Deforestation Mapping Research Articles

Modeling land-use change in the Ipswich watershed, Massachusetts, USA

The Ipswich watershed in northeastern Massachusetts, USA, is experiencing important land-use changes, which are contributing to severe environmental problems such as eutrophication, ground water depletion and loss of wildlife. The objective of this paper is to model deforestation between 1971, 1985 and 1991 in the watershed of the Ipswich River in Massachusetts, USA, where most of the forest loss is attributable to new residential development. The maps of suitability for deforestation are calibrated with maps of real change between 1971 and 1985 by using logistic regression, multi-criteria analysis and spatial filters. The maps of 1971 and 1985 serve also as the basis to extrapolate the quantity of predicted future deforestation. Then, the calibrated suitability maps and extrapolated quantities predict the location of deforestation between 1985 and 1991. The predicted deforestation maps are validated with the map of real forest loss of 1985–1991. relative operating characteristic (ROC) and variations of the Kappa index of agreement (Kno, Klocation and Kquantity) measure the validation. For most simulation runs, Kno=93%, Klocation=8% and Kquantity=100%. The best predictor of quantity of deforestation from 1985 to 1991 is linear extrapolation forward in time of the deforestation that occurred from 1971 to 1985. It is difficult to predict the exact locations of deforestation in the watershed because only 2% of the watershed is deforested from 1971 to 1991, the patches of deforestation are scattered evenly across the landscape, and the some of the most important variables are not readily available in digital form. Nevertheless, the best predictor of location of deforestation (ROC=70%) is a suitability map that uses a spatial filter and multi-criteria evaluation of elevation, slope, and proximity to existing residential areas. The locations that are most threatened are those that are unprotected, near existing residential development and in towns where the demand for new residential development is high.

Historical satellite data used to map Pan‐Amazon forest cover

Deforestation in the Brazilian Amazon is well documented and the contributions of Brazilian deforestation to global change have been extensively discussed in both scientific and popular literature [e.g., Skole and Tucker, 1993]. However, deforestation within the non‐Brazilian tropics of South America has received much less attention. The Pan‐Amazon region covering Venezuela, Colombia, Ecuador, Peru, and Bolivia comprises ˜2 million km2 of tropical forest that is under increasing pressure from logging and development. Wall‐to‐wall high‐resolution forest cover maps are needed to properly document the complex distribution patterns of deforestation in the Pan‐Amazon [Tucker and Townshend, 2000]. The Deforestation Mapping Group at the University of Marylands Global Land Cover Facility is using Landsat data to generate tropical forest cover maps in this region (Figure l). The study shows that while rates of forest loss are generally lower than those in Brazil, there are hot spots where deforestation rates run as high as 2,200 km2 yr1.

Large-scale impoverishment of Amazonian forests by logging and fire

Amazonian deforestation rates are used to determine human effects on the global carbon cycle1,2,3 and to measure Brazil's progress in curbing forest impoverishment1,4,5. But this widely used measure of tropical land use tells only part of the story. Here we present field surveys of wood mills and forest burning across Brazilian Amazonia which show that logging crews severely damage 10,000 to 15,000 km2 yr−1 of forest that are not included in deforestation mapping programmes. Moreover, we find that surface fires burn additional large areas of standing forest, the destruction of which is normally not documented. Forest impoverishment due to such fires may increase dramatically when severe droughts provoke forest leaf-shedding and greater flammability; our regional water-balance model indicates that an estimated 270,000 km2 of forest became vulnerable to fire in the 1998 dry season. Overall, we find that present estimates of annual deforestation for Brazilian Amazonia capture less than half of the forest area that is impoverished each year, and even less during years of severe drought. Both logging and fire increase forest vulnerability to future burning6,7 and release forest carbon stocks to the atmosphere, potentially doubling net carbon emissions from regional land-use during severe El Nino episodes. If this forest impoverishment is to be controlled, then logging activities need to be restricted or replaced with low-impact timber harvest techniques, and more effective strategies to prevent accidental forest fires need to be implemented.

Mapping deforestation and land use in amazon rainforest by using SIR-C imagery

In this paper, the potential use of spaceborne polarimetric synthetic aperture radar (SAR) data in mapping landcover types and monitoring deforestation in tropics is studied. Here, the emphasis is placed on several clearing practices and forest regeneration that can be characterized by using the sensitivity of SAR channels to vegetation biomass and canopy structure. A supervised Bayesian classifier designed for SAR signal statistics is employed to separate five classes: primary forest, secondary forest, pasture-crops, quebradao, and disturbed forest. The L- and C-band polarimetric SAR data acquired during the shuttle imaging radar-C (SIR-C)/X-SAR space-shuttle mission in 1994 are used as input data to the classifier. The results are verified by field observation and comparison with the Landsat data acquired in August of 1994. The SAR data can delineate these five classes with approximately 72% accuracy. The confusion arises when separating old secondary forests from primary forest and the young ones from pasture-crops. It is shown that Landsat and SAR data carry complementary information about the vegetation structure that, when used in synergism, may increase the classification accuracy over secondary forest regrowth. When the number of land-cover types was reduced to three classes including primary forest, pasture-crops, and regrowth-disturbed forest, the accuracy of classification increased to 87%. A dimensionality analysis of the classifier showed that the accuracy can be further improved to 92% by reducing the feature space to L-band HH and HV channels. Comparison of SIR-C data acquired in April (wet period) and October (dry period) indicates that multi-temporal data can be used for monitoring deforestation; however, the data acquired during the wet season are not suitable for accurate land-cover classification.

Mapping deforestation and secondary growth in Rondonia, Brazil, using imaging radar and thematic mapper data

Excellent data on deforestation have been obtained in the tropics with the use of high-resolution optical sensors. Yet, several problems remain. Cloud cover creates data gaps that limit the possibility of complete and frequent assessments, and secondary growth is not well characterized. Active microwave sensors could complement these sensors because they operate independently of cloud cover and smoke and can detect differences in woody biomass and forest structure associated with various stages of forest clearing and regrowth. An example of comparison and synergy between the two techniques is discussed here. Polarimetric, C- (5.6 cm) and L-band (24 cm) frequency, radar data gathered in October 1994 by NASA's Spaceborne Imaging Radar C, on a test site southeast of the city of Porto Velho, in the state of Rondonia, Brazil, are analyzed in conjunction with one 1993 Landsat Thematic Mapper (TM) scene, a 9-year time series of Satellite pour l'observation de la Terre (SPOT) XS data, two Japan Earth Resources Satellite (JERS-1) radar images from 1994 and 1995, and a field visit conducted in 1995. The C-band radar data are found to be of limited utility for mapping deforestation. At L-band, multiple polarizations are required to obtain a reliable classification. The single polarization, L-band, single date, JERS-1 data underestimate the extent of deforestation, especially during the wet season. With multiple polarizations, six classes of land cover, including one level of regrowth, are mapped with 90% accuracy, but intermediate regrowth 5–8 years of age is not well separated from the forest. The Landsat TM data identify deforested areas better but provide less information on residual woody biomass levels. Combining the two classifications, seven classes of land cover invluding two levels of regrowth are mapped with 93% accuracy. The results show that the deforestation rate for 1994 was 1.7%. Large variations in residual woody biomass are detected among new clearings. Half of the total deforested land is in some stage of regrowth, but most of it is less than 5 years old. Secondary growth is therefore a significant form of land use that is recleared quickly.

NOAA‐AVHRR studies of vegetation characteristics and deforestation mapping in the Amazon Basin

Abstract This paper reviews and illustrates the advantages of the use of NOAA‐AVHRR data for vegetation mapping and monitoring in the Amazon Basin. The data are considered under two main categories: data of low spatial but high temporal resolution and long time period availability, and data of a higher spatial resolution but more limited temporal availability. A review of the use of the former type of data reveals that this has been relatively successful in generalised classifications of broad land cover categories in the Amazon Basin. However these generalised classifications have in the past attempted to replicate as closely as possible, broad classes represented on continental or global scale maps. By doing so they have overlooked the important inter‐ and intra‐annual information on vegetation vigour and seasonality within Amazon forest areas that may be discerned with careful analysis of the data. The widespread use of the full spatial resolution NOAA‐AVHRR imagery for large scale deforestation mapping and monitoring is also reviewed. This shows that while most deforestation was successfully detected on the imagery, the resolution of the sensor hindered the accurate measurement of deforestation areas and rates, especially where the pattern of deforestation was complex. Finally, the potential operational use of NOAA‐AVHRR data in an automated deforestation mapping system is presented. It is suggested that full spatial resolution NOAA‐AVHRR data can provide a suitable base for an automated deforestation mapping system, especially when combined with information derived from higher spatial resolution imagery to adjust for forest/non‐forest interface patterns and with data of low spatial but high temporal resolution to adjust for forest seasonality variations.