The approach with the change mask and image algebra change detection using NDVI gave more accurate results, than post-classi cation comparison alone, in terms of changes between the forest types. Also, the NDVI image appeared to show good separation between pine-dominan t and oak-dominant stands for the Pinelands area. 4.3. L andscape changes Oak/pine forest is the predominant landcover in the present Pinelands landscape ( gure 6). During the study period from 1972 to 1988, the oak/pine coverage increased from 28% to 36%, while the coniferous forest coverage decreased from 41% to 33% ( gure 6). Therefore, the Pinelands have experienced a change from a coniferous forest dominated landscape to an oak/pine dominated landscape. Note that the major change occurs in 1976 when non-forested areas increased at the expense of coniferous forest. Between 1972–76 the Pinelands experienced the largest shift in coverage from coniferous and mixed deciduous into non-forest, as well as from coniferous forest into mixed deciduous forest ( gure 7). The shift from non-forest into forest was lowest during the period 1972–76 ( gure 7). The changes that occurred between 1976 –84 were intermediate to pre-CMP (previous to the implementation of the Plan)
2604
S. S. L uque
Figure 6. Total percentage per land cover type for the New Jersey Pinelands study area. Percentage of error from the accuracy assessment is indicated by a vertical line.
Figure 7. Percentage of land cover changes per time periods studied in the NJPNR. Note that between 1972–76 the Pinelands experienced the largest shift in coverage from forest into non-forest, as well as from coniferous forest into mixed deciduous forest.
and post-CMP (after the implementation of the Plan) changes. It is important to note, also, that the shift from non-forest into forest increased between 1976 –84 and 1984 –88, and most of the new forest was mixed deciduous forest.
L andscape pattern and change
2605
The areas that experienced changes through the study period, were located mostly outside the preservation area and near the borders of the NJPNR ( gure 8). Some large scale housing development has occurred within the reserve, and in particular in the west-east southern corridor and to the west. Most of this development, however, was prior to the o cial designation of the Pinelands area as a reserve. Most of the forest area that has been converted into non-forest (sky-blue areas gure 8) was restricted to special management districts set aside for agriculture, rural development and Pinelands towns according to the Pinelands CMP. The fragmentation of the forest was more evident along the roads that cross the Reserve, in the borders of the designated area, and in the west-east corridor along the Atlantic City Expressway. Rural development and regulated growth were the areas that accounted
Figure 8. Spatial Land Cover Changes from 1972 to 1988 in the NJPNR. Black lines are the divisions of the Pinelands Land Management Areas. The major changes were mostly restricted to special management districts according to the Pinelands CMP. green: red: yellow: sky-blue:
Non-forest into coniferous forest Coniferous forest into mixed deciduous forest Non-forest into mixed deciduous forest Forest into non-forest
2606
S. S. L uque
for the major percentage of coniferous forest that shifted into mixed deciduous forest (red areas— gure 8), a trend that continued after the CMP was implemented (Luque 2000 ). It was also noted that mixed deciduous forest seems to dominate in areas under the eŒect of anthropogenic disturbances, within built-up land, Pinelands towns, and areas under regulated management. To more closely evaluate trends in the area of maximum protection, the preservation area was analysed separately. The same trends found for the whole study area presented above, were also observed within the preservation area. In particular however, coniferous forest remained as the dominant coverage within the preservation area in 1991 ( gure 9). In terms of forest/non-forest this area did not experience any change ( gure 10). The proportion of forest was considerably higher inside the preservation area, where 91% of the area was forest cover versus the 82% forest cover observed for the whole Pinelands Land Management Area. When looking at the diŒerent forest communities, the coniferous forest coverage decreased from 71% in 1972 to 53% in 1991 ( gure 9). The preservation area did not present any important change before or after the management was implemented. The amount of coniferous forest that shifted into mixed deciduous forest, within the preservation area, was the same before and after the implementation of the CMP. 5.
Conclusions The management plan implemented in the Pinelands is an outstanding example of cooperative contributions that allow a sustainable form of resource-management. The new challenge is to nd long-term strategies to solve and adjust the weaknesses of the plan presented in this study if the biological integrity of the Pinelands is to be preserved. One of the major concerns considering the changes found in the Pinelands is the
Figure 9. Total percentage per land cover type by year for the Pinelands Preservation Area District in particular. Note that coniferous forest remained as the dominant coverage within the Preservation Area in 1991.
L andscape pattern and change
2607
Figure 10. Total percentage for a binary matrix of forest/non-forest by year for the Pinelands Preservation Area District in particular. The proportion of forest is considerably higher inside the Preservation area.
conversion of coniferous forest into mixed deciduous forest. Since the Pinelands is a natural re community the shift may have been due mostly to the increase in re control, that in turn leads to a gradual succession of increasing dominance of oak. The observed changes in relation to the increase in mixed deciduous forest are consistent with the re record from the Forest Fire Service (DEP 1990) that showed a 35% decrease in the extension of forest aŒected by re between 1980–89 as compared with 1970–79. Also, the average surface burned per re in the 80s decreased by 25% as compared with the 70s. This trends call for attention on further research in re dynamics and its relationship with the Pinelands forest in order to nd viable ways to manage a ‘let-burn’ policy in the Pinelands. There are many theories regarding the re-establishment of a re regime in terms of the size and frequency of res (Sharitz et. al. 1992, Turner et al. 1993, Baker 1995). The most important consideration is that until the present, the US Forest Service and state forest commissions have policies to control large, catastrophic forest res, but use controlled res as management tools. The re program, as stated by the Commission (Pinelands Commission 1982) outlines ‘standards to protect human health and welfare’. It is di cult to meet a particular disturbance management prescription when re cannot enter into many restricted zones because of the spread of housing or the presence of agricultural land. Thus, current prescriptions for using re fail to simulate the historical role of re in prone- re ecosystems. Most prescribed burns, like the ones in the Pinelands, are low-intensity winter res (Pierson, personal communication) , whereas formerly most lightning res and those set by Native Americans occurred during the growing season and were of higher intensity (Sharitz et al. 1992). Restoration of some forest communities like the Pinelands will be impossible without the reintroduction of summer res. However
2608
S. S. L uque
it is no longer possible to permit high-intensity summer res to extend across landscapes since the spread of single-family housing within the woodland make the implementation of a re program almost impossible. Also, the shift of coniferous forest into mixed deciduous forest is explained by the success of mixed deciduous forest to revegetate areas that were non-forested before 1976. In addition, it should be noted that there was an important percentage (20%) of non-forest that was converted into forest in the privately-held areas (Luque 2000 ), due in part to the conversion of agricultural land into forest (1982 Census of Agriculture). Most of the new forest that took over the abandoned agricultural land was found to be mixed deciduous forest. However, we must be careful in interpreting this trend of loss-of-pine/replacement-of-oak , since additional accuracy assessment and further research is necessary to adequately validate the change detection maps. Remote sensing has long been an important tool in regional studies of natural resources, but its potential for understanding ecological patterns and processes has not been fully realized. One of the main reasons is the question of accuracy. Research based on remote sensing data is always subject to possible errors based on the subjectivity of the interpreter, on the thresholding process performed to separate classes, on diŒerences in sensors, dates, etc. The sources of errors are many and varied. The challenge is how to minimize these sources of errors. In the last years substantial progress has been made in nding diŒerent algorithms and techniques to minimize sources of error (e.g. Arai 1992, Jong 1993, Curran and Hay 1996 ). In this research, intensive work with the probability les was done in order to nd the best threshold among the diŒerent classes. Also radiometric normalization was performed in order to produce a binary mask to improve the change detection procedure. Despite the attempts to reduce the in uence of sensor diŒerences on the change detection analysis, subtle diŒerences in classi cation may remain uncorrected. The errors involved in the type of analysis performed may change details in the results regarding a possible over-estimation of mixed deciduous forest for TM data, but overall they will not aŒect the more general conclusions regarding the trends and dynamics of the Pinelands system. It is important, therefore, to note that the results presented do indicate a change in the structure of the NJPNR, and future research should focus on this change.† Decision makers may need to consider ways to recover and maintain the coniferous forest within the Pinelands Reserve where the shift of coniferous into mixed deciduous forest cover is evident. This is a critical issue, from the perspective of maintaining biodiversity. Otherwise many unique species of animals and plants associated with this community type will be lost. Due to increased concerns about local and global ecological problems, public land managers have had to reorient their emphasis toward conservation of functional ecosystems (Benham 1990, Kessler et al. 1992). Maintaining biodiversity, water quality, and aesthetic values are considered as important as providing products such as timber on the same parcels of land (Turner et al. 1995). Maintaining a natural disturbance (e.g. re) regime may also be a goal in the management of some public lands. However, managing for biodiversity, water quality, or natural disturbance necessitates a regional or landscape perspective. Managers today need to perceive †In this regard, I undertook a landscape ecology approach in order to understand the management problems and the changes within the Pinelands. I focused on spatial patterns, heterogeneity, and disturbances in the area under management (Luque 2000, this issue).
L andscape pattern and change
2609
the entire landscape instead of individual parcels of land. In addition, managers must also begin to anticipate how activities in one area might aŒect the physical and biotic properties of adjoining areas. One of the main challenges for the future of the Pinelands is the intensi cation of studies related to the Pinelands disturbance regimes, in particular res. Because of the extent of the Pinelands, the NJPNR can serve as one of the few large reserves in the world where scienti c answers regarding disturbance regimes can be found. Then, management can be focused upon perpetuating the disturbance regime in order to ‘restore’ the native Pine Barrens vegetation community. Acknowledgments Support for this work has been in part provided from a NASA Graduate Student Fellowship in Global Change research to S. S. Luque. The author wish to thanks J. A. Bognar for invaluable assistance in all phases of the analysis and J. Gasprich for inestimable technical support. Dr R. G. Lathrop reviewed earlier drafts of this manuscript and provided extremely helpful advice in the image processing. I also thank the valuable comments of two anonymous reviewers. This work greatly bene ted from helpful discussions with Drs E. J. Gustafson, R. G. Lathrop, D. A. Robinson, and S. Madry. References Andropogon Associates, 1980, Forest vegetation of the Pinelands (New Lisbon, New Jersey: New Jersey Pinelands Commission). Andropogon Associates, 1981, Vegetation maps of the Pinelands (New Lisbon, New Jersey: New Jersey Pinelands Commission). Arai, K., 1992, Maximum likelihood TM classi cation—taking the eŒect of pixel to pixel correlation into account. Geocarto International, 2, 33–39. Baker, W. L., 1995, Longterm response of disturbance landscapes to human intervention and global change. L andscape Ecology, 10, 143–159. Benhan, R. W., 1990, Multiresource forest management: a paradigmatic challenge to professional forestry. Journal of Forestry, 88, 12–18. Benson, A. S., and Degloria, S. D., 1985, Interpretation of Landsat-4 thematic mapper and multispectral scanner data for forestry surveys. Photogrammetric Engineering and Remote Sensing, 51, 1281–1289. Buchholz, K., and Good, R. E., 1982, Compendium of New Jersey Pine Barrens literature (New Jersey: Center for Coastal and Environmental Studies, Division of Pinelands Research, Rutgers University). Collins, B. R., 1988a, The backdrop for Pinelands legislation. In Protecting the New Jersey Pinelands—A new direction in land-use management, edited by B. R. Collins and E. W. B. Russell (New Brunswick, New Jersey: Rutgers University Press), pp. 34–59 Collins, B. R., 1988b, How is the Pinelands Program working? In Protecting the New Jersey Pinelands—A new direction in land-use management, edited by B. R. Collins and E. W. B. Russell (New Brunswick, New Jersey: Rutgers University Press), New Brunswick, New Jersey, USA, pp. 275-295. Collins, B. R., Good, N. F., and Good, R. E., 1988, The landscape of the New Jersey Pine Barrens. In Protecting the New Jersey Pinelands—A new direction in land-use management, edited by B. R. Collins and E. W. B. Russell (New Brunswick, New Jersey: Rutgers University Press), pp. 3–33. Congalton, R. G., 1991, A review of assessing the accuracy of classi cations of remotely sensed data. Remote Sensing of Environment, 37, 35–46. Curran, P. J., and Hay, A. M., 1996, The importance of measurement error for certain procedures in remote sensing at optical wavelengths. Photogrammetric Engineering and Remote Sensing, 52, 229–241. Department of Environmental Protection (DEP), 1990, Protecting New Jersey’s forest f rom
2610
S. S. L uque
re. DEP Paper. New Jersey Division of Parks and Forestry. Forest Fire Service, Trenton, New Jersey, USA. Eckhardt, D. W., Verdin, J. P., and Lyford, G. R., 1990, Automated update of an irrigated lands GIS using SPOT HRV imagery. Photogrammetric Engineering and Remote Sensing, 56, 1515–1522. Ehrenfeld, J. G., 1983, The eŒects of changes in land-use on swamps of the New Jersey Pine Barrens. Biological Conservation, 25, 353– 375. Ehrenfeld, J. G., Han, X. F. J., Parsons, W. F. J., and Zhu, W., 1997, On the nature of environmental gradients: temporal and spatial variability of soils and vegetation in the New Jersey Pinelands. Journal of Ecology, 85, 785–798. Fearnside, P. M., Tardin, A. T., and Meira, L. G. M., 1990, Deforestation rate in Brazilian Amazonia (Brasilia, Brasil:National Secretariat of Science and Technology). Fiorella, M., and Ripple, W. J., 1993, Determining successional stage of temperate coniferous forests with Landsat satellite data. Photogrammetric Engineering and Remote Sensing, 59, 239–246. Gibson, D. J., Collins, S. L., and Good, R. E., 1988, Ecosystem fragmentation of oak-pine forest in the New Jersey Pinelands. Forest Ecology and Management, 25, 105–122. Good, R. E., (editor), 1982, Ecological solutions to environmental management concerns in the Pinelands National Reserve (New Brunswick, New Jersey: Center for Coastal and Environmental Studies, Rutgers University). Good, R. E., and Good, N. F., 1984, The Pinelands National Reserve: an ecosystem approach to management. BioScience, 34, 169–173. Good, R. E., and Matlack, G. R., 1987, The biological impact of landscape fragmentation in the New Jersey Pine Barrens. Final Report, Department of Environmental Protection (DEP). Division of Pinelands Research—Center for Coastal and Environmental Studies, Rutgers University, New Jersey, USA. Green, G. M., and Sussman, R. W., 1990, Deforestation history of the eastern rain forests of Madagascar from satellite images. Science, 248, 212– 215. Hall, G. F., Botkin, D. B., Strebel, D. E., Woods, D. E., and Goetz, S. J., 1991, Largescale patterns of forest succession as determined by remote sensing. Ecology, 72, 628–640. Hall, G. F., Strebel, D. E., and Sellers, P. J., 1988, Linking knowledge among spatial scales: vegetation, atmosphere, climate and remote sensing. L andscape Ecology, 2, 3–22. Iverson, L. R., Graham, R. L., and Cook, E. A., 1989, Applications of satellite remote sensing to forested ecosystems. L andscape Ecology, 3, 131– 143. Jong, S. M., 1993, An application of spatial ltering techniques for land cover mapping using TM-images. Geocarto International, 1, 43–49. Kessler, W. B., Salwasser, H., Cartwright, C. W. Jr., and Caplan, J. A., 1992, New perspectives for sustainable natural resources management. Ecological Applications, 2, 221–225. Lathrop, R. G., Jr., 1994, Satellite remote sensing of Pinelands ecosystems. Bartonia, 58, 1–9. Luque, S., 2000, The challenge to manage the biological integrity of nature reserves: a landscape ecology perspective. International Journal of Remote Sensing, 21, 2613–2643. Luque, S. S., Lathrop, R. G. Jr., and Bognar, J. A., 1994, Temporal and spatial changes in the New Jersey Pine Barrens landscape. L andscape Ecology, 9, 287–300. Luque, S. S., 1992, Natural and human processes interacting to cause landscape change in the New Jersey Pinelands. MS Thesis. Rutgers, The State University of New Jersey. New Brunswick, New Jersey. Matlack, G. R., Gibson, D. J., and Good, R. E., 1993, Clonal propagation, local disturbance, and the structure of vegetation ericaceous shrubs in the Pine Barrens of New Jersey. Biological Conservation, 63, 1–8. McCormick, J., 1979, The vegetation of the New Jersey Pine Barrens. In Pine Barrens: Ecosystem and L andscape, edited by R. T. T. Forman (New York Academic Press), pp. 229–243. McCormick, J., and Forman, R. T. T., 1979, Introduction: location and boundaries of the New Jersey Pine Barrens. In Pine Barrens: Ecosystem and L andscape, edited by R. T. T. Forman (New York: Academic Press), pp. xxxv–xlii. Myers, N., 1988, Tropical forests and their species, Going, Going...? In Biodiversity, edited by E. O. Wilson and F. M. Peter (Washington, DC: National Academy Press), pp. 28–35.
L andscape pattern and change
2611
Peterson, D. L., and Running, S. W., 1989, Applications in forest science and management. In T heory and Applications of Optical Remote Sensing, edited by Ghassem Assar (New York: Wiley & Sons), pp. 429–465. Pinelands Commission, 1982, New Jersey Pinelands—Comprehensive Management Plan (New Lisbon, New Jersey: New Jersey Pinelands Commission). Ripple, W. J., Bradshaw, G. A., and Spies, T. A., 1991, Measuring forest landscape patterns in the Cascade Range of Oregon, USA. Biological Conservation, 57, 73–88. Roman, C. T., and Good, R. E., 1983, Wetlands of the New Jersey Pinelands: values, functions, impacts and a proposed buVer delineation model (New Brunswick, New Jersey: Center for Coastal and Environmental Studies, Rutgers University). Sader, S. A., and Joyce, T. A., 1988, Deforestation rates and trends in Costa Rica, 1940 to 1983. Biotropica, 20, 11–19. Sharitz, R. R., Bprong, L. R., Van Lear, D. H., and Pinder, J. E., 1992, Integrating ecological concepts with natural resource management of southern forests. Ecological Applications, 2, 223– 237. Spies, T. A., Ripple, W. C., and Bradshaw, G. A., 1994, Dynamics and pattern of managed coniferous forest landscape in Oregon. Ecological Applications, 4, 555–568. Stearns, F., 1990, Forest history and management in the northern midwest. In Management of Dynamic Ecosystems, edited by J. M. Sweeney (West Lafayette, Indiana: North Central Section, The Wildlife Society), pp. 107–122. Stoltzfus, D. L., 1990, Development of community structure in relation to disturbance and ecosystem fragmentation in Atlantic white cedar swamps in the Pinelands National Reserve, New Jersey. PhD Thesis, Rutgers University, New Brunswick, New Jersey, USA. Turner, M. G., Arthaud, G. J., Engstrom, R. T., Hejl, S. J., Liu, J., Loeb, S., and McKelvey, K., 1995, Usefulness of spatially explicit population models in land management. Ecological Applications, 5, 12–16. Turner, M. G., Romme, W. H., Gardner, R. H., O’ Neill, R. V., and Kratz, T. K., 1993. A revised concept of landscape equilibrium: Disturbance and stability on scaled landscapes. L andscape Ecology, 8, 213–227. Turner, M. G., and. Ruscher, C. L., 1988, Changes in landscape patterns in Georgia, USA. L andscape Ecology, 1, 241– 251. White, M. A., and Mlandenoff, D. J., 1994, Old-growth forest landscape transitions from pre-European settlement to present. L andscape Ecology, 9, 191–205. Wickham, J. D., and Riitters, K. H., 1995, Sensitivity of landscape metrics to pixel size. International Journal of Remote Sensing, 16, 3584–3594.
