Telecoupled landscapes: Spatial effects of external financial inflows on Africa’s biodiversity

ABSTRACTThis study was conducted to determine the patterns and drivers of forest land cover changes in Bobiri and Oboyow Forest Reserves (BFR and OFR, respectively), Ghana. Landsat images were employed to determine forest land cover types and changes in 1990, 2000 and 2010 using supervised classification method. Factors that drive forest land cover changes in the forest reserves were determined using a semi-structured questionnaire and field observations. Generally, closed-canopy forest decreased by 49% in both forests over 20-year period resulting in a tremendous increase in open-canopy forest (BFR: 85%; OFR: 46%) and non-forest land cover types (BFR: 48–80% OFR: 127–350%). Factors such as logging manual illiteracy among timber operators, offences of authorised timber operators, ineffective community participation, harvesting schedule revision, chainsaw logging, illegal logging, bushfires, fuel gathering and weak penalty for offences were identified as contributing to rapid depletion of closed canopy forest cover in the forest reserves.

Due to human activities Lithuanian historical land use and land cover (LULC) changed. Forest land cover was also changing. 200 years ago, forests occupied almost 40% of the entire Lithuania territory, in 1914 – only 20%, and in 1939 – only 17%. The historical changes in forest land cover were mainly related to deforestation. Today, the forest cover is increasing in Lithuania, but changes within different municipalities boundaries differ. The aim of this study was to compare geodata of forest land cover, within different municipalities of Lithuania boundaries, in the 1950s and 2017 for a better understanding of spatial patterns of occurred forest land cover changes. To evaluate forest land cover changes in Lithuania, two geodatabases, representing the forest cover in the 1950s and 2017, were used. Methods of descriptive statistical analysis, spatial autocorrelation, cluster and outlier analysis using ArcGIS and MS EXCEL software were used to process, evaluate and represent the data of this study. The results of this study revealed that forest land cover increased by 7.1% during the investigated period resulting in the forest land area proportion of 33.6% in 2017. Despite positive changes of Lithuania forest land cover, two municipalities met afforestation. Forest land covers most intensively, i. e. 17.1–31.1%, increased in the south-eastern part of Lithuania and west-central part of Lithuania. The most passive afforestation was detected in northern, central, and south-western parts of Lithuania, where forest land cover increased only from 1.2% to 4.9%.

Deforestation and changes in forest cover are subject to the effects of many factors: economic, social, cultural, political, and environmental and their interactions. To assess these factors, one should necessarily know their nature and then model them. Nowadays many models have been introduced to explore deforestation, however most of them are based on past events and do not pay enough attention to socioeconomic factors. Moreover, considering the future is sometimes absent in those models. The present study develops an agent-based model for studying deforestation and changes in forest land cover using a case study. The model assesses deforestation in the past and then concentrates on the future of forest land cover. The study area is located in the north of Iran. The socioeconomic factors considered in this research as agents are farmers, ranchers and lumberjacks. The information and statistics of the agents were gathered over 10 years and so their manner in the forest was simulated. To validate the model, satellite imagery was used. The comparison between the simulated forest and the real one shows the power of the model as being 76% correct considering the Kappa coefficient. The results of forecasting reveal that the forest land cover will be reduced by 189 ha in 10 years time. Also the results show that in the study area the ranchers have more severe effects on deforestation than the beneficiaries.

Evaluating forest policy implementation effectiveness with a cross-scale remote sensing analysis in a priority conservation area of Southwest China

Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia

Digital and scalable technologies are increasingly important for rapid and large-scale assessment and monitoring of land cover change. Until recently, little research has existed on how these technologies can be specifically applied to the monitoring of Reducing Emissions from Deforestation and Forest Degradation (REDD+) activities. Using the Google Earth Engine (GEE) cloud computing platform, we applied the recently developed phenology-based threshold classification method (PBTC) for detecting and mapping forest cover and carbon stock changes in Siem Reap province, Cambodia, between 1990 and 2018. The obtained PBTC maps were validated using Google Earth high resolution historical imagery and reference land cover maps by creating 3771 systematic 5 × 5 km spatial accuracy points. The overall cumulative accuracy of this study was 92.1% and its cumulative Kappa was 0.9, which are sufficiently high to apply the PBTC method to detect forest land cover change. Accordingly, we estimated the carbon stock changes over a 28-year period in accordance with the Good Practice Guidelines of the Intergovernmental Panel on Climate Change. We found that 322,694 ha of forest cover was lost in Siem Reap, representing an annual deforestation rate of 1.3% between 1990 and 2018. This loss of forest cover was responsible for carbon emissions of 143,729,440 MgCO2 over the same period. If REDD+ activities are implemented during the implementation period of the Paris Climate Agreement between 2020 and 2030, about 8,256,746 MgCO2 of carbon emissions could be reduced, equivalent to about USD 6-115 million annually depending on chosen carbon prices. Our case study demonstrates that the GEE and PBTC method can be used to detect and monitor forest cover change and carbon stock changes in the tropics with high accuracy.


 ABSTRACT
 
 This research aims to: 1) know the changes in forest land cover into settlements in Lima Puluh Kota Regency. 2) knowing the suitability of changes in forest land cover to settlements with the Lima Puluh Kota Regency RTRW.
 This research is a descriptive study with secondary data analysis consisting of numeric data, namely data in the form of numbers obtained from relevant agencies and map data. The data analysis technique used is map overlay. The results showed that: 1) changes in forest land cover area into settlements in Lima Puluh Kota District for a period of 22 years from 1996 - 2018 were obtained through an analysis of land cover maps in 1996, 2006, 2018. Lima Puluh District experienced extensive changes forest amounting to 2,195.27 hectares which turned into residential land. 2) changes in forest land cover to residential land that are in accordance with the Lima Puluh Kota District RTRW of the city are 305,967.68 Ha and those that do not match amount to 1,969.3 Ha.

The temperate forest is a complex biome due to the diversity of forest types, forest cover change dynamics and forest use management practices. While temperate forests play an important role in the global carbon cycle, their net carbon exchange is uncertain. Quantifying forest cover change is an important step in documenting disturbance regimes and carbon exchange estimates. Biome-wide gross forest cover loss was estimated using a probability-based sampling approach that integrated moderate and high spatial resolution satellite data sets. Area of gross forest cover loss from 2000 to 2005 within the temperate forest biome is estimated to be 1.03% of the total biome area, or 18.41 Mha. Estimated forest cover loss represented a 3.5% reduction in year 2000 forest area. About 68% of the total forest cover loss occurred in Eastern North America and in Europe. The mid-latitude forests of the United States exhibited the highest forest cover loss rates within the biome. Biome-wide rate of gross forest cover loss gradually increased from 2001 to 2005. The lowest change was detected in 2004, followed by the year of the highest change over the 5-year period. The regional forest cover change dynamics were confirmed by official forest fire and timber production statistics. The validation of the MODIS-based product demonstrated its efficiency in forest cover mapping and monitoring. Forest cover change monitoring using the approach presented should bring greater understanding on forest cover dynamics in temperate forests and enable improved carbon accounting.

Global, Landsat-based forest-cover change from 1990 to 2000

Malaria prevalence and forest cover change in Kenya: a geospatial analysis

Forest cover and land use change directly impact biological diversity worldwide, contribute to climate change and affect the ability of biological systems to support human needs by altering ecosystem services. Given the forest land use characteristics and ecosystem types in Luang Namtha Province, Lao PDR, the forest cover and land cover category of Luang Namtha Province were divided into six classes, i.e., current forest (CF), potential forest (PF), other wooded areas (OW), permanent agricultural land (PA), other non-forest areas (NF) and water (W). In first instance, earlier geographic information data (GIS data) of forest cover and land use during 1992 and 2002 was obtained from the Ministry of Agriculture and Forestry (MAF), Lao PDR. Two steps of forest land use change assessment were conducted by the MAF, i.e., plot sampling on satellite image maps (SIMs) to detect the changes of forest cover and land use during 1992 and 2002 for the entire Luang Namtha Province and field verification in order to identify causes of the changes. Secondly, dynamic information of the forest land cover changes during this ten-year period was calculated by means of map algebra in ArcGIS 9.2. Thirdly, based on the theory of ecosystem service functions and the service function values of different global ecosystems, the value of the six forest cover and land use categories in the province was obtained. Finally, ecological environmental effects, produced by the regional land cover changes over the study period, were calculated.

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

Abstract. Dramatic political and economic changes in Eastern European countries following the dissolution of the “Eastern Bloc” and the collapse of the Soviet Union greatly affected land-cover and land-use trends. In particular, changes in forest cover dynamics may be attributed to the collapse of the planned economy, agricultural land abandonment, economy liberalization, and market conditions. However, changes in forest cover are hard to quantify given inconsistent forest statistics collected by different countries over the last 30 years. The objective of our research was to consistently quantify forest cover change across Eastern Europe from 1985 until 2012 using the complete Landsat data archive. We developed an algorithm for processing imagery from different Landsat platforms and sensors (TM and ETM+), aggregating these images into a common set of multi-temporal metrics, and mapping annual gross forest cover loss and decadal gross forest cover gain. Our results show that forest cover area increased from 1985 to 2012 by 4.7% across the region. Average annual gross forest cover loss was 0.41% of total forest cover area, with a statistically significant increase from 1985 to 2012. Most forest disturbance recovered fast, with only 12% of the areas of forest loss prior to 1995 not being recovered by 2012. Timber harvesting was the main cause of forest loss. Logging area declined after the collapse of socialism in the late 1980s, increased in the early 2000s, and decreased in most countries after 2007 due to the global economic crisis. By 2012, Central and Baltic Eastern European countries showed higher logging rates compared to their Western neighbours. Comparing our results with official forest cover and change estimates showed agreement in total forest area for year 2010, but with substantial disagreement between Landsat-based and official net forest cover area change. Landsat-based logging areas exhibit strong relationship with reported roundwood production at national scale. Our results allow national and sub-national level analysis of forest cover extent, change, and logging intensity and are available on-line as a baseline for further analyses of forest dynamics and its drivers.

This document and accompanying maps describe land cover classifications and change detection for a 13.8 million ha landscape straddling the border between Minnesota, and Ontario, Canada (greater Border Lakes Region). Land cover classifications focus on discerning Anderson Level II forest and nonforest cover to track spatiotemporal changes in forest cover. Multi-temporal Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Multi-Spectral Scanner (MSS) data from 1972 to 2000 were used to classify forest cover types and disturbances at 5-year intervals. A composite dataset depicting the period of forest disturbance was produced using the 1975-2000 sequence of land cover data. These land cover change data were produced to facilitate analysis of forest disturbance patterns, to support landscape simulation modeling, and to support cross-ownership land management within the region. A double-sided fold-out map shows A) forest land cover change across differently managed forests, and B) classified period of forest canopy disturbance for the entire study area. Digital versions of the map are available online, as are the datasets and code used to produce them. Order a printed copy of this publication Time series examples from differently managed areas of the Border Lakes ecoregion (PDF Map)(1.9 MB) Period of most recent forest canopy disturbance (PDF Map) (1.8 MB) Data set, including metadata, GIS data and supplemental files

Pakistan has an annual deforestation rate of 4.6% which is the second highest in Asia. It has been described by the Food and Agriculture Organization (FAO) that the deforestation rate increased from 1.8–2.2% within two decades (1980–2000 and 2000–2010). KPK (Khyber Pakhtunkhwa), Pakistan’s northwestern province, holds 31% of the country’s total forest resources, the majority of which are natural forests. The Malam Jabba region, known for its agro-forestry practices, has undergone significant changes in its agricultural, forestry, and urban development. Agricultural and built-up land increased by 77.6% in the last four decades, and significant changes in land cover especially loss in forest, woodland, and agricultural land were observed due to flood disasters since 1980. For assessing and interpreting land-cover dynamics, particularly for changes in natural resources such as evergreen forest cover, remote sensing images are valuable assets. This study proposes a framework to assess the changes in vegetation cover in the Malam Jabba region during the past four decades with Landsat time series data. The random forest classifier (RF) was used to analyze the forest, woodland, and other land cover changes over the past four decades. Landsat MMS, TM, ETM+, and OLI satellite images were used as inputs for the random forest (RF) classifier. The vegetation cover change for each period was calculated from the pixels using vegetation indices such as NDVI, SAVI, and VCI. The results show that Malam Jabba’s total forest land area in 1980 was about 236 km2 and shrank to 152 km2 by 2020. The overall loss rate of evergreen forests was 35.3 percent. The mean forest cover loss rate occurred at 2.1 km2/year from 1980 to 2020. The area of woodland forest decreased by 87 km2 (25.43 percent) between 1980 and 2020. Other landcover increased by 121% and covered a total area of 178 km2. The overall accuracy was about 94% and the value of the kappa coefficient was 0.92 for the change in forest and woodland cover. In conclusion, this study can be beneficial to researchers and decision makers who are enthusiastic about using remote sensing for monitoring and planning the development of LULC at the regional and global scales.