Forest Cover Mapping Research Articles

Оцінювання втрат лісового покриву Українських Карпат дистанційними методами за матеріалами відкритих джерел супутникової інформації

Для оцінювання втрат лісового покриву Українських Карпат на прикладі території Сколівських Бескидів використано дистанційні методи. Для території досліджень на основі аналізу цифрових моделей рельєфу виокремлено ділянки, де, відповідно до чинних інструкцій та нормативів, заборонені суцільні рубки головного користування. На таких ділянках були виявлено та проаналізовано зміни лісового покриву. Для аналізу довгострокових змін лісового покриву використано Карту глобальних змін лісу (Global Forest Change – GFC). За даними аналізу такої інформації встановлено, що у 2010 р. частка природних лісів становила 19 % від загальної площі країни, або від 60,1 млн га. За період з 2001 по 2018 рр. в Україні втрачено 958 тис. га, що відповідає 8,6 % відносно площі лісового покриву за 2000 р. Для порівняння карт змін використано знімки із супутників Sentinel2 з роздільною здатністю 10 м×pix-1 для аналізу втрат лісу за 2015-2018 рр. Розмежування вододілу проведено для досліджуваної території за допомогою інструменту SAGA "Басейни вододілу" з використанням цифрової моделі рельєфу ASTER GDEM. За допомогою інструменту QGIS розраховано стрімкість схилів на основі цифрової моделі рельєфу ASTER GDEM2. Окрім цього, обчислено середнє значення, мінімум та максимум стрімкості схилу для порівняння її із наведеними даними стрімкості в базах лісовпорядкування для кожного виділу. Для визначення площі для екорегіону Українські Карпати на території Сколівських Бескидів спочатку вирізано растрову карту змін за даними Глобальної лісової варти (Global Forest Watch – GFW) за контурами екорегіону, векторизовано растр за картою змін, а потім обчислено площі за кожною категорією змін. Розраховано площі втрат лісового покриву. Встановлено, що вища частка втрат лісового покриву припадає на 2014-2018 рр. Він істотно вищий за середній щорічна частка втрат. Також виявлено, що останніми роками втрати лісового покриву зумовлені рубками, значна частка, котрих припадає на висоту понад 1100 м н.р.м. Аналіз змін лісового покриву для території Сколівських Бескид дав змогу порівняти такі зміни в лісах різної відомчої приналежності: Національного природного парку "Сколівські Бескиди", державного підприємства "Сколівське лісове господарство" та деяких лісництв, котрі належать до юрисдикції Сколівського війського лісгоспу ДП "Івано-Франківський військовий ліспромкомбінат". Порівняння даних втрати лісового покриву показав значні обсяги втрат на території військових лісництв, які були набагато вищими, ніж на інших територіях, що свідчить про їх антропогенне походження, тобто значні обсяги рубок.

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.

Regional-Scale Forest Mapping over Fragmented Landscapes Using Global Forest Products and Landsat Time Series Classification

Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15%.

ЗАСТОСУВАННЯ ТЕХНОЛОГІЙ ГІС ТА ДЗЗ В ЗАДАЧАХ МОНІТОРИНГУ ЛІСОВИХ ПОЖЕЖ

The paper considers the problems of the forest industry, namely forest fires. Emphasis is placed on the suffering of theforests of Ukraine from large-scale fires. The main factors in reducing forest areas are forest fires. Despite the constantimplementation of preventive and precautionary fire-fighting measures, fires affect large areas of forests, which places a heavyburden on the country's budget. In addition to direct detection of fires, assessment of their power and development forecast, theurgent task is to monitor the parameters of fires: area, perimeter of the edge and radiation power of the fire, damage,quantification of vegetation changes and more. The ability to determine the areas burned during large forest fires, allows you tomake an inventory of the post-fire condition of forests. An important task of both economic and strategic nature is the study ofdynamic changes and the state of forests. Highly informative observations from artificial satellites of the Earth make it possible to quickly and objectively assess the reserves of forest resources and investigate changes in them: fires, damage assessments,reforestation in fires and deforestation, clarification of estimates of forest damage by diseases and pests, fires, identification ofcutting activities for the purpose of further control of their legality, solution of inventory problems, assessment of forest cover ofterritories, mapping of forest cover of areas and breed structure of forests. This allows to take timely measures for the rat ionaluse of forest resources and prevent damage.

Long-term forest mapping from classification of MODIS time series: best practices

Coarse spatial resolution (CSR) time series have been successfully used at regional scale to produce homogeneous and up-to-date forest cover maps. This study aims to classify CSR time series using a nomenclature as detailed as national forest inventories nomenclatures. To identify best practices for classifying time series, three algorithms are compared: maximum likelihood, support vector machine, and random forest. For each algorithm, training, temporal compositing, and selection of input features have been optimized. Results establish a clear improvement in classification accuracy when red, near-infrared, and short-wave infrared spectral bands are used instead of vegetation indices. Temporal compositing has a major impact when the whole phenological cycle is used for 3 consecutive years. Random forest produces the best classification, support vector machine appears to be sensitive to overtraining, and maximum likelihood is unable to deal with the complex characteristics of forest and natural vegetation classes.

SAR – OPTICAL REMOTE SENSING BASED FOREST COVER AND GREENNESS ESTI-MATION OVER INDIA

Abstract. Indian natural forest has a high ecological significance as it holds much biodiversity and is primarily affected due to deforestation. The present study exhibits the forest cover change on Global Forest Non-Forest (FNF) data for India and greenness trend using MOD15A2H LAI product, which is the best product available till date. JAXA uses of SAR datasets for forest classification based on FAO definitions. Later, Forest Survey of India (FSI) used different definitions for forest classification from FAO and was to compare with JAXA based forest cover. The global FNF study exhibited that total forest cover was reduced from 568249 Km2 to 534958 Km2 during 2007–17 in India. The significant loss of forest cover (33291.59 Km2; by −5.85% change) was primarily evident in Eastern Himalayas followed by Western Himalayas. Whereas forest cover increase was observed in Eastern and the Western Ghats from 2007 to 2017. The state of forest report by FSI states an increase in the forest cover from 690889 Km2 to 708273 Km2 during 2007–17 by 2.51%. The difference in forest cover as estimated by JAXA global FNF datasets and FSI report is attributed to differences in forest cover mapping definitions by both the agencies and use of varied datasets (SAR datasets by JAXA and optical datasets by FSI). It is to note that SAR is highly sensitive to forest cover and vegetation’s as compare to optical datasets. Recent satellite-based (2000–2018) LAI product reveals the increase in leaf area of vegetation during 2000–18. It may be attributed to proper human land use management and implications of green revolutions in the region. The greening in India is most evident from the croplands with insignificant contribution from forest cover.

Assessment of historical forest cover loss and fragmentation in Asian elephant ranges in India.

India is home of the largest remaining population of the Asian elephant (Elephas maximus L.) in the South and Southeast Asia. The forest loss and fragmentation is the main threat to the long-term survival of Asian elephants. In the present study, we assessed forest loss and fragmentation in the major elephant ranging provinces in India, viz., north-eastern, north-western, central, and southern since the 1930s. We quantified forest cover changes by generating and analyzing forest cover maps of 1930, 1975, and 2013, whereas fragmentation of contiguous forest areas was quantified by applying landscape metrics on the temporal forest cover maps. A total of 21.49% of the original forest cover was lost from 1930 to 1975, while another 3.19% forest cover was lost from 1975 to 2013 in the elephant ranges in India. The maximum forest loss occurred in the southern range (13,084km2) followed by north-eastern (10,188km2), central (5614km2), and north-western (4030km2) elephant ranges in the past eight decades. The forests in the central range were the most fragmented followed by southern, north-eastern, and north-western elephant ranges. The forest fragmentation in the southern range occurred at the fastest rate than central, north-eastern, and north-western ranges. The core forest areas shrunk by 39.6% from 1930 to 2013. The causative factors of forest change and situation of elephant-human conflict have been discussed. Study outcomes would be helpful in planning effective conservation strategies for Asian elephants in India.

Assessing the Importance of Tree Cover Threshold for Forest Cover Mapping Derived from Global Forest Cover in Myanmar

Comprehensive forest cover mapping is essential for making policy and management decisions. However, creating a forest cover map from raw remote sensing data is a barrier for many users. Here, we investigated the effects of different tree cover thresholds on the accuracy of forest cover maps derived from the Global Forest Change Dataset (GFCD) across different ecological zones in a country-scale evaluation of Myanmar. To understand the effect of different thresholds on map accuracy, nine forest cover maps having thresholds ranging from 10% to 90% were created from the GFCD. The accuracy of the forest cover maps within each ecological zone and at the national scale was assessed. The overall accuracies of ecological zones other than tropical rainforest were highest when the threshold for tree cover was less than 50%. The appropriate threshold for tropical rainforests was 80%. Therefore, different optimal tree cover thresholds were required to achieve the highest overall accuracy depending on ecological zones. However, in the unique case of Myanmar, we were able to determine the threshold across the whole country. We concluded that the threshold for tree cover for creating a forest cover map should be determined according to the areal ratio of ecological zones determined from large-scale monitoring. Our results are applicable to tropical regions having similar ecological zones.

JAXA Annual Forest Cover Maps for Vietnam during 2015–2018 Using ALOS-2/PALSAR-2 and Auxiliary Data

Monitoring the temporal changes of forests is important for sustainable forest management. In this study, we investigated the potential of using multi-temporal synthetic aperture radar (SAR) images for mapping annual change in forest cover at a national scale. We assessed the robustness of using multi-temporal Phased Array L-band Synthetic Aperture Radar-2/Scanning Synthetic Aperture Radar (PALSAR-2/ScanSAR) mosaic images for forest mapping by comparison with single-temporal PALSAR-2 mosaic images for three test sites in North, Central, and Southern Vietnam. We then used a combination of multi-temporal PALSAR-2/ScanSAR images, multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) images, and Shuttle Radar Topography Mission (SRTM) images to map annual forest cover for mainland Vietnam during 2015–2018. Average overall accuracies of our forest/non-forest (FNF) maps (86.6% ± 3.1%) were greater than recent maps of Japan Aerospace Exploration Agency (JAXA, (77.5% ± 3.2%)) and European Space Agency (ESA, (85.4% ± 1.6%)). Our estimates of mainland Vietnam’s forest area were close to that of the Vietnamese government. A comparison of the spatial distribution of forest estimated from JAXA and ESA FNF maps showed that our FNF map in 2015 agreed relatively well with the ESA map, with 77% of pixels being consistent. This study demonstrates the merit of using multi-temporal PALSAR-2/ScanSAR images for annual forest mapping at a national scale.

Deriving a Forest Cover Map in Kyrgyzstan Using a Hybrid Fusion Strategy

Forests have potential economic value and play a significant role in maintaining ecological balance. Considering its outdated and incomplete forest statistics, the Kyrgyzstan Republic urgently needs a forest cover map for assessing its current forest resources and assisting national policies on improving rural livelihood and sustainability. This study adopted a hybrid fusion strategy to develop a forest cover map for the Kyrgyzstan Republic with improved accuracy. The fusion strategy uses the merits of the GlobeLand30 in 2010 and the USGS TreeCover2010, the benefits of auxiliary geographic information, and the advantages of the stacking learning method in classification. Additionally, we explored the influence of different forest definitions, based on the tree cover percentage value in the USGS TreeCover2010, on the accuracy of forest cover. Results suggested that the accuracy of our model can be improved significantly by including auxiliary geographic features and feeding the optimal size of training samples. Thereafter, using our model, forest cover maps were derived at different tree cover threshold values in the USGS TreeCover2010. Importantly, the forest cover map at the tree cover threshold value of 40% was determined as the most accurate one with the kappa value of 0.89, whose spatial extent constitutes about 2.4% of the entire territory. This estimated forest cover percentage suggests a low estimation of forest resources based on rigorous definition, which can be valuable for reviewing and amending the current national forest policies.

Identifying Biases in Global Tree Cover Products: A Case Study in Costa Rica

Global tree cover products are widely used in analyses of deforestation, fragmentation, and connectivity, but are rarely critically assessed. Inaccuracies in these products could have consequences for future decision making, especially in data-poor regions like the tropics. In this study, potential biases in global and regional tree cover products were assessed across a diverse tropical country, Costa Rica. Two global tree cover products and one regional national forest cover map were evaluated along biophysical gradients in elevation, precipitation, and agricultural land cover. To quantify product accuracy and bias, freely available high-resolution imagery was used to validate tree and land cover across these gradients. Although the regional forest cover map was comparable in accuracy to a widely-used global forest map (the Global Forest Change of Hansen et al., also known as the GFC), another global forest map (derived from a cropland dataset) had the highest accuracy. Both global and regional forest cover products showed small to severe biases along biophysical gradients. Unlike the regional map, the global GFC map strongly underestimated tree cover (>10% difference) below 189 mm of precipitation and at elevations above 2000 m, with a larger bias for precipitation. All map products misclassified agricultural fields as forest, but the GFC product particularly misclassified row crops and perennial erect crops (banana, oil palm, and coffee), with maximum tree cover in agricultural fields of 89%–100% across all crops. Our analysis calls into further question the utility of the GFC product for global forest monitoring outside humid regions, indicating that, in tropical regions, the GFC product is most accurate in areas with high, aseasonal rainfall, low relief, and low cropland area. Given that forest product errors are spatially distributed along biophysical gradients, researchers should account for these spatial biases when attempting to analyze or generate forest map products.

Quantifying Impacts of National-Scale Afforestation on Carbon Budgets in South Korea from 1961 to 2014

Forests play an important role in regulating the carbon (C) cycle. The main objective of this study was to quantify the effects of South Korean national reforestation programs on carbon budgets. We estimated the changes in C stocks and annual C sequestration in the years 1961–2014 using Korea-specific models, a forest cover map (FCM), national forest inventory (NFI) data, and climate data. Furthermore, we examined the differences in C budgets between Cool forests (forests at elevations above 700 m) and forests in lower-altitude areas. Simulations including the effects of climate conditions on forest dynamics showed that the C stocks of the total forest area increased from 6.65 Tg C in 1961 to 476.21 Tg C in 2014. The model developed here showed a high degree of spatiotemporal reliability. The mean C stocks of the Cool forests and other forests increased from 4.03 and 0.43 Mg C ha−1, respectively, to 102.43 and 73.76 Mg C ha−1 at a rate of 1.82 and 1.36 Mg C ha−1 yr−1 during the same period. These results imply that, although the total Cool forest area of South Korea occupied only about 12.3% (772,788 ha) of the total forest area, the Cool forests play important roles in C balances and forest ecosystems in South Korea. Annual C sequestration totals are projected to decrease at a low rate in the near future because the overall growth rate of a mature forest decreases as the stand ages. Our results quantified forest C dynamics in South Korean forests before and after national reforestation programs. Furthermore, our results can help in development of regional and national forest management strategies to allow for sustainable development of society and to cope with climate change in South Korea.

Mapping tropical dry forest age using airborne waveform LiDAR and hyperspectral metrics

Tropical dry forest (TDF) regeneration has been extensively characterized as three deterministic successional stages, i.e., early, intermediate, and late, for the past few decades. This deterministic definition, however, ignores many biophysical and biochemical processes in the forest regeneration. This study mapped a second TDF as a function of regeneration age at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site, Costa Rica, using an airborne full-waveform LiDAR (Laser Vegetation Imaging Sensor or LVIS), a hyperspectral dataset (Hyperspectral MAPper or HyMap) and three advanced machine learning methods. We defined five age groups (0–10 years, 10–20 years, 20–30 years, 30–50 years, 50 + years) based on historical forest cover maps, and analyzed their effective LiDAR waveforms and cumulative return energy curves (derived from LVIS) and their reflectance (derived from the HyMap). Then, nine LVIS metrics and eleven HyMap indices were calculated and their abilities to differentiate the age groups were evaluated using Multiple Comparison Analysis (MCA). We found that six of the LVIS metrics which describe the vertical structure of the forests can significantly differentiate all age groups. None of HyMap metrics can differentiate all age groups, but some of them can identify certain age groups. Selected LVIS metrics and HyMap indices were used to map TDF age, through Support Vector Machine, Artificial Neural Network and Random Forest (RF). We found LVIS plus HyMap metrics generally produced more accurate forest age maps than either LVIS metrics or HyMap indices and RF better performed than other two classifiers. We finally proposed a method to synthesize different forest age maps into one age map which had the highest accuracy for all age groups. Our study highlighted the importance to consider the forest regeneration as a continuous stochastic phenomenon, and also highlighted the advantages to incorporate multiple remote sensing techniques to describe the forest regeneration. Our method to synthesize the forest age map can also benefit other researchers who need to take advantage of multiple mapping results.

The relationship between forests and freshwater fish consumption in rural Nigeria.

Nigerians depend on fish for maintaining diverse and healthy diets. Fish are a key source of protein and micronutrients, both of which are important for healthy diets. Some research has shown that forests provide important ecosystem functions that support the productive capacity and sustainability of inland fisheries. Our study aims to empirically assess the relationship between forest cover around rivers and fish consumption. We use data from the Living Standards Measurement Survey (LSMS) and spatially merge household and village data with forest cover and river maps. We estimate the relationship between forest cover around rivers and average village fresh fish consumption, while also accounting for other socio-economic and geographical determinants. We find that that the density of forest cover around rivers is positively and significantly correlated with village consumption of fresh fish. Our results suggest that forests influence the consumption of fresh fish by improving the productivity of inland fisheries and increasing the availability of fish. Aquatic habitats tend to be overlooked in debates on land use and food production, and yet can be critically important sources of nutrient-rich foods that are limited in rural diets in developing countries, particularly for the poor. Clearing forests for agriculture in order to produce more agricultural crops might have the unintended consequence of reducing another important food source.

Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination

Image classification and interpretation are greatly aided through the use of image segmentation. Within the field of environmental remote sensing, image segmentation aims to identify regions of unique or dominant ground cover from their attributes such as spectral signature, texture and context. However, many approaches are not scalable for national mapping programmes due to limits in the size of images that can be processed. Therefore, we present a scalable segmentation algorithm, which is seeded using k-means and provides support for a minimum mapping unit through an innovative iterative elimination process. The algorithm has also been demonstrated for the segmentation of time series datasets capturing both the intra-image variation and change regions. The quality of the segmentation results was assessed by comparison with reference segments along with statistics on the inter- and intra-segment spectral variation. The technique is computationally scalable and is being actively used within the national land cover mapping programme for New Zealand. Additionally, 30-m continental mosaics of Landsat and ALOS-PALSAR have been segmented for Australia in support of national forest height and cover mapping. The algorithm has also been made freely available within the open source Remote Sensing and GIS software Library (RSGISLib).

Using Forest Cover Maps and Local People’s Perceptions to Evaluate the Effectiveness of Community-based Ecotourism for Forest Conservation in Chambok (Cambodia)

SummaryIncreasing attention has been given to evaluating the effectiveness of forest conservation projects, but it is not well known whether different methods yield similar results when evaluating changes in forest resources. The present study compares forest cover maps and local people’s perceptions for evaluating the effectiveness of the Chambok community-based ecotourism (CBET) programme in Cambodia. We assessed forest cover changes from 2000 to 2012 using published global maps and used a covariate matching method to compare forest sites in CBET and non-CBET areas. We also analysed local people’s perceptions of forest resource changes by interviewing 174 households. The forest cover maps showed that the Chambok CBET was effective at reducing deforestation, although the outcome was not completely robust to unobserved heterogeneity. Local people’s perceptions concurred with the effectiveness observed in the forest cover maps, in that 64% of the people perceived that forest resources increased and 75% thought that the local community could protect its own forest resources. We conclude that the Chambok CBET performed effectively for forest conservation and suggest that mixed-method approaches are essential for evaluating the effectiveness of conservation programmes.

The assessment of forest cenotic diversity and its dynamics in Moscow region reference landscapes based on remote sensing data

«Вестник Санкт-Петербургского университета. Науки о Земле» — научно-теоретический рецензируемый журнал, обобщающий результаты исследований в области геологии, гидрогеологии и геохимии, петрологии и экологии, ландшафтного планирования и геоморфологии, экономической и социальной географии.

Mapping Forest Cover in Northeast China from Chinese HJ-1 Satellite Data Using an Object-Based Algorithm.

Forest plays a significant role in the global carbon budget and ecological processes. The precise mapping of forest cover can help significantly reduce uncertainties in the estimation of terrestrial carbon balance. A reliable and operational method is necessary for a rapid regional forest mapping. In this study, the goal relies on mapping forest and subcategories in Northeast China through the use of high spatio-temporal resolution HJ-1 imagery and time series vegetation indices within the context of an object-based image analysis and decision tree classification. Multi-temporal HJ-1 images obtained in a single year provide an opportunity to acquire phenology information. By analyzing the difference of spectral and phenology information between forest and non-forest, forest subcategories, decision trees using threshold values were finally proposed. The resultant forest map has a high overall accuracy of 0.91 ± 0.01 with a 95% confidence interval, based on the validation using ground truth data from field surveys. The forest map extracted from HJ-1 imagery was compared with two existing global land cover datasets: GlobCover 2009 and MCD12Q1 2009. The HJ-1-based forest area is larger than that of MCD12Q1 and GlobCover and more closely resembles the national statistics data on forest area, which accounts for more than 40% of the total area of the Northeast China. The spatial disagreement primarily occurs in the northern part of the Daxing’an Mountains, Sanjiang Plain and the southwestern part of the Songliao Plain. The compared result also indicated that the forest subcategories information from global land cover products may introduce large uncertainties for ecological modeling and these should be cautiously used in various ecological models. Given the higher spatial and temporal resolution, HJ-1-based forest products could be very useful as input to biogeochemical models (particularly carbon cycle models) that require accurate and updated estimates of forest area and type.

Optical and radar remote sensing data for forest cover mapping in Peninsular Malaysia

This study aims to map forest cover in Peninsular Malaysia using satellite images as deforestation is of concern in the recent decades, and is an important environmental issue for the future too. The Carnegie Landsat Analysis System‐Lite (CLASlite) program was used in this study to detect forest cover in Peninsular Malaysia using Landsat satellite data. The results of the study show that CLASlite algorithm misclassified some oil palm, rubber and urban areas as forest vegetation. A reliable forest cover map was produced by first combining Landsat and ALOS PALSAR images to identify oil palm, rubber and urban areas, and then subsequently removing them. The HH and HV polarization data of ALOS PALSAR (threshold method) could detect oil palm plantations with 85.26 per cent of overall accuracy. For urban area detection, Enhance Build up Index (EBBI) using spectral bands from Landsat provided higher overall accuracy of 94 per cent. These methods produced a forest cover reading of 5 914 421 ha with an overall classification accuracy of 94.5 per cent. The forest cover (including rubber areas) detected in this study is 0.38 per cent higher than the percentage of 2010 forest cover detected by the Forestry Department of Peninsular Malaysia. The technique described in this paper presents an alternative and viable approach for updating forest cover maps in Malaysia.

Méthodologie De Production Des Données Cartographiques Sur Le Couvert Forestier En République Du Congo, De 2000 À 2012

A forest-based mapping data production study was conducted in the Republic of Congo as part of the Reducing Emissions from Deforestation and Forest Degradation (REDD) process including sustainable forest management, forest conservation, and forest degradation. biodiversity and increasing carbon stocks (REDD +). The purpose of this study was to produce a forest cover map and cover changes for the period 2000 to 2012, which meets the criteria of the national definition of forest adopted by the Republic of Congo and the evaluation of forest areas. and losses. The map produced was based on the combination of forest map parameters developed prior to the adoption of the forest definition criteria. The 2000 and 2010 Landsat image segmentation was used to create polygons that respect the definition and in which the values of the underlying maps were injected, through a decision tree transcribed in the R language and implemented in conjunction with the tools Open Foris Tools Kit. The results indicate an overall accuracy of 90% of the map without stratification, with greater accuracy for the forest (86%) than for the losses (73%). The forest occupies 23,517,000 hectares, representing 69% of the national territory. The forest area lost during this period was estimated at about 145,356 hectares, which represents an annual average of 12,113 hectares (0.052%). These results lead to the conclusion that the Republic of Congo is one of the countries with high forest cover and low deforestation.