Anthropogenic Threats to Degraded Forest Land in the Savannahs’ Region of Togo from 1984 to 2020, West Africa

This study focuses on the landscape dynamics of the savannahs’ region in the far north of Togo. Based on a literature review and satellite images analysis using GIS and remote sensing, the study aims to ascertain the effects of anthropogenic threats on the forest coverage of the Savannahs’ Region between 1984 to 2020. The objective is to clarify the dynamics of land use in the region from 1984 to 2000 and from 2000 to 2020. The findings indicate a significant decline in forest coverage within the region from 1984 to 2020, a trend attributed to land use patterns. Dry forests in the Savannah region are largely converted to farmlands, housing, dry savannahs or agroforestry parks, leading to a steady reduction in forest areas.

Spatial assessment of forest cover and land-use changes in the Hindu-Kush mountain ranges of northern Pakistan

Anthropogenic activities and natural processes are continuously altering the mountainous environment through deforestation, forest degradation and other land-use changes. It is highly important to assess, monitor and forecast forest cover and other land-use changes for the protection and conservation of mountainous environment. The present study deals with the assessment of forest cover and other land-use changes in the mountain ranges of Dir Kohistan in northern Pakistan, using high resolution multi-temporal SPOT-5 satellite images. The SPOT-5 satellite images of years 2004, 2007, 2010 and 2013 were acquired and classified into land-cover units. In addition, forest cover and land-use change detection map was developed using the classified maps of 2004 and 2013. The classified maps were verified through random field samples and Google Earth imagery(Quick birds and SPOT-5). The results showed that during the period 2004 to 2013 the area of forest land decreased by 6.4%, however, area of range land and agriculture land have increased by 22.1% and 2.9%, respectively. Similarly, barren land increased by 1.1%, whereas, area of snow cover/glacier is significantly decreased by 21.3%. The findings from the study will be useful for forestry and landscape planning and can be utilized by the local, provincial and national forest departments; and REDD+ policy makers in Pakistan.

Land Use/Land Cover and Forest Canopy Density Monitoring of Wafi-Golpu Project Area, Papua New Guinea

This study aims to examine the use of Remote Sensing and Geographical Information System (GIS) technology in land use/land cover mapping to aide sustainable planning and development in the Wafi-Golpu project area. At the same time, this study examines an existing method of Forest Canopy Density (FCD) model to estimate forest canopy density of the proposed deforestation site, which is known as the Advanced Exploration Feasibility Study Activities (AEFSA) area within the Wafi-Golpu Project site. The FCD model calculates the forest canopy density using the three (3) indices of vegetation, soil and shadow from the Landsat-8 Operational Land Imager (OLI) satellite image of year 2013. In this study an attempt has been made to monitor the forest loss or degradation during deforestation in a natural forest stand of the Wafi-Golpu project area using forest FCD mapping and monitoring model and the findings of the study will assist the project planners and developers with their work on forest rehabilitation and reforestation for the purposes of sustainable forest management. The result of the work shows that a considerable amount of forest loss will be undertaken during the AEFSA deforestation exercise and also the findings show that a reliable land use/land cover map will greatly assist sustainable development in a resource project development period.

An Assessment of Land Cover Change in Gashaka-Gumti National Park, Nigeria

The paper assessed the land cover change in Gashaka-Gumti National Park between 1991 and 2021. To achieve this, LandSat data of years 1991, 2001, 2011 and 2021 were obtained from the United States Geological Survey online resource. The findings of the study revealed that there is decrease in the different land cover types over time as a result of anthropogenic activities of the enclave dwellers. The study observed that the continuous existence of enclaves within and around the Park constitutes a serious threat to the survival of the Park. The study recommended that the federal government should consider resettlement of the enclave dwellers to give way for the development of the Park.

Drivers of Land Cover Dynamics for Pugu and Kazimuzumbwi Forest Reserves in Kisarawe, Tanzania

Forests that are close to growing urban centres have been subject to constant deforestation and degradation from various factors. This study assesses the drivers of land cover dynamics in Pugu and Kazimzumbwi forest reserves in the context of urban and peri-urban expansion of Dar es Salaam for the past three decades. The study adopted review of relevant literature and household survey from three settlements surrounding the forest reserves. One hundred and fifty (150) households were collected from Buyuni, Chanika and Masaki in Ilala and Kisarawe and administered with semi-structured questionnaire to collect information on migration, use of forest products by communities and perception on climate change and variability. SPSS computer program was used to analyse the questionnaire data while tables and graphs were adopted for presentation of the results. Rural-urban and internal urban migration in Dar es Salaam was identified as one of the primary drivers of land cover dynamics in peri-urban areas and adjoining environments. The migration was intensified by push drivers which include urban growth, market of land and reliance on forest product among community members as source of their livelihoods. The increased rainfall variability accompanied with high temperature has contributed to frequent droughts which compromises rainfed agriculture. Thus, the successful conservation of the forest will require strengthened enforcement of protection measures supported with introduction of alternative livelihood strategies for majority of poor community members.

Land cover mapping of deciduous forest regions using ETM+ data: a case study of Azerbaijan Province, Iran

Up to date information about the existing land cover patterns and changes in land cover over time is one of the prime prerequisites for the preparation of an integrated development plan and economic development program of a region. By using ETM＋ image data from 2002, we provided a land cover map of deciduous forest regions in Azerbaijan Province, Iran. Initial qualitative evaluation of the data showed no significant radiometric errors. Image classification was carried out using a maximum likelihood-based supervised classification method. In the end, we determined five major land cover classes, i.e., grass lands, deciduous broad-leaf forest, cultivated land, river and land without vegetation cover. Accuracy, estimated by the use of criteria such as overall accuracy from a confusion matrix of classification was 86% with a 0.88 Kappa coefficient. Such high accuracy results demonstrate that the combined use of spectral and textural characteristics increased the number of classes in the field classification, also with excellent accuracy. The availability and use of time series of remote sensing data permit the detection and quantification of land cover changes and improve our understanding of the past and present status of forest ecosystems.

Land Use/Land Cover Changes of Ago-Owu Forest Reserve, Osun State, Nigeria Using Remote Sensing Techniques

Remote sensing (RS) and GIS are important methods for land use assessment and land cover transition. In this study, land use/land cover changes in the Ago-Owu Forest Reserve, Osun State, Nigeria have been assessed. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI were acquired for 1986, 2002 and 2017 respectively. The three scenes corresponded to path 190 and row 055 of WRS-2 (Worldwide Reference System). The processing of the imagery was preceded by the clipping of the study area from the satellite image. The boundary of the reserve was carefully digitized and used to clip the imagery to produce an image map of the forest reserve. Using the supervised image classification procedure, training sites were used to produce land use/land cover maps. The same classification scheme was used for the 1986, 2002 and 2017 images to facilitate the detection of change. The differences in the area covered by the different polygons between the three sets of images were measured in km2. The results show that during 1986 and 2017, there is a dramatic increase of build-up areas with a change of 55.65 km2 and sparse vegetation (farmland and grassland) with a change of 53.97 km2, while a dramatic decrease of dense vegetation (forest areas) with a change of 109.61 km2. The consequence of these results is that over the years, the population of people living in the forest reserve has increased and many of them are engaged in farming, leading to an increase in farmland. In addition, logging activities continued unabated in the forest reserve, as demonstrated by a sharp increase in the deforested area within the reserve. The maps produced in this study will serve as a planning tool for the Osun State Forestry Department to plan reforestation activities for the forest reserve.

Google Earth Engine Based Three Decadal Landsat Imagery Analysis for Mapping of Mangrove Forests and Its Surroundings in the Trat Province of Thailand

Monitoring and understanding the changes in mangrove ecosystems and their surroundings are required to determine how mangrove ecosystems are constantly changing while influenced by anthropogenic, and natural drivers. Cosistency in high spatial resolution (30 m) satellite and high performance computing facilities are limiting factors to the process, with storage and analysis requirements. With this, we present the Google Earth Engine (GEE) based approach for long term mapping of mangrove forests and their surroundings. In this study, we used a GEE based approach: 1) to create atmospheric contamination free data from 1987-2017 from different Landsat satellite imagery;and 2) evaluating the random forest classifier and post classification change detection method. The obtained overall accuracy for the years 1987 and 2017 was determined to be 0.87 and 0.96, followed by a Kappa coefficient 0.80 and 0.94. The change detection results revealed a significant decrease in the agricultural area, while there was an increase in mangrove forest, shrimp/fish farm, and bareland area. The results suggest that interconversion of land use and land cover is affecting the landscape dynamics within the study area.

Assessment of Supervised Classifiers for Land Cover Categorization Based on Integration of ALOS PALSAR and Landsat Data

Many supervised classification algorithms have been proposed, however, they are rarely evaluated for specific application. This research examines the performance of machine learning classifiers support vector machine (SVM), neural network (NN), Random Forest (RF) against maximum classifier (MLC) (traditional supervised classifier) in forest resources and land cover categorization, based on combination of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) and Landsat Thematic Mapper (TM) data, in Northern Tanzania. Various data categories based on Landsat TM surface reflectance, ALOS PALSAR backscattering and their derivatives were generated for various classification scenarios. Then a separate and joint processing of Landsat and ALOS PALSAR data were executed using SVM, NN, RF and ML classifiers. The overall classification accuracy (OA), kappa coefficient (KC) and F1 score index values were computed. The result proves the robustness of SVM and RF in classification of forest resource and land cover using mere Landsat data and integration of Landsat and PALSAR (average OA = 92% and F1 = 0.7 to 1). A two sample t-statistics was utilized to evaluate the performance of the classifiers using different data categories. SVM and RF indicate there is no significance difference at 5% significance level. SVM and RF show a significant difference when compared to NN and ML. Generally, the study suggests that parametric classifiers indicate better performance compared to parametric classifier.

Land use and land cover change processes in China's eastern Loess Plateau

Using Landsat remote sensing images, we analyzed changes in each land use type and transitions among different land use types during land use and land cover change （LUCC） in Ningwu County, located in the eastern Loess Plateau of China, from 1990 to 2010. We found that grassland, woodland, and farmland were the main land use types in the study area, and the area of each type changed slightly from 1990 to 2010, whereas the area of water, construction land, and unused land increased greatly. For the whole area, the net change and total change were insignificant due to weak human activity intensity in most of the study area, and the LUCC was dominated by quasi-balanced two-way transitions from 1990 to 2010. The insignificant overall amount of LUCC appears to have resulted from offsetting of rapid increases in population, economic growth, and the im- plementation of a program to return farmland to woodland and grassland in 2000. This program converted more farmland into woodland and grassland from 2000 to 2010 than from 1990 to 2000, but reclamation of woodland and grassland for use as farmland continued from 2000 to 2010, and is a cause for concern to the local government.

Characterisation of Landscape with ForestFragmentation Dynamics

Land cover (LC) and land use (LU) dynamics induced by human and natural processes play a major role in global as well as regional patterns of landscapes influencing biodiversity, hydrology, ecology and climate. Changes in LC features resulting in forest fragmentations have posed direct threats to biodiversity, endangering the sustainability of ecological goods and services. Habitat fragmentation is of added concern as the residual spatial patterns mitigate or exacerbate edge effects. LU dynamics are obtained by classifying temporal remotely sensed satellite imagery of different spatial and spectral resolutions. This paper reviews five different image classification algorithms using spatio-temporal data of a temperate watershed in Himachal Pradesh, India. Gaussian Maximum Likelihood classifier was found to be apt for analysing spatial pattern at regional scale based on accuracy assessment through error matrix and ROC (receiver operating characteristic) curves. The LU information thus derived was then used to assess spatial changes from temporal data using principal component analysis and correspondence analysis based image differencing. The forest area dynamics was further studied by analysing the different types of fragmentation through forest fragmentation models. The computed forest fragmentation and landscape metrics show a decline of interior intact forests with a substantial increase in patch forest during 1972-2007.

Forest Change and Its Effect on Biomass in Yok Don National Park in Central Highlands of Vietnam Using Ground Data and Geospatial Techniques

This paper assesses the changes in forest cover in Yok Don National Park of Vietnam between 2004 and 2010, and the implications of such changes on the biomass stocks of this national park. Remote sensing and GIS tools along with the ground truth data collected from the field were employed for classifying the forest types of the study area from SPOT HRV satellite imagery for years 2004 and 2010. The total area considered in this study is 115.5 thousand ha. Five different categories of forests were identified. The results demonstrated that between 2004 and 2010, the Evergreen broad leaved rich quality forest decreased by 11.2 thousand ha (3.5 Mega tons of biomass) and the Dry open dipterocarps medium quality forest decreased by 15.3 thousand ha (2.5 Mega tons of biomass). In that time period, the Evergreen broad leaved medium quality forest increased by 3.2 thousand ha (0.8 Mega tons of biomass), the Evergreen broad leaved poor quality forest increased by 2.5 thousand ha (0.24 Mega tons of biomass), and the Dry open dipterocarps poor quality forest increased by 3.2 thousand ha (0.69 Mega tons of biomass). Total biomass of the study area decreased by 4.3 Mega tons.

Integrated Assessment of Forest Cover Change and Above-Ground Carbon Stock in Pugu and Kazimzumbwi Forest Reserves, Tanzania

A study was conducted to estimate the forest cover change, quantify and map tree above-ground carbon stock using Remote sensing and GIS techniques together with forest inventory. Landsat images of 1980, 1995 and 2010 acquired during dry season were used in the estimation of cover changes. Supervised image classification using Maximum Likeli-hood Classifier was performed in ERDAS Imagine software to analyze the images and further analysis was performed in Arc GIS 9.3 software. Stratified sampling procedure was used to select concentric inventory plots in Pugu Forest Reserve (PFR) and Kazimzumbwi Forest Reserve (KFR). Plots were laid according to NAFORMA, and the tree parameters in each sampling plot were collected. A Microsoft Excel spreadsheet was used to compute the above-ground bio- mass for each plot using an empirical equation relating wood basic density and tree height. The above-ground carbon was calculated using a conversion factor of 0.49. Geostatistical method in ArcGIS was used to analyze and map carbon. Results revealed that for the periods 1980-1995 and 1995-2010, Closed Forest in PFR decreased by 4.5% and 25.3% respectively, while for KFR, Closed Forest decreased by 11.9% and 31.3% respectively. The mean carbon density for PFR and KFR were respectively 5.72 tC/ha and 0.98 tC/ha while carbon stocks were 14 730.41 tC and 7 206.46 tC re- spectively. The revealed low carbon densities were attributable to decline in area under Closed Forest in the two Forest Reserves. The study recommends concerted efforts to enhance proper management of the forests so that the two forest reserves may contribute to REDD initiatives.

A Study on Tropical Land Cover Classification Using ALOS PALSAR 50 m Ortho-Rectified Mosaic Data

The main objective of this study is to find better classifier of mapping tropical land covers using Synthetic Aperture Radar (SAR) imagery. The data used are Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 50 m ortho-rectified mosaic data. Training data for forest, herbaceous, agriculture, urban and water body in the test area located in Kalimantan were collected. To achieve more accurate classification, a modified slope correction formula was created to calibrate the intensity distortions of SAR data. The accuracy of two classifiers called Sequential Minimal Optimization (SMO) and Random Forest (RF) were applied and compared in this study. We focused on object-based approach due to its capability of providing both spatial and spectral information. Optimal combination of features was selected from 32 sets of features based on layer value, texture and geometry. The overall accuracy of land cover classification using RF classifier and SMO classifier was 46.8% and 55.6% respectively, and that of forest and non-forest classification was 74.4% and 79.4% respectively. This indicates that RF classifier has better performance than SMO classifier.

海南岛土地覆盖变化对海风锋结构演变影响的数值模拟

本文利用WRF-Noah陆气耦合中尺度模式,针对海南岛一次典型的海风锋事件进行了高分辨率的数值模拟,研究了海南岛土地覆盖(下垫面)变化对海风锋结构演变的影响及其机制。结果表明,海南岛土地覆盖变化对海风锋的作用是通过多种地表和植被属性的综合影响决定的。森林化试验中动力和热力作用分别抑制和促进海风锋的发展,对海风锋的影响是两者共同作用的结果;反照率的减小引起净辐射的增大,从而使感热通量小幅增加,使低层大气增温而增加海陆温度梯度,这在一定程度上增强了海风驱动力,但地表粗糙度的增大减弱了海风风速,进一步减弱了海风锋传播距离和上升速度。然而,由于海南岛森林覆盖面积较大,导致森林化试验与控制试验中海风锋的整体差异较小。相比之下,荒漠化试验中热力和动力作用均有利于海风锋的发展;反照率的增大和叶面积指数的降低,改变了地表能量分配,造成潜热通量显著减小,感热通量先减少后增大,对低层大气的增温效应非常明显,从而加大了海陆温度梯度。另一方面,地表粗糙度显著减小,下垫面对海风的阻挡作用减弱,海风风速增大。因此,荒漠化试验海风锋传播距离、上升速度以及海风厚度都显著增大。

基于Landsat8卫星影像土地利用景观破碎化研究——以陕西省延安麻塔流域为例

景观破碎化的过程将伴随着景观的功能衰退,因此对景观破碎化的研究对于及时监控生态安全与土地优化调整具有重要意义。以陕西省延安麻塔流域为研究对象,通过对Landsat8卫星影像的解译,获取土地利用图;利用6个景观指数评估麻塔流域在斑块、景观要素、景观3个层次的景观破碎化程度。研究结果表明:Landsat8卫星能够准确地刻画该区的土地利用现状,监督分类精确度为74%,Kappa值为0.68;麻塔流域土地利用可以分成6个类型:森林、灌木、草地、果园、农田和其他(道路和房屋),其中果园在所有土地利用类型中占据的面积最大;森林、果园和其他3类土地利用类型的景观破碎化程度低,它们在生态防护和农业生产服务方面发挥重要的景观功能;灌木、草地和农田的景观破碎化严重,它们在生态防护和农业生产方面的景观功能已经被削弱;将灌木、草地和农田的小斑块改造成相邻大斑块的土地类型将提高麻塔流域景观整体化水平,有利于麻塔流域整体景观功能的提升。

基于Landsat-8影像和随机森林方法的土地分类研究

随着对地观测数据获取能力的飞速发展,遥感应用已经进入大数据时代,土地利用分类是海量遥感数据的重要应用方向,有必要研发简单快速的分类流程。本文利用USGS云平台自身存储的遥感数据,选取最新的Landsat-8 OLI光学遥感影像,基于R语言和随机森林方法进行土地覆盖的监督分类研究。首先利用Google Map高分辨率遥感影像选取样本点,存储为KML格式,然后利用R语言的random forest包将影像分为森林、农田、城市、水体4类。分类混淆矩阵表明分类误差均小于0.2,其中森林分类精度最高,农田分类精度最低。该方法简单实用、适用性强,可以用于中小区域的快速土地分类的情景。

1989—2020年黄河流域巴彦淖尔段地表覆盖类型时空演变研究

地表覆盖类型变化对研究区域生态环境变化具有重要意义。为准确掌握黄河流域巴彦淖尔段1989—2020年间地表覆盖类型变化,该文利用Landsat卫星数据影像,以目视解译和随机森林监督分类相结合的方法,对黄河流域巴彦淖尔段内各旗县1989—2020年平均每隔10 a的地表覆盖类型进行解译分类。经过精度验证总体分类精度均大于85%,Kappa系数均大于0.80。通过地表覆盖类型转移变化矩阵,发现黄河流域巴彦淖尔段1989—2020年间沙地减少22.17%,草地减少26.18%,耕地增加20.83%,水面变化不明显;不同区域地表覆盖类型变化情况各不相同,荒漠草原区表现为沙地与草地之间的相互转化,耕地区和沙地区主要表现为沙地向耕地的转化,其中磴口县最为显著,2020年较1989年沙地减少了32.17%,耕地增加了57.48%。荒漠草原区以社会因素和自然因素共同驱动地表覆盖类型变化,耕地及沙地分布区主要以社会因素驱动地表覆盖类型变化。研究结果可为更加合理地规划利用土地空间提供有力的数据参考和支撑。

韩江中上游地区的崩岗分布特征

选取韩江中上游地区2016—2022年的Sentinel-1/2卫星星座遥感影像和其他辅助数据,基于机器学习方法进行区域尺度的崩岗识别和崩岗影响因子测度。结果表明:1)分类模型的总体精度达到84%,Kappa系数为0.8。其中,崩岗识别的用户精度和生产者精度都超过95%,且其FScore为0.97。2)截至2022年,韩江中上游地区的崩岗侵蚀面积共有435.5 km^(2),各县(市、区)崩岗侵蚀面积差异明显,年变化趋势不一。其中,五华县崩岗侵蚀面积最多(199.2 km^(2)),其年平均变化率为16.29 km^(2)/a。梅江区崩岗侵蚀面积最少(1.6 km^(2)),其年平均变化率为0.18 km^(2)/a。3)韩江中上游地区崩岗发生概率与高程、坡度、植被覆盖、地质类型、人口密度、大气压、降雨量、经向风速、纬向风速和风速等10个因素存在显著相关性(P<0.001)。在一定的变化范围内,高程、坡度、地质类型、大气压、经向风速、纬向风速和风速对研究区崩岗发生为正向影响,植被覆盖、人口密度和降雨量对崩岗发生为负向影响。

Separability Analysis of Atlantic Forest Patches by Comparing Parametric and Non-Parametric Image Classification Algorithms

The aim of this work was to differentiate Atlantic Forest patches, as well as their spatial distribution, from other tree covers that compose the landscape, by comparing three methods of digital images classification, using techniques of geoprocessing and remote sensing. The study area was a sub-basin of the Iperó River, tributary of the Iperó-Mirim stream, Sarapuí River basin, in Ara&#231;oiaba da Serra, State of S&#227;o Paulo, Brazil. This research has been developed on a Geographic Information System environment platform, using medium resolution images from Sentinel-2 Satellite. Three image classification algorithms: Maximum Likelihood Classification (MLC), Support Vector Machines (SVM) and Random Tree (RT) were applied to verify the separability of forest patches, forestry and other uses. The results were analyzed by means of a confusion matrix, accuracy and kappa index, thus showing that the three algorithms were able to successfully differentiate the targets, with the higher efficiency attributed to MLC and the lowest to RT. Overall, the three classifiers presented errors, but specifically for the forest patches, the highest accuracy was obtained from SVM.