Forest Change Detection Research Articles

MODIS time-series imagery for forest disturbance detection and quantification of patch size effects

The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m single day surface reflectance (MOD09GQK) and 16-day composite gridded vegetation index data (MOD13Q1) were used to detect forest harvest disturbance between 2000 and 2004 in northern Maine. A MODIS multi-date Normalized Difference Vegetation Index (NDVI) forest change detection map was developed from each MODIS data set. A Landsat TM/ETM+ change detection map was developed as a reference to assess the effect of disturbed forest patch size on classification accuracy (agreement) and disturbed area estimates of MODIS. The MODIS single day and 16-day composite data showed no significant difference in overall classification accuracies. However, the 16-day NDVI change detection map had marginally higher overall classification accuracy (at 85%), but had significantly lower detection accuracy related to disturbed patch size than the single day NDVI change detection map. The 16-day composite NDVI data achieved 69% detection accuracy and the single day NDVI achieved 76% when the disturbed patch size was greater than 20 ha. The detection accuracy increased to approximately 90% for both data sets when the patch size exceeded 50 ha. The R 2 (range 0.6 to 0.9) and slope (range 0.5 to 0.9) of regression lines between Landsat and MODIS data (based on forest disturbance percent of township) increased with the mean disturbed patch size of each township. The 95% confidence intervals of forest disturbance percent estimate for each township were narrow with less than 1% of each township at the mean MODIS forest disturbance level.

Perspectives of Maine Forest Cover Change from Landsat Imagery and Forest Inventory Analysis (FIA)

Abstract A forest change detection map was developed to document forest gains and losses during the decade of the 1990s. The effectiveness of the Landsat imagery and methods for detecting Maine forest cover change are indicated by the good accuracy assessment results: forest—no change, forest loss, and forest gain accuracy were 90, 88, and 92%, respectively, and the good correlation of mapped areas of forest cover loss with forest inventory analysis (FIA) tree size class decrease at the county level. The combination of annually collected permanent FIA plots with accurate statewide forest change maps offers a complementary perspective of Maine's forest resource.

Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances

Vegetation indices and transformations have been used extensively in forest change detection studies. In this study, we processed multitemporal normalized difference moisture index (NDMI) and tasseled cap wetness (TCW) data sets and compared their statistical relationships and relative efficiencies in detecting forest disturbances associated with forest type and harvest intensity at five, two and one year Landsat acquisition intervals. The NDMI and TCW were highly correlated (>0.95 r 2) for all five image dates. There was no significant difference between TCW and NDMI for detecting forest disturbance. Using either a NDMI or TCW image differencing method, when Landsat image acquisitions were 5 years apart, clear cuts could be detected with nearly equal accuracy compared to images collected 2 years apart. Partial cuts had much higher commission and omission errors compared to clear cut. Both methods had 7–8% higher commission and 12–22% higher omission error to detect hardwood disturbance when it occurred in the first year of the 2-year interval (as compared to 1-year interval). Softwood and hardwood change detection errors were slightly higher at 2-year Landsat acquisition intervals compared to 1-year interval. For images acquired 1 and 2 years apart, NDMI forest disturbance commission and omission errors were slightly lower than TCW. The NDMI can be calculated using any sensor that has near-infrared and shortwave bands and is at least as accurate as TCW for detecting forest type and intensity disturbance in biomes similar to the Maine forest, particularly when Landsat images are acquired less than 2 years apart. Where partial cutting is the most dominant harvesting system as is currently the case in northern Maine, we recommend images collected every year to minimize (particularly omission) errors. However, where clear cuts or nearly complete canopy removal occurs, Landsat intervals of up to 5 years may be nearly as accurate in detecting forest change as 1 or 2 year intervals.

Use of metatruth image concept to assess forest change detection accuracy at pixel level

Traditionally, the validation of a classified multispectral image only quantifies its correspondence to ground reference data containing thematic information generalized at the stand level, with stands represented as vector polygons. Little is known of the accuracy of such classifications at a scale below the stand. This study presents a methodology to assess classification accuracy at pixel level, i.e. sub-polygon, where the classification procedure is embedded in a change detection environment. A new type of reference data (Metatruth Image) was generated based on the integration of the outputs of various independent change detection procedures. The integration consisted of calculating for each pixel a probability distribution or pixel purity index for each change class by independent change detection procedures, defined by the number of times the pixel has been classified as a certain change class. First, the relationship between purity and accuracy was successfully validated. Next, the Metatruth Image was created based on ‘high purity pixels’. Performing traditional accuracy assessment on the outputs of individual change detection procedures using the Metatruth Image as reference dataset, demonstrated that former outputs identified change events accurately at pixel level. As a consequence, traditional accuracy assessment at polygon level underestimates the true accuracy at pixel level of the change detection procedure in a systematic way with differences in kappa coefficients of agreement around 20%.

Surface-based image-to-patient registration using a hand-guided laser-range scanner system

This paper demonstrates the applicability of small footprint, high sampling density airborne laser scanners for boreal forest change detection, i.e. the estimation of forest growth and monitoring of harvested trees. Two laser acquisitions were carried out on a test site using a Toposys-1 laser scanner. Three-dimensional canopy height models were calculated for both data sets using raster-based algorithms. Object-oriented algorithms were developed for detecting harvested and fallen trees, and for measuring forest growth at plot and stand levels. Out of 83 field-checked harvested trees, 61 could be automatically and correctly detected. All mature harvested trees were detected; it was mainly the smaller trees that were not. Forest growth was demonstrated at plot and stand levels using an object-oriented tree-to-tree matching algorithm and statistical analysis. The precision of the estimated growth, based on field checking or statistical analysis, was about 5 cm at stand level and about 10–15 cm at plot level. The authors expect that the methods may be feasible in large area forest inventories that make use of permanent sample plots. Together with methods for detecting individual sample trees, the methods described may be used to replace a large number of permanent plots with laser scanning techniques.

Automatic detection of harvested trees and determination of forest growth using airborne laser scanning

This paper demonstrates the applicability of small footprint, high sampling density airborne laser scanners for boreal forest change detection, i.e. the estimation of forest growth and monitoring of harvested trees. Two laser acquisitions were carried out on a test site using a Toposys-1 laser scanner. Three-dimensional canopy height models were calculated for both data sets using raster-based algorithms. Object-oriented algorithms were developed for detecting harvested and fallen trees, and for measuring forest growth at plot and stand levels. Out of 83 field-checked harvested trees, 61 could be automatically and correctly detected. All mature harvested trees were detected; it was mainly the smaller trees that were not. Forest growth was demonstrated at plot and stand levels using an object-oriented tree-to-tree matching algorithm and statistical analysis. The precision of the estimated growth, based on field checking or statistical analysis, was about 5 cm at stand level and about 10–15 cm at plot level. The authors expect that the methods may be feasible in large area forest inventories that make use of permanent sample plots. Together with methods for detecting individual sample trees, the methods described may be used to replace a large number of permanent plots with laser scanning techniques.

Indices from snow-covered Landsat imagery for boreal forest change detection.

Boreal forest, which covers 15% of the Earth's land area, is subjected to disturbances such as fires and logging. Because such forest is covered by snow during the long winter, the window of opportunity for observation in summer, when the trees are fully foliated, is short. The use of snow-covered Landsat images for forest change detection was studied as alternative to the use of summer images. A time series of five Landsat images taken between 1980 (MSS) and 1999 (TM) in the Russian Far East was used. Changes were detected by comparing certain indices in successive images. The indices were RED, normalized RED (N-RED) NIR, MIR, NDVI, NDSI, Tasseled Cap Brightness, Greenness and Wetness and SAITO and YAMAZAKI's V2 and S3. Indices from summer images taken in 1995 were compared with indices from winter images. The majority of winter indices showed points of change for much longer periods after disturbance than did the summer indices, although the winter indices derived by deviation were less stable than the summer indices. Winter RED, N-RED and Wetness were particularly suitable for change detection, while summer Wetness was suitable for estimating the succession stage after disturbance. The detection accuracies were 66% to 100%.

Structural change detection in a disturbed conifer forest using a geometric optical reflectance model in multiple-forward mode

Geometric optical reflectance models provide a physical linkage between image data and forest structure. We developed a "multiple-forward-mode" pseudoinversion modeling approach to produce structural lookup tables for Landsat Thematic Mapper images before and after 1993 partial harvests in New Brunswick, Canada. Modeling results validated for stand density, crown radius, and stem counts enabled simple forest structural change detection.

Forest change detection in Kalarani round, Vadodara, Gujarat— a Remote Sensing and GIS approach

Studies on spatial distribution of the different forests during 1970 to 1999 using integrated remote sensing and GIS techniques reveals that area under forests in the Kalarani Round, is progressively reducing with the time. In 1970 forest area was found to be 22.75 sq km. in 1989 it was 15.34 sq km and in 1999 it was only 12.93 sq km. Thus. there is considerable loss in the tree cover from 1989 to 1999. Jhanpa and Kalarani R.F. are the example of this. Ground surveys indicate that the majority of the loss is caused by heavy grazing pressure there by decreasing regeneration of vegetation. If suitable measures are not taken. whole area may be converted into wasteland in due course of time. Another reason for the forest loss, could be land encroachments by villagers for their overwhelming needs. Karali R.F. is not much disturbed apart from the plantation raised in the foothills. Outside the reserve forest boundary, the change was observed due to construction of Narmada Sagar canal.

Reflectance calibration of thematic mapper data for forest change detection

Abstract Landsat-5 TM images over a forest area in Sweden were calibrated to reflectance. Using images for each summer from 1984 to 1991, changes in the sensor response offset were found for TM bands 1 to 4. The aerosol optical thicknesses were estimated from horizontal visibility and the sensor deterioration was estimated by assuming a constant lake reflectance. The reflectance changes to be detected in a forest are small compared with the differences between absolute calibrated images. Statistical relative calibration, followed by the scaling of the residuals to reflectance, is therefore recommended for making local reflectance changes comparable over time.

Forest change detection in Margala hills of Pakistan

The Margala hills forests in Pakistan's capital city, Islamabad, are suffering degradation due to stone quarrying and related industrial activities in the neighbourhood. Multi-temporal/multi-resolution satellite data of a part of the Margala hills were used to monitor the forest changes over the last 14 years. Digital enhancement and principal component analysis were applied on MSS (1976), SPOT XS (1987) and TM (1990) data to detect degraded areas. The above data were classified into forested and non-forested zones using maximum likelihood classification techniques and were overlaid to delineate areas undergoing changes.