Characterizing tropical forest regeneration in Cameroon using NOAA AVHRR data

To facilitate estimation of the carbon sink associated with tropical forests in Cameroon, regenerating and mature forests were mapped using an unsupervised classification of AVHRR channels 1, 2 and 3. Stages of regeneration were defined using nonlinear relationships between AVHRR channel 3 radiance and basal area, estimated using data collected from 183 plots (1 ha in size) in an area south-east of the capital, Yaounde. The overall extent and patterns of regenerating forest were comparable to those generated in previous studies. Older stages of regeneration could not, however, be discriminated adequately from mature forest, suggesting that areas of tropical forest disturbance may be underestimated when mapped using AVHRR data. closed tropical forests were regenerating and that their rate of expansion million ha y 1. These regenerating forests accumulate biomass more rapidly     

Fraction images derived from NOAA AVHRR data for studying the deforestation in the Brazilian Amazon

Considerable variation in the performance of passive microwave global rainfall algorithms, both spatially and temporally,was revealed by the first WetNet Precipitation Intercomparison Project, PIP-1, with no one algorithm achieving the best results, in all locations, and all the time. In this paper a Composite Algorithm Procedure is described for the Special Sensor Microwave/Imager (SSM/I) algorithms submitted to PIP-1, that attempts through combining the best algorithm results from different regions of the globe to achieve better overall global rainfall estimates than are possible from any individual algorithm alone. The Composite Algorithm Procedure (CAP) involves the segmentation of the globe into homogeneous regions, the production of validation statistics for the various algorithm results in the different regions, and the identification of combinations of algorithms which perform best globally. The segmentations were based on aspects of the spatial and temporal variability of rainfall, or the microwave properties of the surfaces of the Earth. Initial results for the Composite Algorithm Procedure are presented for a sample month (October 1987): these confirm that improved global rainfall products can be produced in this way. Code detailing a selected Composite Algorithm based on the segmentation method of the microwave properties of the Earth has been supplied to the WetNet Support Group at the Marshall Space Flight Center, Huntsville, Alabama, for experimental, regular production of global rainfall data sets on a near real-time basis.     

Assessing the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon using SPOT VEGETATION data

There has been growing concern about land use/land cover change in tropical regions, as there is evidence of its influence on the observed increase in atmospheric carbon dioxide concentration and consequent climatic changes. Mapping of deforestation by the Brazil's National Space Research Institute (INPE) in areas of primary tropical forest using satellite data indicates a value of 587,727 km² up to the year 2000. Although most of the efforts have been concentrated in mapping primary tropical forest deforestation, there is also evidence of large-scale deforestation in the cerrado savanna, the second most important biome in the region.The main purpose of this work was to assess the extent of agriculture/pasture and secondary succession forest in the Brazilian Legal Amazon (BLA) in 2000, using a set of multitemporal images from the 1-km SPOT-4 VEGETATION (VGT) sensor. Additionally, we discriminated primary tropical forest, cerrado savanna, and natural/artificial waterbodies. Four classification algorithms were tested: quadratic discriminant analysis (QDA), simple classification trees (SCT), probability-bagging classification trees (PBCT), and k-nearest neighbors (K-NN). The agriculture/pasture class is a surrogate for those areas cleared of its original vegetation cover in the past, acting as a source of carbon. On the contrary, the secondary succession forest class behaves as a sink of carbon.We used a time series of 12 monthly composite images of the year 2000, derived from the SPOT-4 VGT sensor. A set of 19 Landsat scenes was used to select training and testing data. A 10-fold cross validation procedure rated PBCT as the best classification algorithm, with an overall sample accuracy of 0.92. High omission and commission errors occurred in the secondary succession forest class, due to confusion with agriculture/pasture and primary tropical forest classes. However, the PBCT algorithm generated the lower misclassification error in this class. Besides, this algorithm yields information about class membership probability, with ∼80% of the pixels with class membership probability greater or equal than 0.8. The estimated total area of agriculture/pasture and secondary succession forest in 2000 in the BLA was 966 × 10³ and 140 × 10³ km², respectively. Comparison with an existing land cover map indicates that agriculture/pasture occurred primarily in areas previously occupied by primary tropical forest (46%) and cerrado savanna (33%), and also in transition forest (19%), and other vegetation types (2%). This further confirms the existing evidence of extensive cerrado savanna conversion.This study also concludes that SPOT-4 VGT data are adequate for discriminating several major land cover types in tropical regions. Agriculture/pasture was mapped with errors of about 5%. Very high classification errors were associated with secondary succession forest, suggesting that a different methodology/sensor has to be used to address this difficult land cover class (namely with the inclusion of ancillary data). For the other classes, we consider that accurate maps can be derived from SPOT-4 VGT data with errors lower than 20% for the cerrado savanna, and errors lower than 10% for the other land cover classes. These estimates may be useful to evaluate impacts of land use/land cover change on the carbon and water cycles, biotic diversity, and soil degradation.     

Subpixel measurement of tropical forest cover using AVHRR data

Abstract

Tropical forest assessment using data from the Advanced Very High Resolution Radiometer (AVHRR) may lead to inaccurate estimates of forest cover in regions of small subpixel forest or non-forest patches and in regions where the pattern of clearance is particularly convoluted. Test sites typifying these two patterns were chosen in Ghana and Rondonia, respectively. To capture the subpixel proportions of forest cover, a linear mixture model was applied to two AVHRR test images over the test sites. The model produced image outputs in which pixel intensities indicated the proporton of forest cover per km². For comparison, supervised maximum likelihood classifications were also performed. The outputs were assessed against classified Landsat TM scenes, converted to proportions maps and coregistered to the AVHRR images. An empirical method was applied for determining the critical forest cover per km² needed for an AVHRR pixel to be classified as forest. The critical values exceeded 50 per cent, indicating a tendency for AVHRR classification to underestimate forest cover. This was confirmed by comparing estimates of total forest cover obtained from the AVHRR and TM classifications. In the case of Ghana, a more accurate estimate of forest cover was obtained from the AVHRR mixture model than from the classification. Both mixture model outputs were found to be well correlated with those from Landsat TM. Further work should test the robustness of the approach adopted here when applied to much larger areas.     

A simple cloud masking approach using NOAA AVHRR daytime data for tropical areas

A simple method for cloud detection for AVHRR daytime data is presented and checked for equatorial/tropical areas based on a study area in northeastern Brazil. Five different cloud masking techniques based on visible and infrared spectral information for cloud detection are calibrated. The significant differences between the equatorial threshold obtained in this work and the midlatitude thresholds given by Saunders and Kriebel and by Thiermann and Ruprecht are compared and discussed. Results from the cloud masking algorithm are presented and comments are made about the problems related to the automatic cloud detection algorithm presented in this study.     

A new methodology for the estimation of biomass of coniferdominated boreal forest using NOAA AVHRR data

In this paper, a new methodology to estimate the biomass (organic matter) of conifer-dominated boreal forests is developed. It aims to estimate biomass of extensive areas where ground data are limited. First, the principal models are computed using ground measurements and high resolution satellite images. Spectral models are then applied directly to a calibrated AVHRR image mosaic covering the entire area of interest. This methodology was tested quantitatively in Finland, where detailed forest measurement data are available, on an area reaching from the west coast of Norway to the Ural mountains. The methodology appeared to perform beyond pre-test expectation.     

Assessment of forest disturbances by selective logging and forest fires in the Brazilian Amazon using Landsat data

The rapid environmental changes occurring in the Brazilian Amazon due to widespread deforestation have attracted the attention of the scientific community for several decades. A topic of particular interest involves the assessment of the combined impacts of selective logging and forest fires. Forest disturbances by selective logging and forest fires may vary in scale, from local to global changes, mostly related to the increase of carbon dioxide released into the atmosphere. Selective logging activities and forest fires have been reported by several studies as important agents of land-use and land-cover changes. Previous studies have focused on selective logging, but forest fires on a large scale in tropical regions have yet to be properly addressed. This study involved a more comprehensive investigation of temporal and basin-wide changes of forest disturbances by selective logging and forest fires using remotely sensed data acquired in 1992, 1996, and 1999. Landsat imagery and remote-sensing techniques for detecting burned forests and estimating forest canopy cover were applied. We also conducted rigorous ground measurements and observations to validate remote-sensing techniques and to assess canopy-cover impacts by selective logging and forest fires in three different states in the Brazilian Amazon. The results of this study showed a substantial increase in total forested areas impacted by selective logging and forest fires from approximately 11,800 to 35,600 km² in 1992 and 1999, respectively. Selective logging was responsible for 60.4% of this forest disturbance in the studied period. Approximately 33% and 7% of forest disturbances detected in the same period were due to impacts of forest fires only and selective logging and forest fires combined, respectively. Most of the degraded forests (～90%) were detected in the states of Mato Grosso and Pará. Our estimates indicated that approximately 5467, 7618, and 17437 km² were new areas of selective logging and/or forest fires in 1992, 1996, and 1999, respectively. Protected areas seemed to be very effective in constraining these types of forest degradation. Approximately 2.4% and 1.3% of the total detected selectively logged and burned forests, respectively, were geographically located within protected areas. We observed, however, an increasing trend for these anthropogenic activities to occur within the limits of protected areas from 1992 to 1999. Although forest fires impacted the least area of tropical forests in the study region, new areas of burned forests detected in 1996 and 1999 were responsible for the greatest impact on canopy cover, with an estimated canopy loss of 18.8% when compared to undisturbed forests. Selective logging and forest fires combined impacted even more those forest canopies, with an estimated canopy loss of 27.5%. Selectively logged forest only showed the least impact on canopy cover, with an estimated canopy loss of 5%. Finally, we observed that forest canopy cover impacted by selective logging activities can recover faster (up to 3 years) from impact when compared to those forests disturbed by fires (up to 5 years) in the Amazon region.     

Location of tuna resources in Indian waters using NOAA AVHRR data

Abstract

The world demand for tuna resources is ever increasing and there is scope for better economic returns in terms of foreign exchange earnings. It is one of the least exploited resources of the Indian seas. Remote sensing based studies on the tuna environment began in the seventies in the Gulf of Guinea. This study helped to establish a fishing strategy during the eighties. But so far this has not been attempted in Indian waters. With the basic understanding that most of the species of tuna respond directly to temperature, a study using NOAA AVHRR data was carried out to locate tuna resources. Thermal data of NOAA AVHRR for eight dates in the 1989-90 season were analysed to generate sea surface temperature (SST) images. Catch per unit effort (CPUE) of tuna longline data acquired from the Fishery Survey of India pertaining to fishing conducted by chartered vessels, was plotted on the SST images. Yellowfin tuna (YFT) comprises the maximum catch plus small quantities of marlins. It was observed that almost all the data points were located near the edge of warm water (27°-29°C). A relation between average CPUE of YFT and multi-channel sea surface temperature (MCSST) charts generated by OPC (the Ocean Products Centre) of NOAA was established. It shows on an average an increasing trend in the CPUE of YFT from 26° C (hooking rate ～ 1 per cent) to 29°C (hooking rate ～3 65 per cent) and then shows a drop with further rise in temperature. Since YFT is known to be present in a wide range of temperatures, it can be concluded that the location of warm water edges having a gradient of about 1°C and the above mentioned range of temperature will be desirable in locating tuna potential areas.     

Technical note - Tracking the Indonesian forest fire using NOAA/AVHRR images

The large scale air pollution episode due to the out-of-control biomass burning for agricultural purposes in Indonesia, which started in June 1997, has become a severe environmental problem for the neighbouring countries. Its impact on the health of the residents and ecology in the affected areas is expected to be substantial, costly and possibly long lasting. In this paper, NOAA/AVHRR images of the fire in September are reported.     

Monitoring the grasslands of the Sahel using NOAA AVHRR data: Niger 1983

Normalized difference vegetation index data derived from the Advanced Very High Resolution Radiometer on board the NOAA-7 satellite for the 1983 growing season for the Sahelian Zone of Niger are compared with biomass estimates derived from an empirical grassland productivity model. The model used daily rainfall data to estimate the potential biomass production for fourteen meteorological stations through the growing season. A good general correspondence (r = 0·75) was seen between the productivity model and the satellite-derived integrated NDV1, although specific differences were apparent between actual and potential biomass. The study shows the utility of high-temporal-resolution satellite data for monitoring grassland conditions at a local and regional scale and emphasizes the importance of a maximum value compositing approach to the analysis. The study also shows the potential of the satellite data for quantifying phenological characteristics of vegetation     

A forest fire risk assessment using NOAA AVHRR images in the Valencia area,eastern Spain

The risk of widespread forest fire has been assessed from information supplied by the AVHRR sensor onboard NOAA satellites, for the area of the Autonomous Community of Valencia in eastern Spain, where several major forest fires occurred in the summer of 1994. The burnt surface data were obtained through unsupervised classification of the spectral information of the forest areas, first, from a date previous to the forest fire; and second, from a date following the fire. The methodology for the forest fire risk evaluation is based on the temporal evolution of the NDVI weekly maximum value. Actual forest fires appear to be statistically correlated with the deduced high risk forest fire areas.     

Calibration of NOAA16 AVHRR over a desert site using MODIS data

This paper presents a new approach to AVHRR-sensors cross-calibration in the visible to shortwave-infrared spectral domain using an a-priori, well calibrated sensor (MODIS). The approach has been tested over a stable Sahara desert site and was initially applied to compare the absolute calibration coefficients of three different bands of the Terra and Aqua MODIS instruments. The observed agreement was better than 1% for bands 1 (0.67 μm), 2 (0.87 μm) and 7 (2.13 μm). The approach was then applied to cross-calibrate the AVHRR sensor onboard NOAA16. The absolute calibration coefficients derived for bands 1 and 2, using the Terra MODIS as a reference, were compared to the vicarious coefficients derived using the ocean and clouds method [Vermote E.F. and Kaufman Y.J. (1995). Absolute calibration of AVHRR visible and near-infrared channels using ocean and cloud views, International Journal of Remote Sensing, 16, 13, 2317-2340.]. The coefficients were consistent within less than 1%.     

Correction of the effect of Sun-sensor-target geometry in NOAA AVHRR data

Models of determining the effects of the bidirectional reflectance distribution function (BRDF) of different surfaces and of eliminating the effect of Sun-sensor-target geometry from the remotely sensed satellite data are actual. The objective of this study is to develop a simple relation between the Sun-sensor-target geometry and the remotely sensed vegetation index. In this investigation 238 National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images were used over Hungary during the period 1996-98. The greenness vegetation index (the difference between the reflectance values of near-infrared and visible channels) was used between days of the year 140-200, because the greenness values can be considered as constant in this period over the agricultural areas. The so-called 'hot spot effect' can be observed in the variation of reflectance values with different viewing zenith angles of the sensor. A simple quadratic relation was found between the raw AVHRR greenness values and the angle enclosed by the Sun-target and target-sensor directions over the agricultural areas, forests and grasslands. A correction method was developed to eliminate the effect of the Sun-sensor-target geometry, which it is hoped will improve the accuracy of yield forecasting and estimation procedures using NOAA AVHRR data.     

Creating a forest fire database for the Far East of Asia using NOAA/AVHRR observation

This study proposed a method for burned area accounting that uses data from the Advanced Very High Resolution Radiometer (AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) satellite series. As an area of interest, the boreal forest zone located in the Far East region of Asia was used. The burn scar mapping algorithm consists of two parts. The first is a multi‐channel threshold algorithm used for detection of real‐time burning spots in the boreal environment. The second part uses an abrupt change‐detection technique in Normalized Difference Vegetation Index (NDVI) in an 18‐year NDVI time series. Both parts of the algorithm are connected together in a complementary manner, and a forest burn scar mask is obtained for each month and consequently for each year from 1984 to 2001. The validation of the dataset was performed using data from the literature, forestry management organizations and the Landsat Thematic Mapper (TM). The comparison between those validation data and our forest fire dataset shows a satisfactory level of agreement. If the forest fire history is required for other regions in the boreal zone, the proposed methodology could be extended to that region given that a sufficient data archive is available.     

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

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

Aboveground biomass estimation using Landsat TM data in the Brazilian Amazon

The complicated forest stand structure and associated abundant tree species in the Amazon often induce difficulty in estimating aboveground biomass (AGB) using remotely sensed data. This paper explores AGB estimation using Landsat Thematic Mapper (TM) data in the eastern and western Brazilian Amazon, and discusses the impacts of forest stand structure on AGB estimation. Estimating AGB is still a challenging task, especially for the sites with complicated biophysical environments. The TM spectral responses are more suitable for AGB estimation in the sites with relatively simple forest stand structure than for the sites with complicated forest stand structure. Conversely, textures appear more important than spectral responses in AGB estimation in the sites with complicated forest stand structure. A combination of spectral responses and textures improves AGB estimation performance. Different study areas having various biophysical conditions affect AGB estimation performance.     

Early detection system of drought in East Asia using NDVI from NOAA/AVHRR data

Abstract

The influence of surface bidirectional reflectance factors, including shadowing, and of atmospheric aerosol variability are modelled for their effects on the remote sensing of desert targets from space in the 0·?μm region at high spatial resolution. The white sand reflectance data of Salomonson are used as the basis for the simulation. The effects of the surface bi-directional reflectance and atmospheric aerosol on the nadir-normalized reflectance measured at the satellite are discussed individually and jointly. The net influence of these two factors is shown to depend on the magnitude of other parameters, such as the surface reflectance and solar zenith angle.     

Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina

We used multiple regression analysis to relate evapotranspiration (ET), computed from a water balance technique, to both thermal infrared and normalized difference vegetation index data obtained from the Advanced Very High Resolution Radiometer (AVHRR) sensor on board on the National Oceanic and Atmospheric Administration (NOAA) satellite. This approach, based on only remotely sensed data, provided a reliable estimate of ET over the Pampas, the main agricultural region of Argentina. The relationship between spectral data and ET was more sensitive to the dates than to the sites used to generate the models.     

Mapping of Siberian forest landscapes along the Yenisey transect with AVHRR

In this paper NOAA AVHRR data acquired at the Sukachev Institute of Forest in Siberia, Russia is evaluated for forest management mapping applications. First a classification of the entire 1000km 2 3000km transect was performed, but was found to be too general to be of value. More useful interpretation procedures require a landscape-ecological approach. This means that computer classification should be made separately for segments of territory based ecologically distinct regions. This segmentation of the transect into ecological regions was found to improve the level of detail available in the classification. Using this approach AVHRR data were found to be adequate for small scale mapping at the level of vegetation types or plant formations. A limited study using AVHRR data for classification of mountainous regions showed that AVHRR-derived maps were more detailed than existing landscape maps. AVHRR derived classifications also compared favourably to larger scale forest management maps of softwood and hardwood forests. Current forest management in Siberia relies on very small-scale inventory maps. Thus, there is a potential role for AVHRR (or Terra) data for northern Siberian forest monitoring. The southern forests of the Yenisey meridian (below the 57th parallel ) are less uniform due to considerable human activity, and NOAA/AVHRR data will play a subordinate role in its monitoring.     

Mapping sub-pixel burnt percentage using AVHRR data. Application to the Alcalaten area in Spain

The purpose of this work is to estimate at sub-pixel scale the percentage of burnt land using the Advanced Very High Resolution Radiometer (AVHRR) through a simple approach. This methodology is based on multi-temporal spectral mixture analysis (MSMA), which uses a normalized difference vegetation index (NDVI) and a land-surface temperature (LST) image as input bands. The area of study is located in the Alcalaten region in Castellon (Spain), a typical semi-arid Mediterranean region. The results have shown an extension of approximately 55 km² affected by fire, which is only 5% lower than the statistic reports provided by the Environmental Ministry of Spain. Finally, we include a map of the area showing the percentage of estimated burnt area per pixel and its associated uncertainties. The map was validated through supervised classification of an Airborne Hyperspectral Sensor (AHS) image taken on 27 September 2007. Results have a high accuracy, with a mean error of 6.5%.