Capturing ephemeral forest dynamics with hybrid time-series and composite mapping in the Republic of Panama

Measuring and progressing toward international goals of curbing deforestation and improving livelihoods of people who depend on forests requires nuanced understanding of forests and the processes surrounding deforestation and degradation. Despite rapid improvements in Earth Observation technology, monitoring of tropical forests remains hindered by persistent cloud cover, heterogeneous landscapes, long wet seasons, and small and ephemeral clearings masked by rapid growth. A hybrid method is presented that combines elements of both time-series and compositing approaches to best overcome these obstacles to map forest cover and change in the Republic of Panama based on Landsat imagery. The resulting Panama Vegetation-Cover Time-Series (PVCTS) maps depict forest cover in Panama from 1990 to 2016 at 30 m resolution. Acknowledging the fuzzy boundary between forest and non-forest classes, these maps employ a hierarchical classification scheme that reflects the natural process of regeneration and can accommodate different definitions of forest and deforestation. Classification accuracy is 97–98 % between forest/non-forest categories and 76–81 % for deforestation events. The maps show a slight greening of Panama from 1990 to 2016 caused by expansion of young secondary growth. The annual rate of deforestation in mature forest has remained around -0.6 %/yr, although young forests have matured at a similar rate such that there is no net loss of forest. While estimates of total forest cover are similar to official national estimates depending on forest definition, there is little agreement in location of deforestation events.     

Use of multi-temporal Landsat images for analyzing forest transition in relation to socioeconomic factors and the environment

Recently there have been reports of forest regrowth occurring in different regions across the world. There is also a growing recognition of the potential beneficial impact that secondary forests may have on the global environment: providing crucial ecosystem services such as soil conservation, stabilization of hydrological cycles, carbon sequestration, and support for forest dependent communities. Consequently, there is a growing awareness of the need to recognize that landscapes are complex shifting mosaics wherein forest clearing and reforestation take place. In this study, the rates of reforestation, deforestation, forest regrowth and degradation were measured using multi-temporal Landsat images of Danjiangkou, China. Landsat data from 1990, 1999 and 2007 were (1) classified as dense forest, open forest and non-forest areas and (2) compared between years to identify forest cutting, regeneration and degradation. The results showed that there was a net gain of 29,315 ha of forest area (including dense and open forest) from 1990 to 2007, showing a clear trend of reforestation in the study area. Forest modification (degradation and regrowth) and change categories (deforestation and reforestation) occurred simultaneously during the observation time period. Socioeconomic data from public statistics and environmental attributes allowed the assessment of the socioeconomic factors and the environmental conditions that caused these changes using non-metric multidimensional scaling (NMDS). The research showed that the socioeconomic factors due to different policies were major driving forces of forest transition, whereas environmental attributes of the underlying landscape constrained forest cover changes. These findings have led to a better understanding of forest transition at a local scale in our study region. Comprehensive knowledge of these relationships may be useful to reconstruct past forest transitions and predict future changes, and may help to enhance sustainable management practices aimed at preserving essential ecological functions.     

Monitoring of deforestation and land use changes (1925–2012) in Idukki district,Kerala, India using remote sensing and GIS

The studies on forest cover change can reveal the status of forests and facilitate for its conservation planning. Idukki is the largest district in the state of Kerala having a total geographical area of 5019 km². The objectives of the present study are to map forest cover in Idukki district using multi-temporal remote sensing data (1975, 1990, 2001 and 2012) and topographical maps (1925), to analyze the trends in deforestation and land use changes. Overall statistics for the period of 1925 indicate that about 4675.7 km² (93.2 %) of the landscape was under forest. The forest cover in 2012 was estimated as 2613.4 km² (52.1 %). Recently, due to the implementation of policies and protection efforts, the rate of deforestation was greatly reduced. The commencement of hydroelectric projects during 1925–1990 responsible for an increase of area under water bodies by inundating other land uses. The long term analysis shows agricultural area been decreasing and commercial plantations been increasing in the district. There has been a significant increase in the area of plantations from 1236.2 km² (1975) to 1317.3 km² (2012).     

Historical effects of dissolved organic carbon export and land management decisions on the watershed-scale forest carbon budget of a coastal British Columbia Douglas-fir-dominated landscape

Background

To address how natural disturbance, forest harvest, and deforestation from reservoir creation affect landscape-level carbon (C) budgets, a retrospective C budget for the 8500 ha Sooke Lake Watershed (SLW) from 1911 to 2012 was developed using historical spatial inventory and disturbance data. To simulate forest C dynamics, data was input into a spatially-explicit version of the Carbon Budget Model-Canadian Forest Sector (CBM-CFS3). Transfers of terrestrial C to inland aquatic environments need to be considered to better capture the watershed scale C balance. Using dissolved organic C (DOC) and stream flow measurements from three SLW catchments, DOC load into the reservoir was derived for a 17-year period. C stocks and stock changes between a baseline and two alternative management scenarios were compared to understand the relative impact of successive reservoir expansions and sustained harvest activity over the 100-year period.

Results

Dissolved organic C flux for the three catchments ranged from 0.017 to 0.057 Mg C ha^?1 year^?1. Constraining CBM-CFS3 to observed DOC loads required parameterization of humified soil C losses of 2.5, 5.5, and 6.5%. Scaled to the watershed and assuming none of the exported terrestrial DOC was respired to CO₂, we hypothesize that over 100 years up to 30,657 Mg C may have been available for sequestration in sediment. By 2012, deforestation due to reservoir creation/expansion resulted in the watershed forest lands sequestering 14 Mg C ha^?1 less than without reservoir expansion. Sustained harvest activity had a substantially greater impact, reducing forest C stores by 93 Mg C ha^?1 by 2012. However approximately half of the C exported as merchantable wood during logging (~176,000 Mg C) may remain in harvested wood products, reducing the cumulative impact of forestry activity from 93 to 71 Mg C ha^?1.

Conclusions

Dissolved organic C flux from temperate forest ecosystems is a small but persistent C flux which may have long term implications for C storage in inland aquatic systems. This is a first step integrating fluvial transport of C into a forest carbon model by parameterizing DOC flux from soil C pools. While deforestation related to successive reservoir expansions did impact the watershed-scale C budget, over multi-decadal time periods, sustained harvest activity was more influential.

     

Local variability in the timing and intensity of tropical dry forest deciduousness is explained by differences in forest stand age

Tropical Dry Forest deciduousness is a behavioral response to climate conditions that determines ecosystem-level carbon uptake, energy flux, and habitat conditions. It is regulated by factors related to stand age, and landscape scale variability in deciduous phenology may affect ecosystem functioning in forests throughout the tropics. This study determines whether observed phenological differences are explainable by forest age in the southern Yucatán Peninsula in Mexico, where forest clearing for shifting cultivation has created a mosaic of forest stands of varying age. Matched-pair statistical tests compare neighboring forest pixels of different age class (12–22 years versus 22+ years) and detect significant differences in Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI)-derived metrics related to the timing and intensity of deciduousness during three dry seasons (2008–2011). In all seasons, young forests exhibit significantly more intense deciduousness, measured as total seasonal change of EVI normalized by annual maximum EVI (p < 0.001), and larger normalized EVI change during successive dry season months relative to start-of-dry-season EVI (p < 0.001), than neighboring older forests subject to similar environmental conditions.     

Space-time detection of deforestation,forest degradation and regeneration in montane forests of Eastern Tanzania

Naturally isolated montane forests in East Africa are hotspots of biodiversity, often characterised by high species endemism, and are fundamental contributors to water services. However, they are located in areas highly suitable for agriculture, making them a prime target for agricultural activities. The Eastern Arc Mountains (EAM) in Eastern Tanzania are within the target regions for agricultural development under the Southern Agricultural Growth Corridor of Tanzania (SAGCOT). However, forest monitoring initiatives that track long-term forest dynamics and the ecological impact of current agricultural development policies on forests, are lacking. Here, we use the STEF (Space-Time Extremes and Features) algorithm and Landsat time series to track forest disturbances (deforestation and degradation) and forest gains (regeneration) as spatio-temporal events over seventeen years (2001–2017) in the montane forests of the Mvomero District in Tanzania. We found that 27 % (∼ 20 487 ha) of montane forests were disturbed between 2001 and 2017, mainly led by deforestation (70 %). Small-scale crop farms with maize, banana, and cassava crops, were the most planted on deforested areas. Most disturbances occurred at lower elevation (lowland montane), but there was an increasing shift to higher elevations in recent years (2011–2017). Forest disturbances exclusively occurred at small spatial scales, a pattern similar to other forest montane landscapes in Africa, which lowers detection capabilities in global forest loss products. Our locally calibrated and validated deforestation map (Producer's accuracy = 80 %; User’s accuracy = 78 %) shows a gross underestimation of forest cover loss (>10 000 ha) by global forest loss products in these mountainous forest landscapes. Overall, we found few areas undergoing forest regeneration, with only 9 % of the disturbed forest regenerating over 17 years. Long-term conversion to cropland prevented regeneration in the lowlands, with regeneration mainly happening at higher elevations. However, the shift of deforestation and forest degradation to higher elevations may challenge high elevation regeneration trends, leaving the remaining blocks of montane forest in the Mvomero District at a risk of degradation and disappearance. Without effective forest conservation measures, market-driven agricultural development is likely to trigger an expansion of cropland at the expense of forests to meet the increased demand for the agricultural products promoted, with negative impact on biodiversity, carbon sequestration and water services.     

Current remote sensing approaches to monitoring forest degradation in support of countries measurement,reporting and verification (MRV) systems for REDD+

Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.     

Greenhouse gas emissions from tropical forest degradation: an underestimated source

Background

The degradation of forests in developing countries, particularly those within tropical and subtropical latitudes, is perceived to be an important contributor to global greenhouse gas emissions. However, the impacts of forest degradation are understudied and poorly understood, largely because international emission reduction programs have focused on deforestation, which is easier to detect and thus more readily monitored. To better understand and seize opportunities for addressing climate change it will be essential to improve knowledge of greenhouse gas emissions from forest degradation.

Results

Here we provide a consistent estimation of forest degradation emissions between 2005 and 2010 across 74 developing countries covering 2.2 billion hectares of forests. We estimated annual emissions of 2.1 billion tons of carbon dioxide, of which 53% were derived from timber harvest, 30% from woodfuel harvest and 17% from forest fire. These percentages differed by region: timber harvest was as high as 69% in South and Central America and just 31% in Africa; woodfuel harvest was 35% in Asia, and just 10% in South and Central America; and fire ranged from 33% in Africa to only 5% in Asia. Of the total emissions from deforestation and forest degradation, forest degradation accounted for 25%. In 28 of the 74 countries, emissions from forest degradation exceeded those from deforestation.

Conclusions

The results of this study clearly demonstrate the importance of accounting greenhouse gases from forest degradation by human activities. The scale of emissions presented indicates that the exclusion of forest degradation from national and international GHG accounting is distorting. This work helps identify where emissions are likely significant, but policy developments are needed to guide when and how accounting should be undertaken. Furthermore, ongoing research is needed to create and enhance cost-effective accounting approaches.

     

Estimation of aboveground biomass and carbon in a tropical rain forest in Gabon using remote sensing and GPS data

The knowledge of biomass stocks in tropical forests is critical for climate change and ecosystem services studies. This research was conducted in a tropical rain forest located near the city of Libreville (the capital of Gabon), in the Akanda Peninsula. The forest cover was stratified in terms of mature, secondary and mangrove forests using Landsat-ETM data. A field inventory was conducted to measure the required basic forest parameters and estimate the aboveground biomass (AGB) and carbon over the different forest classes. The Shuttle Radar Topography Mission (SRTM) data were used in combination with ground-based GPS measurements to derive forest heights. Finally, the relationships between the estimated heights and AGB were established and validated. Highest biomass stocks were found in the mature stands (223 ± 37 MgC/ha), followed by the secondary forests (116 ± 17 MgC/ha) and finally the mangrove forests (36 ± 19 MgC/ha). Strong relationships were found between AGB and forest heights (R² > 0.85).     

Time-series maps of aboveground biomass in dipterocarps forests of Malaysia from PALSAR and PALSAR-2 polarimetric data

Background

Malaysia typically suffers from frequent cloud cover, hindering spatially consistent reporting of deforestation and forest degradation, which limits the accurate reporting of carbon loss and CO₂ emissions for reducing emission from deforestation and forest degradation (REDD+) intervention. This study proposed an approach for accurate and consistent measurements of biomass carbon and CO₂ emissions using a single L-band synthetic aperture radar (SAR) sensor system. A time-series analysis of aboveground biomass (AGB) using the PALSAR and PALSAR-2 systems addressed a number of critical questions that have not been previously answered. A series of PALSAR and PALSAR-2 mosaics over the years 2007, 2008, 2009, 2010, 2015 and 2016 were used to (i) map the forest cover, (ii) quantify the rate of forest loss, (iii) establish prediction equations for AGB, (iv) quantify the changes of carbon stocks and (v) estimate CO₂ emissions (and removal) in the dipterocarps forests of Peninsular Malaysia.

Results

This study found that the annual rate of deforestation within inland forests in Peninsular Malaysia was 0.38% year^?1 and subsequently caused a carbon loss of approximately 9 million Mg C year^?1, which is equal to emissions of 33 million Mg CO₂ year^?1, within the ten-year observation period. Spatially explicit maps of AGB over the dipterocarps forests in the entire Peninsular Malaysia were produced. The RMSE associated with the AGB estimation was approximately 117 Mg ha^?1, which is equal to an error of 29.3% and thus an accuracy of approximately 70.7%.

Conclusion

The PALSAR and PALSAR-2 systems offer a great opportunity for providing consistent data acquisition, cloud-free images and wall-to-wall coverage for monitoring since at least the past decade. We recommend the proposed method and findings of this study be considered for MRV in REDD+?implementation in Malaysia.

     

Multispectral remote sensing for mapping grassland degradation using the key indicators of grass species and edaphic factors

Land degradation is believed to be one of the most severe and widespread environmental problems. In South Africa, large areas of land have been identified as degraded, as shown by the lower vegetation cover. One of the major causes of grassland degradation is change in plant species composition that leads to presence of unpalatable grass species. Some grass species have been successfully used as indicators of different levels of grassland degradation in the country. This paper, therefore explores the possibility of mapping grassland degradation in Cathedral Peak, South Africa, using indicators of grass species and edaphic factors. Multispectral SPOT 5 data were used to produce a grassland degradation map based on the spatial distribution of decreaser (Themeda triandra) and increaser (Hyparrhenia hirta) species. To improve mapping accuracy, soil samples were collected from each species site and analysed for nutrient content. A t-test and machine learning random forest classification algorithm were applied for variable selection and classification using SPOT 5 data and edaphic variables. Results indicated that the decreaser and increaser grass species can be mapped with modest accuracy using SPOT 5 data (overall accuracy of 75.30%, quantity disagreement = 2 and allocation disagreement = 23). The classification accuracy was improved to 88.60%, 1 and 11 for overall accuracy, quantity and allocation disagreements, respectively, when SPOT 5 bands and edaphic factors were combined. The study demonstrated that an approach based on the integration of multispectral data and edaphic variables, which increased the overall classification accuracy by about 13%, is a suitable when adopting remote sensing to monitor grassland degradation.     

利用天宫一号和Landsat7对地观测数据的森林变化检测

由于自然演替和一些干扰因素的影响,森林覆盖处在不断的变化中.结合云南省西双版纳地区的天宫一号高光谱数据以及Landsat影像,研究了热带森林覆盖制图与变化检测的自动化识别方法.首先分析了每景影像中红光波段的光谱属性,依据直方图提取出纯净森林像元,然后计算影像中各像元与纯净森林像元之间的光谱相似性,从而得到森林指数并以此为依据提取出每景影像对应的森林覆盖图,将多期的森林覆盖专题图进行叠加分析即可得到森林变化专题图.结果表明:(1)使用天宫一号高光谱影像可以进行森林覆盖自动化提取,生成的森林覆盖图合理地反映了森林分布状况;(2)与多期遥感影像结合进行森林变化信息提取,提取结果很好地体现了森林减少和森林恢复情况,对新恢复的未郁闭森林也可以进行有效检测.     

Monitoring Forest Disturbance in Lesser Khingan Mountains Using MODIS and Landsat TM Time Series from 2000 to 2011

The widespread changes in forest cover caused by climatological and anthropogenic factors can influence the forest ecosystem and climate system to a great extent. With the increasing availability of remote sensing data, monitoring of forest changes at high temporal resolution and on various scales is becoming more realistic. Though several methods based on time series data have been used to detect forest disturbance, there are few studies paying attention to boreal areas where the forest is significant in regulating the global carbon cycle and biogeophysical processes. In this paper, we present a robust method of Breaks Detection Based On Polynomial Model (BDPM) to track boreal (e.g. Lesser Khingan Mountains) deforestation and forest fires based on the MODIS and Landsat TM time series data. Compared with the previous methods, the BDPM offers the following advantages: (1) Fitting of the polynomial model using the seasonal variation of forests in the whole region instead of a single pixel to avoid error accumulation; (2) to avoid confusion between vegetation change due to climate changes and abrupt forest disturbances, we segmented the long-time NDVI series data into 12 seasonal cycles and simulated the temporal variations in each seasonal cycle.     

A satellite data driven approach to monitoring and reporting fire disturbance and recovery across boreal and temperate forests

The regular and consistent measurements provided by Earth observation satellites can support the monitoring and reporting of forest indicators. Although substantial scientific literature espouses the capabilities of satellites in this area, the techniques are under-utilised in national reporting, where there is a preference for aggregating ad hoc data. In this paper, we posit that satellite information, while perhaps of low accuracy at single time steps or across small areas, can produce trends and patterns which are, in fact, more meaningful at regional and national scales. This is primarily due to data consistency over time and space. To investigate this, we use MODIS and Landsat data to explore trends associated with fire disturbance and recovery across boreal and temperate forests worldwide. Our results found that 181 million ha (9 %) of the study area (2 billion ha of forests) was burned between 2001 and 2018, as detected by MODIS satellites. World Wildlife Fund biomes were used for a detailed analysis across several countries. A significant increasing trend in area burned was observed in Mediterranean forests in Chile (8.9 % yr⁻¹), while a significant decreasing trend was found in temperate mixed forests in China (-2.2 % yr⁻¹). To explore trends and patterns in fire severity and forest recovery, we used Google Earth Engine to efficiently sample thousands of Landsat images from 1991 onwards. Fire severity, as measured by the change in the normalized burn ratio (NBR), was found to be generally stable over time; however, a slight increasing trend was observed in the Russian taiga. Our analysis of spectral recovery following wildfire indicated that it was largely dependent on location, with some biomes (particularly in the USA) showing signs that spectral recovery rates have shortened over time. This study demonstrates how satellite data and cloud-computing can be harnessed to establish baselines and reveal trends and patterns, and improve monitoring and reporting of forest indicators at national and global scales.     

Biomass allometric equation and expansion factor for a mountain moist evergreen forest in Mozambique

Background

Worldwide, forests are an important carbon sink and thus are key to mitigate the effects of climate change. Mountain moist evergreen forests in Mozambique are threatened by agricultural expansion, uncontrolled logging, and firewood collection, thus compromising their role in carbon sequestration. There is lack of local tools for above-ground biomass (AGB) estimation of mountain moist evergreen forest, hence carbon emissions from deforestation and forest degradation are not adequately known. This study aimed to develop biomass allometric equations (BAE) and biomass expansion factor (BEF) for the estimation of total above-ground carbon stock in mountain moist evergreen forest.

Methods

The destructive method was used, whereby 39 trees were felled and measured for diameter at breast height (DBH), total height and the commercial height. We determined the wood basic density, the total dry weight and merchantable timber volume by Smalian’s formula. Six biomass allometric models were fitted using non-linear least square regression. The BEF was determined based on the relationship between bole stem dry weight and total dry weight of the tree. To estimate the mean AGB of the forest, a forest inventory was conducted using 27 temporary square plots. The applicability of Marzoli’s volume equation was compared with Smalian’s volume equation in order to check whether Marzoli’s volume from national forest inventory can be used to predict AGB using BEF.

Results

The best model was the power model with only DBH as predictor variable, which provided an estimated mean AGB of 291?±?141 Mg ha^?1 (mean?±?95% confidence level). The mean wood basic density of sampled trees was 0.715?±?0.182 g cm^?3. The average BEF was of 2.05?±?0.15 and the estimated mean AGB of 387?±?126 Mg ha^?1. The BAE from miombo woodland within the vicinity of the study area underestimates the AGB for all sampled trees. Chave et al.’s pantropical equation of moist forest did not fit to the Moribane Forest Reserve, while Brown’s equation of moist forest had a good fit to the Moribane Forest Reserve, having generated 1.2% of bias, very close to that generated by the selected model of this study. BEF showed to be reliable when combined with stand mean volume from Marzoli’s National Forestry Inventory equation.

Conclusion

The BAE and the BEF function developed in this study can be used to estimate the AGB of the mountain moist evergreen forests at Moribane Forest Reserve in Mozambique. However, the use of the biomass allometric model should be preferable when DBH information is available.

     

Predicting forest carbon stocks from high resolution satellite data in dry forests of Zimbabwe: exploring the effect of the red-edge band in forest carbon stocks estimation

In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R² ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error.     

Characterization of forests and deforestation in Cambodia using ALOS/PALSAR observation

In this study, we have demonstrated the capability of full polarimetric ALOS/Phased Array L-band Synthetic Aperture Radar data for the characterization of the forests and deforestation in Cambodia, to support climate change mitigation policies of Reducing Emission from Deforestation and Forest Degradation (REDD). We have observed mean backscattering coefficient (σ°), entropy (H), alpha angle (α), anisotropy (A), pedestal height (PH), Radar Vegetation Index (RVI) and Freeman–Durden three-component decomposition parameters. The observations show that the forest types and deforested area are showing variable polarimetric and backscattering properties because of the structural difference. Evergreen forest is characterized by a high value of σ° HV (?12.96 dB) as compared with the deforested area (σ° HV=?22.2 dB). The value of polarimetric parameters such as entropy (0.93), RVI (0.91), PH (0.41) and Freeman–Durden volume scattering (0.43) is high for evergreen forest, whereas deforested area is characterized by the low values of entropy (0.36) and RVI (0.17). Based on these parameters, it is found that σ° HV, entropy, RVI and PH provide best results among other parameters.     

Retrieval of tropical forest biomass information from ALOS PALSAR data

Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar (ALOS PALSAR) data from different observation modes were analysed to determine (1) which observation mode most accurately retrieves tropical forest biomass information and (2) whether different modes, when considered together, yield improved results in comparison to identical data-sets analysed independently. We performed regression analysis to estimate above-ground forest biomass using PALSAR backscatter data for natural and planted forests in south-eastern Bangladesh. The coefficient of determination (r ²) was lower or equal to 0.499 (n = 70) when PALSAR data from different observation modes were separately considered, but increased sharply when one class (rubber) is dropped and average backscatter of fine beam single (FBS) and polrimetric (PLR) modes are used in the analysis. The results presented in this article are useful for both regional and global forest biomass inventories and fixing acquisition modes for planned L-band SAR missions.     

Estimation of Above Ground Biomass Using Texture Metrics Derived from IRS Cartosat-1 Panchromatic Data in Evergreen Forests of Western Ghats,India

Assessment of above ground forest biomass (AGB) is essential in carbon modelling studies to provide mitigation strategies as demonstrated by reducing emissions from deforestation and forest degradation. Several researchers have demonstrated the use of remote sensing data in spatial AGB estimation, in terms of spectral and radar backscatter based approaches at a landscape scale with several known limitations. However, these methods lacked the predictive ability at high biomass ranges due to saturation. The current study addresses the problem of saturation at high biomass ranges using canopy textural metric from high resolution optical data. Fourier transform based textural ordination (FOTO) technique, which involves deriving radial spectrum information via 2D fast Fourier transform and ordination through principal component analysis was used for characterizing the textural properties of forest canopies. In the current study, plot level estimated AGB from 15 (1 ha) plots was used to relate with texture derived information from very high resolution datasets (viz., IKONOS and Cartosat-1). In addition to the estimation of high biomass ranges, one of the prime objective of the current study is to understand the effects of spatial resolution on deriving textural-AGB relationship from 2.5 m IRS Cartosat data (Cartosat-A, viewing angle = ?5°) to that of IKONOS imagery with near nadir view. Further, since texture is impacted by several illumination geometry issues, the effect of viewing geometry on the relationship was evaluated using Cartosat-F (Viewing angle = 26°) imagery. The results show that the FOTO method using stereo Cartosat (A and F) images at 2.5 m resolution are able to perform well in characterizing high AGB values since the texture-biomass relationship is only subjected to 18 % relative error to that of 15 % in case of IKONOS and could aid in reduction of uncertainty in AGB estimation at a large landscape levels.     

Supervised classification approach for assessment of forest resources in part of u.p. plains,india using landsat-3 data

Forests in the plains of Uttar Pradesh are depleted to great extent. Existing figures on the area under forest, though contradictory, indicate a grim situation of forest cover. In the present study, supervised classification technique with maximum likelihood algorithum has been used to assess the forest in the region extending between Lucknow through Allahabad to Mirzapur city in the plains of Uttar Pradesh. It has been possible to successfully identify and map 5 different categories of forests by computer processing of Landsat-3 Multispectral Scanner data. The area under each category has also been computed. Whatever little forest exists in this area is also greatly influenced by biotic interferences. The vegetation formation in these forests is thus degraded and/or secondary. Spectral behaviour of various categories of forests have also been discussed.