INVESTIGATION OF DEFORESTATION USING MULTI-SENSOR SATELLITE TIME SERIES DATA IN NORTH KOREA

Department of Urban and Environmental Engineering(Environmental Science and Engineering)North Korea is very vulnerable to natural disasters such as floods and landslides due to institutional, technological, and other various reasons. Recently, the damage has been more severe and vulnerability is also increased because of continued deforestation. However, due to political constraints, such disasters and forest degradation have not been properly monitored. Therefore, using remote sensing based satellite imagery for forest related research of North Korea is regarded as currently the only and most effective method. Especially, machine learning has been widely used in various classification studies as a useful technique for classification and analysis using satellite images. The aim of this study was to improve the accuracy of forest cover classification in the North Korea, which cannot be accessed by using random forest model. Indeed, another goal of this study was to analyze the change pattern of denuded forest land in various ways. The study area is Musan-gun, which is known to have abundant forests in North Korea, with mountainous areas accounting for more than 90%. However, the area has experienced serious environmental problems due to the recent rapid deforestation. For example, experts say that the damage caused by floods in September 2016 has become more serious because denuded forest land has increased sharply in there and such pattern appeared even in the high altitude areas. And this led the mountain could not function properly in the flood event. This study was carried out by selecting two study periods, the base year and the test year. To understand the pattern of change in the denuded forest land, the time difference between the two periods was set at about 10 years. For the base year, Landsat 5 imageries were applied, and Landsat 8 and RapidEye imageries were applied in the test year. Then the random forest machine learning was carried out using randomly extracted sample points from the study area and various input variables derived from the used satellite imageries. Finally, the land cover classification map for each period was generated through this random forest model. In addition, the distribution of forest changing area to cropland, grassland, and bare-soil were estimated to the denuded forest land. According to the study results, this method showed high accuracy in forest classification, also the method has been effective in analyzing the change detection of denuded forest land in North Korea for about 10 years.ope

Competition and Burn Severity Determine Post-Fire Sapling Recovery in a Nationally Protected Boreal Forest of China: An Analysis from Very High-Resolution Satellite Imagery

Anticipating how boreal forest landscapes will change in response to changing fire regime requires disentangling the effects of various spatial controls on the recovery process of tree saplings. Spatially explicit monitoring of post-fire vegetation recovery through moderate resolution Landsat imagery is a popular technique but is filled with ambiguous information due to mixed pixel effects. On the other hand, very-high resolution (VHR) satellite imagery accurately measures crown size of tree saplings but has gained little attention and its utility for estimating leaf area index (LAI, m2/m2) and tree sapling abundance (TSA, seedlings/ha) in post-fire landscape remains untested. We compared the explanatory power of 30 m Landsat satellite imagery with 0.5-m WorldView-2 VHR imagery for LAI and TSA based on field sampling data, and subsequently mapped the distribution of LAI and TSA based on the most predictive relationships. A random forest (RF) model was applied to assess the relative importance and causal mechanisms of spatial controls on tree sapling recovery. The results showed that pixel percentage of canopy trees (PPCT) derived from VHR imagery outperform all Landsat-derived spectral indices for explaining variance of LAI (R2VHR = 0.676 vs. R2Landsat = 0.427) and TSA (R2VHR = 0.508 vs. R2Landsat = 0.499). The RF model explained an average of 55.5% (SD = 3.0%, MSE = 0.382, N = 50) of the variation of estimated LAI. Understory vegetation coverage (competition) and post-fire surviving mature trees (seed sources) were the most important spatial controls for LAI recovery, followed by burn severity (legacy effect), topographic factors (environmental filter) and nearest distance to unburned area (edge effect). These analyses allow us to conclude that in our study area, mitigating wildfire severity and size may increase forest resilience to wildfire damage. Given the easily-damaged seed banks and relatively short seed dispersal distance of coniferous trees, reasonable human help to natural recovery of coniferous forests is necessary for severe burns with a large patch size, particularly in certain areas. Our research shows the VHR WorldView-2 imagery better resolves key characteristics of forest landscapes like LAI and TSA than Landsat imagery, providing a valuable tool for land managers and researchers alike

Identification of windbreaks in Kansas using object-based image analysis, GIS techniques and field survey

Windbreaks are valuable resources in conserving soils and providing crop protection in Great Plains states in the US. Currently, Kansas has no up-to date inventory of windbreaks. The goal of this project was to assist foresters with future windbreak renovation planning and reporting, by outlining a series of semi-automated digital image processing methods that rapidly identify windbreak locations. There were two specific objectives of this research. First, to develop semi-automated methods to identify the location of windbreaks in Kansas, this can be applied to other regions in Kansas and the Great Plains. We used a remote sensing technique known as object-based image analysis (OBIA) to classify windbreaks visible in the color aerial imagery of National Agriculture Imagery Program. We also combined GIS techniques and field survey to complement OBIA in generating windbreak inventory. The techniques successfully located more than 4500, windbreaks covering an approximate area of 2500, hectares in 14 Kansas counties. The second purpose of this research is to determine how well the results of the automated classification schemes match with other available windbreak data and the selected sample collected in the field. The overall accuracy of OBIA method was 58.97 %. OBIA combined with ‘heads up’ digitizing and field survey method yielded better result in identifying and locating windbreaks in the studied counties with overall accuracy of 96 %

Assessment of Spot Satellite Data for Tropical Vegetation Inventory and Monitoring in Sumatra

Following a previous vegetation mapping in Sumatra island (Indonesia), an assessment of SPOT satellite capability to handle specific problems related to vegetation identification and monitoring from remote sensing data has been undertaken. Results of visual interpretation and multispectral analysis have shown the usefulness of SPOT data for the appraisal of tropical vegetation at medium scale. This was particularly striking for the swampy vegetation types including mangroves and for the secondary vegetation, for which significant improvements have been brought by multispectral classifications. A 20 m ground resolution is neither sufficient to provide information on primary forest patterns, nor to identify properly logged over areas. Never theless, several degrees of depletion of the forest and all the serial stages have been identified, which is a considerable progress compared with previous remote sensing means. SPOT is a very good alternative to medium scale aerial photographs for the production of medium scale (1 : 100 000 to 1 : 250 000) vegetation and land-use maps

Land degradation vulnerability mapping in a newly-reclaimed desert oasis in a hyper-arid agro-ecosystem using ahp and geospatial techniques

Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for using remote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security

Comparison of high-resolution NAIP and unmanned aerial vehicle (UAV) imagery for natural vegetation communities classification using machine learning approaches

To map and manage forest vegetation including wetland communities, remote sensing technology has been shown to be a valid and widely employed technology. In this paper, two ecologically different study areas were evaluated using free and widely available high-resolution multispectral National Agriculture Imagery Program (NAIP) and ultra-high-resolution multispectral unmanned aerial vehicle (UAV) imagery located in the Upper Great Lakes Laurentian Mixed Forest. Three different machine learning algorithms, random forest (RF), support vector machine (SVM), and averaged neural network (avNNet), were evaluated to classify complex natural habitat communities as defined by the Michigan Natural Features Inventory. Accurate training sets were developed using both spectral enhancement and transformation techniques, field collected data, soil data, texture, spectral indices, and expert knowledge. The utility of the various ancillary datasets significantly improved classification results. Using the RF classifier, overall accuracies (OA) between 83.8% and 87.7% with kappa (k) values between 0.79 and 0.85 for the NAIP imagery and between 87.3% and 93.7% OA with k values between 0.83 and 0.92 for the UAV dataset were achieved. Based on the results, we concluded RF to be a robust choice for classifying complex forest vegetation including surrounding wetland communities

ASSESSMENT OF SPOT SATELLITE DATA FOR TROPICAL VEGETATION INVENTORY AND MONITORING IN SUMATRA

Following  a  previous  vegetation  mapping  in  Sumatra  island  (Indonesia),  an  assessment  of  SPOT satellite capability to handle specific problems related to vegetation identification and monitoring from  remote sensing data has been undertaken. Results of visual interpretation and multispectral analysis have shown the usefulness of SPOT data  for the appraisal  of  tropical  vegetation  at medium  scale. This was  particularly  striking  for  the  swampy  vegetation types  including mangroves  and  for  the  secondary vegetation,  for which  significant  improvements have been brought by multispectral classifications. A 20 m ground resolution is neither sufficient  to provide information on  primary  forest  patterns,  nor  to  identify  properly  logged  over  areas.  Never theless,  several  degrees  of depletion  of  the  forest  and  all  the  serial  stages  have  been  identified,  which  is  a  considerable  progress compared with previous remote sensing means. SPOT is a very good alternative to medium scale aerial photographs for the production of medium  scale (1 : 100 000 to 1 : 250 000) vegetation and land-use maps

Integration of remote sensing and GIS in studying vegetation trends and conditions in the gum arabic belt in North Kordofan, Sudan

The gum arabic belt in Sudan plays a significant role in environmental, social and economical aspects. The belt has suffered from deforestation and degradation due to natural hazards and human activities. This research was conducted in North Kordofan State, which is affected by modifications in conditions and composition of vegetation cover trends in the gum arabic belt as in the rest of the Sahelian Sudan zone. The application of remote sensing, geographical information system and satellites imageries with multi-temporal and spatial analysis of land use land cover provides the land managers with current and improved data for the purposes of effective management of natural resources in the gum arabic belt. This research investigated the possibility of identification, monitoring and mapping of the land use land cover changes and dynamics in the gum arabic belt during the last 35 years. Also a newly approach of object-based classification was applied for image classification. Additionally, the study elaborated the integration of conventional forest inventory with satellite imagery for Acacia senegal stands. The study used imageries from different satellites (Landsat and ASTER) and multi-temporal dates (MSS 1972, TM 1985, ETM+ 1999 and ASTER 2007) acquired in dry season (November). The imageries were geo-referenced and radiometrically corrected by using ENVI-FLAASH software. Image classification (pixel-based and object-based), post-classification change detection, 2x2 and 3x3 pixel windows and accuracy assessment were applied. A total of 47 field samples were inventoried for Acacia senegal tree’s variables in Elhemmaria forest. Three areas were selected and distributed along the gum arabic belt. Regression method analysis was applied to study the relationship between forest attributes and the ASTER imagery. Application of multi-temporal remote sensing data in gum arabic belt demonstrated successfully the identification and mapping of land use land cover into five main classes. Also NDVI categorisation provided a consistent method for land use land cover stratification and mapping. Forest dominated by Acacia senegal class was separated covering an area of 21% and 24% in the year 2007 for areas A and B, respectively. The land use land cover structure in the gum arabic belt has obvious changes and reciprocal conversions between the classes indicating the trends and conditions caused by the human interventions as well as ecological impacts on Acacia senegal trees. The study revealed a drastic loss of Acacia senegal cover by 25% during the period of 1972 to 2007.The results of the study revealed to a significant correlation (p ≤ 0.05) between the ASTER bands (VNIR) and vegetation indices (NDVI, SAVI, RVI) with stand density, volume, crown area and basal area of Acacia senegal trees. The derived 2x2 and 3x3 pixel windows methods successfully extracted the spectral reflectance of Acacia senegal trees from ASTER imagery. Four equations were developed and could be widely used and applied for monitoring the stand density, volume, basal area and crown area of Acacia senegal trees in the gum arabic belt considering the similarity between the selected areas. The pixel-based approach performed slightly better than the object-based approach in land use land cover classification in the gum arabic belt. The study come out with some valuable recommendations and comments which could contribute positively in using remotely sensed imagery and GIS techniques to explore management tools of Acacia senegal stands in order to maintain the tree component in the farming and the land use systems in the gum arabic belt

Advances in remote sensing applications for urban sustainability

Abstract: It is essential to monitor urban evolution at spatial and temporal scales to improve our understanding of the changes in cities and their impact on natural resources and environmental systems. Various aspects of remote sensing are routinely used to detect and map features and changes on land and sea surfaces, and in the atmosphere that affect urban sustainability. We provide a critical and comprehensive review of the characteristics of remote sensing systems, and in particular the trade-offs between various system parameters, as well as their use in two key research areas: (a) issues resulting from the expansion of urban environments, and (b) sustainable urban development. The analysis identifies three key trends in the existing literature: (a) the integration of heterogeneous remote sensing data, primarily for investigating or modelling urban environments as a complex system, (b) the development of new algorithms for effective extraction of urban features, and (c) the improvement in the accuracy of traditional spectral-based classification algorithms for addressing the spectral heterogeneity within urban areas. Growing interests in renewable energy have also resulted in the increased use of remote sensing—for planning, operation, and maintenance of energy infrastructures, in particular the ones with spatial variability, such as solar, wind, and geothermal energy. The proliferation of sustainability thinking in all facets of urban development and management also acts as a catalyst for the increased use of, and advances in, remote sensing for urban applications

Mapping natural forest cover, tree species diversity and carbon stocks of a subtropical Afromontane forest using remote sensing.

Doctoral Degree. University of KwaZulu-Natal, Durban.Natural forests cover about a third of terrestrial landmass and provides benefits such as carbon sequestration, and regulation of biogeochemical cycles. It is essential that adequate information is available to support forest management. Remote Sensing imageries provide data for mapping natural forests. Hence, our study aimed at mapping the Nkandla Forest Reserve attributes with Remote Sensing imageries. Quantitative information on the forest attributes is non-existent for many of these forests, including the sub-tropical Afromontane Nkandla Forest Reserve. This does not support scientific and evidence based natural forest management. A review of literature revealed that progress has been made in Remote Sensing monitoring of natural forest attributes. The Random Forest (RF) and Support Vector Machine (SVM) were applied to Landsat 8 in classifying the land use land cover (LULC) classes of the forest. Each of the algorithms produced higher accuracy of above 95% with the SVM performing slightly better than the RF. The SVM, Markov Chain and Multi-Layer Perceptron Neural Network (MLPNN) were adopted for a spatiotemporal change detection over the last 30 years at decadal interval for the forest. There were consistent changes in each of the four LULC classes. The study further conducted a forecasting of LULC distribution for 2029. Aboveground carbon (AGC) estimation was carried out using Sentinel 2 imagery and RF modelling. Four models made up smade of Sentinel 2 products could successfully map the AGC with high accuracies. The last two studies focused on tree species diversity with the first evaluating the influence of spatial and spectral resolution on prediction accuracies by comparing the PlanetScope, RapidEye, Sentinel 2 and Landsat 8. Both the spatial and spectral resolution were found to influence accuracies with the Sentinel 2 emerging as the best imagery. The second aspect focused on identifying the best season for the prediction of tree species diversity. Summer imagery emerged as the best season and the winter being the least performer. Overall, our study indicates that Remote Sensing imageries could be used for successful mapping of natural forest attributes. The outputs of our studies could also be of interest to forest managers and Remote Sensing experts.Author's Publications and Manuscripts can be found on page iii