海岛岸线遥感立体测图精细测量方法

提出了一种海岛岸线遥感测图精细测量新方法,该方法直接基于理论定义的海岸线,利用航空影像瞬时水涯线数据在立体测图环境中提取瞬时水位高程;利用海岛周边精密海潮模型和瞬时水位高程推算海岛岸线高程;最后依据海岛岸线高程,采用立体测图方法测制海岛岸线的平面位置。该方法确保了海岛岸线成果的唯一性和连续性,适合大比例尺的大陆海岸线和海岛岸线测量。测试结果显示,在较高精度海潮模型和海面地形支持下,海岛岸线高程精度优于0.2 m,可满足1：2000测图要求。     

Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns

To support the adoption of precision agricultural practices in horticultural tree crops, prior research has investigated the relationship between crop vigour (height, canopy density, health) as measured by remote sensing technologies, to fruit quality, yield and pruning requirements. However, few studies have compared the accuracy of different remote sensing technologies for the estimation of tree height. In this study, we evaluated the accuracy, flexibility, aerial coverage and limitations of five techniques to measure the height of two types of horticultural tree crops, mango and avocado trees. Canopy height estimates from Terrestrial Laser Scanning (TLS) were used as a reference dataset against height estimates from Airborne Laser Scanning (ALS) data, WorldView-3 (WV-3) stereo imagery, Unmanned Aerial Vehicle (UAV) based RGB and multi-spectral imagery, and field measurements. Overall, imagery obtained from the UAV platform were found to provide tree height measurement comparable to that from the TLS (R² = 0.89, RMSE = 0.19 m and rRMSE = 5.37 % for mango trees; R² = 0.81, RMSE = 0.42 m and rRMSE = 4.75 % for avocado trees), although coverage area is limited to 1–10 km² due to battery life and line-of-sight flight regulations. The ALS data also achieved reasonable accuracy for both mango and avocado trees (R² = 0.67, RMSE = 0.24 m and rRMSE = 7.39 % for mango trees; R² = 0.63, RMSE = 0.43 m and rRMSE = 5.04 % for avocado trees), providing both optimal point density and flight altitude, and therefore offers an effective platform for large areas (10 km²–100 km²). However, cost and availability of ALS data is a consideration. WV-3 stereo imagery produced the lowest accuracies for both tree crops (R² = 0.50, RMSE = 0.84 m and rRMSE = 32.64 % for mango trees; R² = 0.45, RMSE = 0.74 m and rRMSE = 8.51 % for avocado trees) when compared to other remote sensing platforms, but may still present a viable option due to cost and commercial availability when large area coverage is required. This research provides industries and growers with valuable information on how to select the most appropriate approach and the optimal parameters for each remote sensing platform to assess canopy height for mango and avocado trees.     

Shoreline and coral reef ecosystem changes in gulf of Mannar, Southeast coast of India

Changes in shoreline, coral reef and seafloor have been mapped using remote sensing satellite data of IRS LISS-III (1998), IRS LISS-II (1988), Survey of India Topographic sheet (1969), Naval Hydrographic Chart (NHO) 1975 and bathymetry data (1999) with ARC-INFO and ARC-VIEW GIS. The analysis of multi-date shoreline maps showed that 4.34 and 23.49 km2 of the mainland coast and 4.14 and 3.31 km² areas of island coast have been eroded and accreted, respectively, in the Gulf of Mannar. The analysis of multi-date coral reef maps showed that 25.52 km² of reef area and 2.16 km² of reef vegetation in Gulf of Mannar have been lost over a period of ten years. The analysis of multi-date bathymetry data indicates that the depth of seafloor has decreased along the coast and around the islands in the study area. The average reduction of depth in seafloor has been estimated as 0.51m over a period of twenty four years. The increased suspended sediment concentration due to coastal and island erosion, and raised reef due to emerging of coast by tectonic movement are responsible for coral reef degradation in the Gulf of Mannar. Validation by ground truth has confirmed these results.     

Spatial modeling using high resolution image for future shoreline prediction along Junput Coast,West Bengal,India

National policies and legal decisions are very much dependent on the position of the shoreline. Shoreline change rates are frequently employed to summarize historical shoreline movements. This also helps to predict the future position of the shoreline based on the perceived historical trends. In this regard, the future shoreline positions at both the long-term, that is 2050, and short-term, that is 2015, time interval was predicted using the End Point Rate (EPR) model along the Junput Coast of West Bengal, India. The whole project work was divided into five parts. The first part showed the detection of shoreline from satellite data like IRS LISS Ⅳ and Landsat 7 ETM+ and from the Survey of India Toposheet. The second part gave the glimpse of the dynamic segmentation of the shoreline to get the dynamically segmented nodal points along the shoreline. Shoreline prediction for the years 2015 and 2050 using End Point Rate (EPR) model was done in the third part. In the fourth part, Coastal Terrain Model (CTM) was prepared, and the digital shoreline estimated. The model result was validated and accuracy assessed with respect to the GPS data collected from the field at the fifth stage. Finally at the end of the present work, limitations of the project and the future scope of the work was sited.     

Shoreline Change Analysis at Chilika Lagoon Coast,India Using Digital Shoreline Analysis System

Shoreline is the dynamic interfaces of both terrestrial and marine environment, which constantly affected by natural coastal processes includes wave, tide, littoral drift and cyclonic storms as well as coastal development. Wave induced littoral drift and fluvial discharge causing the gradual inlet migration and has the concurrent impact on shoreline of Chilika lagoon. This study is to determine the long-term shoreline changes along the coast of Chilika lagoon. Historical satellite images were used to analyse the shoreline erosion and accretion based on statistical approach. The satellite data from 1975 to 2015 were processed by using ERDAS Imagine and the shorelines are extracted. The shoreline oscillation was analysed at an interval of 100 m along the coast of Chilika lagoon using DSAS software. Most commonly used statistical methods such as end point rate and linear regression rate are used. The shoreline change analysis for entire coast of the lagoon since 40 years (1975–2015) indicates that 62% is of accretion, 25% is under stable coast and erosion is 13%. The result reveals that the lagoon coast shows high accretion of 9.12 m/year at updrift side of the lagoon inlet whereas the downdrift side shows high erosion of ??10.73 m/year due to the wave induced northeasterly longshore sediment transport round the year and riverine discharge. This study would provide the potential erosion and accretion area at Chilika lagoon coast and would help in adaptive shoreline management plan.     

Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery

Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing ‘optimal segmentation’. Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved.     

2000～2009年江苏沿海海岸线变迁与滩涂围垦分析

利用2000年3月1日、2003年2月6日、2006年9月18日和2009年10月17日4个时相的遥感影像对江苏盐城-南通段海岸线多年来的变迁进行了连续监测,获取了每个时期发生变化岸段、滩涂围垦面积等信息。研究结果表明:利用遥感影像能够满足准确、快速监测海岸线变迁的要求;2006～2009年,江苏省海岸线快速向海域推进;2000～2003年滩涂围垦面积为25 213ha,2003～2006年新增陆地面积为9601 ha,2006～2009年围垦面积达到23 632ha;滩涂开发总体呈现围垦—修养—围垦的开发模式。     

福州市热岛效应动态分析研究

应用 1988年和 1992年福州市卫星图像 ,对福州市热岛效应进行动态研究 ,结果表明 :随着城市建设的发展 ,原热岛范围内高温区增加 ,热岛范围明显扩大 ,尤其是市区东部施展最快 ,在 1988～ 1992这 4年中福州的热岛范围向东扩展了约 15km2 ,同时热岛外围小热岛也不断涌现。在这些动态变化特征的基础上 ,本文分析了福州市热岛效应动态变化的原因以及减轻热岛效应影响的具体措施。     

Monitoring early stage invasion of exotic Spartina alterniflora using deep-learning super-resolution techniques based on multisource high-resolution satellite imagery: A case study in the Yellow River Delta,China

Over the past decades, Spartina alterniflora, one of the top exotic invasive plants in China, has expanded throughout coastal China. In the Yellow River Delta (YRD), the rapid expansion of S. alterniflora has caused serious negative ecological effects. Current studies have concentrated primarily on mapping the distribution of S. alterniflora with medium-resolution satellite imagery at the regional or landscape scale, which have a limited capability in early detection and monitoring of the invasive process at the patch scale. In this study, we proposed a framework for monitoring the early stage invasion of S. alterniflora patches in the YRD using multiyear multisource high-spatial-resolution satellite imagery with various ground sampling distances (WorldView-2, SPOT-6, GaoFen-1, GaoFen-2, and GaoFen-6 from 2012 to 2019). First, we proposed to use deep-learning-based image super-resolution models to enhance all images to submeter (0.5 m) resolution. Then, we adopted stepwise evolution analysis-based image segmentation and object-based classification rules to detect and delineate S. alterniflora patches from the super-resolved imagery. By investigating Super-Resolution Convolutional Neural Networks (SRCNN) and Fast Super-Resolution Convolutional Neural Networks (FSRCNN) and comparing these methods with the conventional bicubic interpolation method for image resolution enhancement, we concluded that FSRCNN was superior in constructing spectral and structural details from the 1 m/1.5 m/2 m resolution images to 0.5 m resolution. FSRCNN, in particular, was more effective and efficient in discerning and estimating the size of small S. alterniflora patches (<50 m²). Using our method, 76 of 83 field-measured small patches were accurately detected and the delineated S. alterniflora patch perimeters agreed well with the field-measured patch perimeters (root mean square error [RMSE] = 8.29 m, mean absolute percentage error [MAPE] = 23.46 %). The invasion process showed fast expansion from 2012 to 2015 and slow growth from 2016 to 2019. We observed that the landward limits of S. alterniflora patches were influenced by elevation and vicinity to tidal creeks.     

Automated mapping of impervious surfaces in urban and suburban areas: Linear spectral unmixing of high spatial resolution imagery

Quantifying impervious surfaces in urban and suburban areas is a key step toward a sustainable urban planning and management strategy. With the availability of fine-scale remote sensing imagery, automated mapping of impervious surfaces has attracted growing attention. However, the vast majority of existing studies have selected pixel-based and object-based methods for impervious surface mapping, with few adopting sub-pixel analysis of high spatial resolution imagery. This research makes use of a vegetation-bright impervious-dark impervious linear spectral mixture model to characterize urban and suburban surface components. A WorldView-3 image acquired on May 9th, 2015 is analyzed for its potential in automated unmixing of meaningful surface materials for two urban subsets and one suburban subset in Toronto, ON, Canada. Given the wide distribution of shadows in urban areas, the linear spectral unmixing is implemented in non-shadowed and shadowed areas separately for the two urban subsets. The results indicate that the accuracy of impervious surface mapping in suburban areas reaches up to 86.99%, much higher than the accuracies in urban areas (80.03% and 79.67%). Despite its merits in mapping accuracy and automation, the application of our proposed vegetation-bright impervious-dark impervious model to map impervious surfaces is limited due to the absence of soil component. To further extend the operational transferability of our proposed method, especially for the areas where plenty of bare soils exist during urbanization or reclamation, it is still of great necessity to mask out bare soils by automated classification prior to the implementation of linear spectral unmixing.     

Measuring Historical Coastal Change using GIS and the Change Polygon Approach

This study compares two automated approaches, the transect‐from‐baseline technique and a new change polygon method, for quantifying historical coastal change over time. The study shows that the transect‐from‐baseline technique is complicated by choice of a proper baseline as well as generating transects that intersect with each other rather than with the nearest shoreline. The change polygon method captures the full spatial difference between the positions of the two shorelines and average coastal change is the defined as the ratio of the net area divided by the shoreline length. Although then change polygon method is sensitive to the definition and measurement of shoreline length, the results are more invariant to parameter changes than the transect‐from‐baseline method, suggesting that the change polygon technique may be a more robust coastal change method.     

Climate Change and Sea-Level Rise: Impact On Agriculture Along Andhra Pradesh Coast—A Geomatics Analysis

Climate change due to anthropogenic forcing through escalating greenhouse gas emissions and destruction of carbon sinks by deforestation is leading to floods and droughts affecting agriculture production. Global warming induces steric as well as eustatic rise in sea-level, by thermal expansion and addition of ice-melt water, respectively. Although the IPCC (2007) estimated a maximum possible sea-level rise of about 59 cm, more recent estimates show a global average rise of ≥1 m by the 2100 AD. The low-lying coastal zones are more vulnerable to rising sea levels as they face submergence or saltwater intrusion which affects the agriculture activities. Geomatics-based models on the possible impact of the predicted sea-level rise on coastal agriculture are necessary to initiate appropriate mitigation plans. The present study is an attempt in this direction taking the Andhra Pradesh (AP) coast as an example. The land use / land cover of the AP coast was mapped through the interpretation of IRS-P6 LISS III imagery from 2008. SRTM digital elevation models coupled with landform evidences have been used to interpolate contours at 0.5 m interval, although highly approximate, for the entire coastal region. If the sea level rises by 1.0 m, about 4040 km² area including the present intertidal wetlands as well as the land between the present and future high tide lines would be affected along the entire 1030-km-long AP coast displacing about 1.67 million inhabitants and their economic activities, in about 351 revenue villages. The low-lying Krishna-Godavari delta region in the central part of the AP coast would be the worst affected zone as 2205 km² of its area including about 1593 km² under various types of agricultural activities is lying within the future high tide limit of 2.5 m elevation.     

Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications: Dukuduku forest patch as case study

Indigenous forest biome in South Africa is highly fragmented into patches of various sizes (most patches < 1 km²). The utilization of timber and non-timber resources by poor rural communities living around protected forest patches produce subtle changes in the forest canopy which can be hardly detected on a timely manner using traditional field surveys. The aims of this study were to assess: (i) the utility of very high resolution (VHR) remote sensing imagery (WorldView-2, 0.5–2 m spatial resolution) for mapping tree species and canopy gaps in one of the protected subtropical coastal forests in South Africa (the Dukuduku forest patch (ca.3200 ha) located in the province of KwaZulu-Natal) and (ii) the implications of the map products to forest conservation. Three dominant canopy tree species namely, Albizia adianthifolia, Strychnos spp. and Acacia spp., and canopy gap types including bushes (grass/shrubby), bare soil and burnt patches were accurately mapped (overall accuracy = 89.3 ± 2.1%) using WorldView-2 image and support vector machine classifier. The maps revealed subtle forest disturbances such as bush encroachment and edge effects resulting from forest fragmentation by roads and a power-line. In two stakeholders’ workshops organised to assess the implications of the map products to conservation, participants generally agreed amongst others implications that the VHR maps provide valuable information that could be used for implementing and monitoring the effects of rehabilitation measures. The use of VHR imagery is recommended for timely inventorying and monitoring of the small and fragile patches of subtropical forests in Southern Africa.     

Mapping the expansion of galamsey gold mines in the cocoa growing area of Ghana using optical remote sensing

Artisanal gold mining (galamsey) and cocoa farming are essential sources of income for local populations in Ghana. Unfortunately the former poses serious threats to the environment and human health, and conflicts with cocoa farming and other livelihoods. Timely and spatially referenced information on the extent of galamsey is needed to understand and limit the negative impacts of mining. To address this, we use multi-date UK-DMC2 satellite images to map the extent and expansion of galamsey from 2011 to 2015. We map the total area of galamsey in 2013 over the cocoa growing area, using k-means clustering on a cloud-free 2013 image with strong spectral contrast between galamsey and the surrounding vegetation. We also process a pair of hazy images from 2011 and 2015 with Multivariate Alteration Detection to map the 2011–2015 galamsey expansion in a subset, labelled the change area. We use a set of visually interpreted random sample points to compute bias-corrected area estimates. We also delineate an indicative impact zone of pollution proportional to the density of galamsey, assuming a maximum radius of 10 km. In the cocoa growing area of Ghana, the estimated total area of galamsey in 2013 is 27,839 ha with an impact zone of 551,496 ha. In the change area, galamsey has more than tripled between 2011 and 2015, resulting in 603 ha of direct encroachment into protected forest reserves. Assuming the same growth rate for the rest of the cocoa growing area, the total area of galamsey in 2015 is estimated at 43,879 ha. Galamsey is developing along most of the river network (Offin, Ankobra, Birim, Anum, Tano), with downstream pollution affecting both land and water.     

Automated detection of slum area change in Hyderabad,India using multitemporal satellite imagery

This paper presents an approach to automated identification of slum area change patterns in Hyderabad, India, using multi-year and multi-sensor very high resolution satellite imagery. It relies upon a lacunarity-based slum detection algorithm, combined with Canny- and LSD-based imagery pre-processing routines. This method outputs plausible and spatially explicit slum locations for the whole urban agglomeration of Hyderabad in years 2003 and 2010. The results indicate a considerable growth of area occupied by slums between these years and allow identification of trends in slum development in this urban agglomeration.     

Monitoring intra and inter annual dynamics of forest degradation from charcoal production in Southern Africa with Sentinel – 2 imagery

The contribution of forest degradation to changes in forest carbon stocks remains poorly quantified and constitutes a main source of uncertainty in the forest carbon budget. Charcoal production is a major source of forest degradation in sub-Saharan Africa. We used multitemporal Sentinel-2 imagery to monitor and quantify forest degradation extent in the main supplying area of a major urban center of southern Africa over a 4-year period. We implemented an indirect approach combining Sentinel-2 imagery to map kiln and field measurements to estimate AGB removals and carbon losses from charcoal production. This work generated 10 m resolution maps of forest degradation extent from charcoal production in the study area at quarterly intervals from 2016–2019. These maps reveal an intense and rapid forest degradation process and expose the spatial and temporal patterns of forest degradation from charcoal production with high detail. The total area under charcoal production over the study period reached 26,647 ha (SD = 320.8) and the forest degradation front advanced 10.5 km in a 4-year period, with an average of 19.4 ha of woodlands degraded daily. By the end of 2019, charcoal production disturbed most mopane stands in the study area and woodland fragmentation increased in 70.4 % of the mopane woodlands. We estimated that charcoal production was responsible for 2,568,761 Mg (SD = 42,130) of aboveground biomass extracted from the forest and 1,284,381 Mg (SD = 21,075) of carbon loss. The magnitude of these figures underlines the relevance of charcoal production as a main cause of forest cover change and remarks the existing uncertainties in the quantification of forest degradation processes. These results illustrate the potential of multitemporal medium resolution imagery to quantify forest degradation in sub-Saharan Africa and improve REDD + Monitoring, Reporting, and Verification systems in compliance with international reporting commitments.     

Evaluation of the spatial changes in seagrass cover in the lagoons of Lakshadweep islands,India, using IRS LISS III satellite images

A study was conducted in Lakshadweep islands to determine the feasibility of using Indian Remote Sensing (IRS) satellites for detecting changes in the seagrass from other coastal features. IRS ID and IRS P6 LISS III having spatial resolution of 23.5 m with lower cost compared to all other contemporary satellites with the same spatial resolution have not been widely used for monitoring the changes in seagrass cover. In this context, the present study attempted to explore the effectiveness of LISS III data for mapping seagrasses and to inform the international community about the usefulness of these low-cost imageries for coastal resource monitoring. Supervised classification and change detection studies found a significant decrease in seagrass cover of 73.03 ha in the Lakshadweep group of islands. An overall accuracy of 67.5% was obtained for the change maps, and seagrass cover and its changes vary at different islands.     

Resolution vs. image quality in pre-tsunami imagery used for tsunami impact models in Aceh,Indonesia

Land cover roughness coefficients (LCRs) have been used in multivariate spatial models to test the mitigation potential of coastal vegetation to reduce impacts of the 2004 tsunami in Aceh, Indonesia. Previously, a Landsat 2002 satellite imagery was employed to derive land cover maps, which were then combined with vegetation characteristics, i.e., stand height, stem diameter and planting density to obtain LCRs. The present study tested LCRs extracted from 2003 and 2004 Landsat (30 m) images as well as a combination of 2003 and 2004 higher spatial resolution SPOT (10 m) imagery, while keeping the previous vegetation characteristics. Transects along the coast were used to extract land cover, whenever availability and visibility allowed. These new LCRs applied in previously developed tsunami impact models on wave outreach, casualties and damages confirmed previous findings regarding distance to the shoreline as a main factor reducing tsunami impacts. Nevertheless, the models using the new LCRs did not perform better than the original one. Particularly casualties models using 2002 LCRs performed better (δAIC > 2) than the more recent Landsat and SPOT counterparts. Cloud cover at image acquisition for Landsat and low area coverage for SPOT images decreased statistical predictive power (fewer observations). Due to the large spatial heterogeneity of tsunami characteristics as well as topographic and land-use features, it was more important to cover a larger area. Nevertheless, if more land cover classes would be referenced and high resolution imagery with low cloud cover would be available, the full benefits of higher spatial resolution imagery used to extract more precise land use roughness coefficients could be exploited.     

Monitoring forest areas from continental to territorial levels using a sample of medium spatial resolution satellite imagery

A global systematic sampling scheme has been developed by the UN FAO and the EC TREES project to estimate rates of deforestation at global or continental levels at intervals of 5 to 10 years. This global scheme can be intensified to produce results at the national level. In this paper, using surrogate observations, we compare the deforestation estimates derived from these two levels of sampling intensities (one, the global, for the Brazilian Amazon the other, national, for French Guiana) to estimates derived from the official inventories. We also report the precisions that are achieved due to sampling errors and, in the case of French Guiana, compare such precision with the official inventory precision.We extract nine sample data sets from the official wall-to-wall deforestation map derived from satellite interpretations produced for the Brazilian Amazon for the year 2002 to 2003. This global sampling scheme estimate gives 2.81 million ha of deforestation (mean from nine simulated replicates) with a standard error of 0.10 million ha. This compares with the full population estimate from the wall-to-wall interpretations of 2.73 million ha deforested, which is within one standard error of our sampling test estimate. The relative difference between the mean estimate from sampling approach and the full population estimate is 3.1%, and the standard error represents 4.0% of the full population estimate.This global sampling is then intensified to a territorial level with a case study over French Guiana to estimate deforestation between the years 1990 and 2006. For the historical reference period, 1990, Landsat-5 Thematic Mapper data were used. A coverage of SPOT-HRV imagery at 20 m × 20 m resolution acquired at the Cayenne receiving station in French Guiana was used for year 2006.Our estimates from the intensified global sampling scheme over French Guiana are compared with those produced by the national authority to report on deforestation rates under the Kyoto protocol rules for its overseas department. The latter estimates come from a sample of nearly 17,000 plots analyzed from same spatial imagery acquired between year 1990 and year 2006. This sampling scheme is derived from the traditional forest inventory methods carried out by IFN (Inventaire Forestier National). Our intensified global sampling scheme leads to an estimate of 96,650 ha deforested between 1990 and 2006, which is within the 95% confidence interval of the IFN sampling scheme, which gives an estimate of 91,722 ha, representing a relative difference from the IFN of 5.4%.These results demonstrate that the intensification of the global sampling scheme can provide forest area change estimates close to those achieved by official forest inventories (<6%), with precisions of between 4% and 7%, although we only estimate errors from sampling, not from the use of surrogate data.Such methods could be used by developing countries to demonstrate that they are fulfilling requirements for reducing emissions from deforestation in the framework of an REDD (Reducing Emissions from Deforestation in Developing Countries) mechanism under discussion within the United Nations Framework Convention on Climate Change (UNFCCC). Monitoring systems at national levels in tropical countries can also benefit from pan-tropical and regional observations, to ensure consistency between different national monitoring systems.     

Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type classification

We developed a classification workflow for boreal forest habitat type mapping. In object-based image analysis framework, Fractal Net Evolution Approach segmentation was combined with random forest classification. High-resolution WorldView-2 imagery was coupled with ALS based canopy height model and digital terrain model. We calculated several features (e.g. spectral, textural and topographic) per image object from the used datasets. We tested different feature set alternatives; a classification accuracy of 78.0% was obtained when all features were used. The highest classification accuracy (79.1%) was obtained when the amount of features was reduced from the initial 328 to the 100 most important using Boruta feature selection algorithm and when ancillary soil and land-use GIS-datasets were used. Although Boruta could rank the importance of features, it could not separate unimportant features from the important ones. Classification accuracy was bit lower (78.7%) when the classification was performed separately on two areas: the areas above and below 1 m vertical distance from the nearest stream. The data split, however, improved the classification accuracy of mire habitat types and streamside habitats, probably because their proportion in the below 1 m data was higher than in the other datasets. It was found that several types of data are needed to get the highest classification accuracy whereas omitting some feature groups reduced the classification accuracy. A major habitat type in the study area was mesic forests in different successional stages. It was found that the inner heterogeneity of different mesic forest age groups was large and other habitat types were often inside this heterogeneity.