Sample-based estimation of tree cover change in haiti using aerial photography:Substantial increase in tree cover between 2002 and 2010

Recent studies have used high resolution imagery to estimate tree cover and changes in natural forest cover in Haiti. However, there is still no rigorous quantification of tree cover change accounting for planted or managed trees, which are very important in Haiti’s farming systems. We estimated net tree cover change, gross loss, and gross gain in Haiti between 2002 and 2010 from a stratified random sample of 400 pixels with a systematic sub-sample of 25 points. Using 30 cm and 1 m resolution images, we classified land cover at each point, with any point touching a woody plant higher than 5 m classified as tree crown. We found a net increase in tree crown cover equiva-lent to 5.0 ± 2.3% (95% confidence interval) of Haiti’s land area. Gross gains and losses amounted to 9.0 ± 2.1% and 4.0 ± 1.3% of the territory, respectively. These results challenge, for the first time with empirical evidence, the predominant narrative that portrays Haiti as experiencing ongoing forest or tree cover loss. The net gain in tree cover quantified here represents a 35% increase from 2002 to 2010. Further research is needed to determine the drivers of this substantial net gain in tree cover at the national scale

Spatio-temporal analysis of landuse dynamics in Upper Opa Catchment, Southwest Nigeria

This study explored the use of geospatial techniques to assess land use change within upper Opa catchment area in Ile-Ife, Osun State, Nigeria for a period of 28 years between 1986 and 2014. To accomplish this, Landsat TM 1986, ETM 2002 and OLI 2014 were acquired from the USGS Earth Explorer in Global Land Cover Facility (GLCF) web site and subjected to supervised classification using the Anderson classification Scheme. Six land use/landcover classes were identified: Built-up, Bareland, Riparian, Forest Vegetation, Rock Outcrop and Water body using ENVI 5.1 Software. A change detection analysis of LULC was carried out to provide the necessary understanding of changes over the period and prediction for expected change in future was carried out. Result showed remarkable changes in all the land uses. For instance, Built-up increased from 7.01 km2 (6.4%) in 1986 to 11.92 km2 (10.8%) and 20.86km2 (18.8%) in 2002 and 2014, respectively while vegetation reduced from 61.72km2 (61.30%) in 1986 to 55.41km2 (50.2%) in 2014. The study further confirmed that if the current rate of reduction in the vegetation cover is allowed to continue unabated, there may be no vegetation again in the area in the next 30 years, thus, jeopardizing the need of the future generation and causing greater harm to the environment. In view of the above, efforts should be made to control land use activities within upper Opa catchment by enforcing the “Green Policy” of the Environment Act of the Federal Government of Nigeria which will check the indiscriminate land uses particular the encroachment of other uses into vegetation land.Keywords: Opa Catchment, LULC, Anthropogenic activities, Southwestern Nigeri

Accuracy of Supervised Classification of Cropland in sub–Saharan Africa

Mali is a country in sub–Saharan Africa where monitoring of cropped land area would greatly benefit food security initiatives and aid organizations. More importantly village–scale studies on cropped land are fundamental to making a difference in the way we look at cropped land area and food availability in this region of the world. Using Landsat surface reflectance imagery and World View–2 derived labeled data, this study focuses on accuracy of supervised classification methods while addressing various levels of scale. Several classification methods are taken into account to determine the best method possible to produce cropped area estimates using this data. The relationship between classification and scale is addressed by taking into account how distance and proximity affect accuracy. Accuracy is measured by kappa coefficients, and results among the different methods vary. Kappa coefficients generated are very low, and results suggest that estimates between labels are more accurate than estimates far from labels

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Spatio-Temporal Analysis of Forest Cover Change by Using GIS and Remote Sensing Techniques; a Case of Geba Watershed, Western Ethiopia

Forest cover change was the serious environmental problem in the world. The forest cover around Geba watershed was declined substantial. The declining of this forest cover in the study area does not get research attention. This study analyze the Spatio-temporal forest cover change of the study area over the period 1990 to 2020 using Landsat image TM of 1990, ETM+ of 2003 and OLI/TIRS of 2020. The land use/land cover change (LULCC) detection results reveals that agricultural land is highly increase from 1786.6km2 (37.2%) in 1990 to 3163.2km2 (65.8%) 2020. Whereas; dense forest was dramatically decreased 2129.2km2(44.3%) in 1990 to 1127.8km2 (23.5%) in 2020. Agricultural land was increased by the rate of 45.9km2/year while dense forest was decreased by the rate of 33.4km2/year. Our finding reveals that dense forest and open forest are decreased over the study period whereas; agricultural land and settlement are increased from 1990 to 2020. The declining trend of forest cover change is associated with agricultural expansion in the periphery of the forest. Timber and charcoal production, and firewood harvesting for energy consumptions are the major driving factors for declining of forest in the study area. Generally, our results recommend the importance of participatory forest management and community awareness creation to sustain Geba watershed. Keywords: Spatio-temporal, forest cover change; Land use/Land cover change, Land use/Land cover DOI: 10.7176/JEES/12-10-03 Publication date:October 31st 202

Classification of Vegetation to Estimate Forest Fire Danger Using Landsat 8 Images: Case Study

The vegetation cover of the Earth plays an important role in the life of mankind, whether it is natural forest or agricultural crop. The study of the variability of the vegetation cover, as well as observation of its condition, allows timely actions to make a forecast and monitor and estimate the forest fire condition. The objectives of the research were (i) to process the satellite image of the Gilbirinskiy forestry located in the basin of Lake Baikal; (ii) to select homogeneous areas of forest vegetation on the basis of their spectral characteristics; (iii) to estimate the level of forest fire danger of the area by vegetation types. The paper presents an approach for estimation of forest fire danger depending on vegetation type and radiant heat flux influence using geographic information systems (GIS) and remote sensing data. The Environment for Visualizing Images (ENVI) and the Geographic Resources Analysis Support System (GRASS) software were used to process satellite images. The area’s forest fire danger estimation and visual presentation of the results were carried out in ArcGIS Desktop software. Information on the vegetation was obtained using the analysis of the Landsat 8 Operational Land Imager (OLI) images for a typical forestry of the Lake Baikal natural area. The maps (schemes) of the Gilbirinskiy forestry were also used in the present article. The unsupervised k-means classification was used. Principal component analysis (PCA) was applied to increase the accuracy of decoding. The classification of forest areas according to the level of fire danger caused by the typical ignition source was carried out using the developed method. The final information product was the map displaying vector polygonal feature class, containing the type of vegetation and the level of fire danger for each forest quarter in the attribute table

Earth resources: A continuing bibliography (issue 26)

This bibliography lists 480 reports, articles, and other documents introduced into the NASA scientific and technical information system between April 1, 1980 and June 30, 1980. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Urbanization, agriculture and spatio-temporal dynamics of the anthropization of forest ecosystems in Haiti

En Haïti, le paysage naturel connaît de nombreuses transformations incluant la destruction des écosystèmes forestiers liés à une synergie des activités anthropiques, notamment de l’agriculture et de l’urbanisation exacerbées par la croissance démographique galopante dans un contexte socioéconomique précaire. Le présent travail a été initié en vue de cartographier et de quantifier les dynamiques spatio-temporelles de l’anthropisation du paysage naturel en zone urbaine et périurbaine de (Port-au-Prince et du Cap-Haitien), en zone rurale (commune de Vallières) et au sein des aires protégées (Parcs Nationaux Naturels de la Forêt des Pins/unité 2 (PNN-FP2), de La-Visite (PNN-LV), du Macaya (PNN-M) en Haïti. Les approches cartographiques et du gradient urbain-rural, appuyées sur des outils d’analyse de l’écologie du paysage, ont été développée à partir des images satellitaires Landsat acquises entre 1973 et 2021. Les résultats obtenus ont montré que le paysage est dynamique en zone urbaine et périurbaine des villes d’Haïti. En effet, au niveau de l’agglomération de Port-au-Prince, la capitale d’Haïti, la surface de la zone urbaine a septuplé alors que celle de la zone périurbaine a quintuplé au détriment de la zone rurale adjacente. Dans cette ville, la dynamique de composition du paysage est caractérisée par une rapide progression du bâti en zone urbaine, périurbaine et des champs en zone rurale par opposition à la régression de la végétation plus accentuée en zone périurbaine. Une tendance similaire a été notée dans la ville de Cap-Haitien où une réduction de la surface de la végétation naturelle, nettement plus marquée en zone périurbaine, a été notée au profit du bâti et des champs. L’expansion agricole menace également la préservation de la végétation ligneuse au niveau de la zone rurale adjacente. Au niveau des zones rurales éloignées des villes, l’étude de cas sur la commune de Vallières et de ses trois sections communales révèle qu’en 35 ans la superficie des forêts a régressé à travers le morcellement des grandes taches initiales par opposition à la dynamique progressive des zones agricoles et des surfaces dénudées. La dégradation accentuée de la couverture végétale au profit des zones agricoles a mené inéluctablement à la création de zones dénudées qui se varient d’une section communale à une autre (les zones dénudées couvrent environ 3,94% du paysage de Corosse, 6,88 à Trois-Palmistes en 2019 et jusqu’à 9% du paysage à Grosse Roche). Suite à une saturation foncière dans les zones rurales habitées, les activités anthropiques étendent leur emprise jusqu’au niveau des aires protégées d’Haïti. Ainsi, au sein du PNN-FP2, PNN-LV et du PNN-M, nos résultats soulignent que les activités anthropiques illicites ont entrainé une dynamique paysagère matérialisée par des pertes du couvert forestier entre 1985 à 2018 au profit des zones agricoles notamment. Les taux annuels de déforestation sont également importants et ont varié selon les parcs (1,8% dans le PNN-FP2, 1,2% dans le PNN-LV et 1,4% dans le PNN-M). La régression de la couverture forestière est sous-tendue, au niveau des aires protégées étudiées, par la dissection et la fragmentation de ses taches par opposition à la création et la fusion des taches de classes anthropiques (champs, bâtis, sols nus, etc.). En définitive, cette étude a mis en évidence la forte dynamique des paysages naturels en milieu urbain, périurbain et rural d’Haïti. Ces mutations, dues aux activités anthropiques, vont compromettre dangereusement l’avenir de ces paysages naturels dont dépend la survie des populations locales. Cette étude justifie le besoin urgent de développer une politique rationnelle de conservation et d’aménagement des paysages naturels d’Haïti soutenue par des mesures de développement socio-économique contextualisées.11. Sustainable cities and communitie

Towards Automated Analysis of Urban Infrastructure after Natural Disasters using Remote Sensing

Natural disasters, such as earthquakes and hurricanes, are an unpreventable component of the complex and changing environment we live in. Continued research and advancement in disaster mitigation through prediction of and preparation for impacts have undoubtedly saved many lives and prevented significant amounts of damage, but it is inevitable that some events will cause destruction and loss of life due to their sheer magnitude and proximity to built-up areas. Consequently, development of effective and efficient disaster response methodologies is a research topic of great interest. A successful emergency response is dependent on a comprehensive understanding of the scenario at hand. It is crucial to assess the state of the infrastructure and transportation network, so that resources can be allocated efficiently. Obstructions to the roadways are one of the biggest inhibitors to effective emergency response. To this end, airborne and satellite remote sensing platforms have been used extensively to collect overhead imagery and other types of data in the event of a natural disaster. The ability of these platforms to rapidly probe large areas is ideal in a situation where a timely response could result in saving lives. Typically, imagery is delivered to emergency management officials who then visually inspect it to determine where roads are obstructed and buildings have collapsed. Manual interpretation of imagery is a slow process and is limited by the quality of the imagery and what the human eye can perceive. In order to overcome the time and resource limitations of manual interpretation, this dissertation inves- tigated the feasibility of performing fully automated post-disaster analysis of roadways and buildings using airborne remote sensing data. First, a novel algorithm for detecting roadway debris piles from airborne light detection and ranging (lidar) point clouds and estimating their volumes is presented. Next, a method for detecting roadway flooding in aerial imagery and estimating the depth of the water using digital elevation models (DEMs) is introduced. Finally, a technique for assessing building damage from airborne lidar point clouds is presented. All three methods are demonstrated using remotely sensed data that were collected in the wake of recent natural disasters. The research presented in this dissertation builds a case for the use of automatic, algorithmic analysis of road networks and buildings after a disaster. By reducing the latency between the disaster and the delivery of damage maps needed to make executive decisions about resource allocation and performing search and rescue missions, significant loss reductions could be achieved

Monitoring the impact of deforestation on an aquatic ecosystem using remote sensing: a case study of the Mngazana mangrove forest in the eastern cape province.

Coastal mangrove vegetation at Mngazana continues to be threatened and reduced periodically due to unmonitored harvesting. Covering an area of 148ha, the Mngazana mangrove forest remains unreserved, thus, research on the Mngazana mangroves is essential in order to monitor their state and sustainable management. Since in-situ monitoring of mangrove areas is both challenging and time-consuming, remote sensing technologies have been used to monitor these ecosystems. This study was carried out to monitor the impact of deforestation using ASTER satellite images over ten years: from 2008 - 2018. Validation was carried out by comparing classification results with the ground-referenced data, which yielded satisfactory agreement, with an overall accuracy of 94.64 percent and Kappa coefficient of 0.93 for 2008; and in 2009, the overall accuracy was 88.62 percent and a Kappa coefficient of 0.85. While the overall accuracy of 95.08 percent and a Kappa coefficient of 0.92 for 2016 and 2018 were observed, the overall accuracy of 93.58 percent and a Kappa coefficient of 0.91 was yielded. NDVI and SAVI indices were used as monitoring indicators. The results obtained in the study indicated that the canopy density of the mangrove forest remained unchanged in the years under investigation. However, insignificant changes in canopy density were identified between 2009 and 2016.Thesis (MSc) (Applied Remote Sensing & GIS) -- University of Fort Hare, 202