Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree,random forest and information value models

This work was to generate landslide susceptibility maps for the Three Gorges Reservoir(TGR) area, China by using different machine learning models. Three advanced machine learning methods, namely, gradient boosting decision tree(GBDT), random forest(RF) and information value(InV) models, were used, and the performances were assessed and compared. In total, 202 landslides were mapped by using a series of field surveys, aerial photographs, and reviews of historical and bibliographical data. Nine causative factors were then considered in landslide susceptibility map generation by using the GBDT, RF and InV models. All of the maps of the causative factors were resampled to a resolution of 28.5 m. Of the 486289 pixels in the area,28526 pixels were landslide pixels, and 457763 pixels were non-landslide pixels. Finally, landslide susceptibility maps were generated by using the three machine learning models, and their performances were assessed through receiver operating characteristic(ROC) curves, the sensitivity, specificity,overall accuracy(OA), and kappa coefficient(KAPPA). The results showed that the GBDT, RF and In V models in overall produced reasonable accurate landslide susceptibility maps. Among these three methods, the GBDT method outperforms the other two machine learning methods, which can provide strong technical support for producing landslide susceptibility maps in TGR.

Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models.For this,a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points,which was randomly divided into two datasets for model training(70%)and model testing(30%).22 factors were initially selected to establish a landslide factor database.We applied the GeoDetector and recursive feature elimination method(RFE)to address factor optimization to reduce information redundancy and collinearity in the data.Thereafter,the frequency ratio method,multicollinearity test,and interactive detector were used to analyze and evaluate the optimized factors.Subsequently,the random forest(RF)model was used to create a landslide susceptibility map with original and optimized factors.The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve(AUC)and accuracy.The accuracy of the two hybrid models(0.868 for GeoDetector-RF and 0.869 for RFE-RF)were higher than that of the RF model(0.860),indicating that the hybrid models with factor optimization have high reliability and predictability.Both RFE-RF GeoDetector-RF had higher AUC values,respectively 0.863 and 0.860,than RF(0.853).These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models.

The global rainforest mapping project JERS-1: a paradigm of international collaboration for monitoring land cover change

The Global Rainforest Mapping (GRFM) project was initiated in 1995 and, through a dedicated data acquisition policy by the National Space Development Agency of Japan (NASDA), data acquisitions could be completed within a 1.5-year period, resulting in a spatially and temporally homogeneous coverage to contain the entire Amazon Basin from the Atlantic to the Pacific; Central America up to the Yucatan Peninsular in Mexico; equatorial Africa from Madagascar and Kenya in the east to Sierra Leone in the west; and Southeast Asia, including Papua New Guinea. To some extent, GRFM project is an international endeavor led by NASDA, with the goal of producing spatially and temporally contiguous Synthetic Aperture Radar (SAR) data sets over the tropical belt on the Earth by use of the JERS-1 L-band SAR, through the generation of semi-continental, 100m resolution, image mosaics. The GRFM project relies on extensive collaboration with the National Aeronautics and Space Administration (NASA), the Joint Research Center of the European Commission (JRC) and the Japanese Ministry of International Trade and Industry (MITI) for data acquisition, processing, validation and product generation. A science program is underway in parallel with product generation. This involves the agencies mentioned above, as well as a large number of international organizations, universities and individuals to perform field activities and data analysis at different levels.

An agent-based model of agricultural land expansion in the mountain forest of Timor Island,Indonesia

The Mutis-Timau Forest Complex,located on Timor Island,Indonesia,is a mountainous tropical forest area that gradually decreases due to deforestation and forest degradation.Previous modelling studies based on patterns indicate that deforestation primarily occurs at lower elevations and near the boundaries of forests and settlements,often associated with shifting cultivation by local farmers.This study adopts a process-based modelling approach,specifically the agent-based model,to simulate land changes,particularly farmers'expansion of agricultural land around the Mutis mountain forest.The underlying concept of this agent-based approach is the interaction between the human and environmental systems.Farmers,representing the human system,interact with the land,which represents the environmental system,through land use decision-making mechanisms.The research was conducted in the Community Forest of the Timor Tengah Utara District,one of the sites within the Mutis-Timau Forest Complex with the highest deforestation rate.Land use change simulations were performed using agent-based modelling from 1999 to 2030,considering the socio-economic conditions of farmers,spatial preferences,land use decisions,and natural transitions.The results revealed that the agricultural area increased by 14%under the Business as Usual scenario and 5%under the Reducing Emission from Deforestation and Forest Degradation scenario,compared to the initial agricultural area of 245 hectares.The probability of farmers deciding to extend agricultural activities was positively associated with the number of livestock maintained by farmers and the size of the village area.Conversely,the likelihood of farmers opting for agricultural extensification decreased with an increase in the area of private land and the farmer's age.These findings are crucial for the managers of the Mutis-Timau Forest Complex and other relevant stakeholders,as they aid in arranging actions to combat deforestation,designing proper forest-related policies,and providing support for initiatives such as reducing emissions from deforestation and forest degradation programs or further incentive schemes.

An improved algorithm for mapping burnt areas in the Mediterranean forest landscape of Morocco

The identification of burnt forests and their monitoring provide essential information for the suitable management and conservation of these ecosystems. This research focuses on the use of remote sensing with MODIS sensor data in a Mediterranean environment, precisely in the Rif region known for its high occurrence of forest fires and the largest burnt areas in Morocco. It mapped the burnt areas during the summer of 2016 using spectral indices from MODIS images, namely the Normalized Burn Ratio (NBR) and the Burnt Area Index for MODIS (BAIM). Two field surveys were used to calibrate spectral indices and validate the maps. First, a monotemporal analysis using a single pre-fire image determined the appropriate threshold of the spectral indices (BAIM and NBR) for burn detecting. Secondly, a multitemporal method was applied based on dBAIM and dNBR images which represented pre-fire and postfire differences of the BAIM and NBR images, respectively. The results show that separate use of monotemporal postfire and multitemporal methods produced an overestimation of the burnt areas. Finally, we propose a new algorithm combining both methods for burnt area mapping that we name Burnt Area Algorithm. MCD45A1 and MCD64A1 MODIS burnt area products were compared to the proposed algorithm. Validation of the estimated burnt areas using reference data of the Moroccan High Commission for Water, Forests and Fight against Desertification showed satisfactory results using the proposed algorithm, with a determination coefficient of 0.68 and a root mean square error of 44.0 ha.

Uncertainties of landslide susceptibility prediction:influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.

The Review of Land Use/Land Cover Mapping AI Methodology and Application in the Era of Remote Sensing Big Data

With the increasing number of remote sensing satellites,the diversification of observation modals,and the continuous advancement of artificial intelligence algorithms,historically opportunities have been brought to the applications of earth observation and information retrieval,including climate change monitoring,natural resource investigation,ecological environment protection,and territorial space planning.Over the past decade,artificial intelligence technology represented by deep learning has made significant contributions to the field of Earth observation.Therefore,this review will focus on the bottlenecks and development process of using deep learning methods for land use/land cover mapping of the Earth’s surface.Firstly,it introduces the basic framework of semantic segmentation network models for land use/land cover mapping.Then,we summarize the development of semantic segmentation models in geographical field,focusing on spatial and semantic feature extraction,context relationship perception,multi-scale effects modelling,and the transferability of models under geographical differences.Then,the application of semantic segmentation models in agricultural management,building boundary extraction,single tree segmentation and inter-species classification are reviewed.Finally,we discuss the future development prospects of deep learning technology in the context of remote sensing big data.

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.

Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province,China

Background:Accurate mapping of tree species is highly desired in the management and research of plantation forests,whose ecosystem services are currently under threats.Time-series multispectral satellite images,e.g.,from Landsat-8(L8)and Sentinel-2(S2),have been proven useful in mapping general forest types,yet we do not know quantitatively how their spectral features(e.g.,red-edge)and temporal frequency of data acquisitions(e.g.,16-day vs.5-day)contribute to plantation forest mapping to the species level.Moreover,it is unclear to what extent the fusion of L8 and S2 will result in improvements in tree species mapping of northern plantation forests in China.Methods:We designed three sets of classification experiments(i.e.,single-date,multi-date,and spectral-temporal)to evaluate the performances of L8 and S2 data for mapping keystone timber tree species in northern China.We first used seven pairs of L8 and S2 images to evaluate the performances of L8 and S2 key spectral features for separating these tree species across key growing stages.Then we extracted the spectral-temporal features from all available images of different temporal frequency of data acquisition(i.e.,L8 time series,S2 time series,and fusion of L8 and S2)to assess the contribution of image temporal frequency on the accuracy of tree species mapping in the study area.Results:1)S2 outperformed L8 images in all classification experiments,with or without the red edge bands(0.4%–3.4%and 0.2%–4.4%higher for overall accuracy and macro-F1,respectively);2)NDTI(the ratio of SWIR1 minus SWIR2 to SWIR1 plus SWIR2)and Tasseled Cap coefficients were most important features in all the classifications,and for time-series experiments,the spectral-temporal features of red band-related vegetation indices were most useful;3)increasing the temporal frequency of data acquisition can improve overall accuracy of tree species mapping for up to 3.2%(from 90.1%using single-date imagery to 93.3%using S2 time-series),yet similar overall accuracies were achieved using S2 time-series(93.3%)and the fusion of S2 and L8(93.2%).Conclusions:This study quantifies the contributions of L8 and S2 spectral and temporal features in mapping keystone tree species of northern plantation forests in China and suggests that for mapping tree species in China's northern plantation forests,the effects of increasing the temporal frequency of data acquisition could saturate quickly after using only two images from key phenological stages.

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.

Social and Environmental Impacts of Maryland Oil Palm Plantations on Forests,Biodiversity,and Community Livelihoods in Liberia

Liberia holds 44.5% of the remaining portion of the Upper Guinean Rainforest in West Africa,which is home to critically endangered forest elephants and western chimpanzees.The forests are of vital importance for the livelihoods of millions of West Africans and provide key ecosystem services of local and global importance for food systems transformation and agroecology.Liberia’s efforts toward land reform through legislation and policies recognise communities’rights to own and manage their customary lands and resources.These include the National Forestry Reform Law of 2006,the Community Rights Law Concerning Forest Lands of 2009,and the Land Rights Act of 2018,and more.In May 2022,a program team from the Sustainable Development Institute(SDI)-Friends of the Earth Liberia researched the social and environmental impacts of Maryland Oil Palm Plantations(MOPPs)in Liberia.Twenty-three(23)key informant interviews(KIIs)and 10 focus group discussions(FGDs)were conducted in seven communities in and around the MOPP.They included farmers,contract workers,MOPP staff,local authorities,women and youth leaders,the Environmental Protection Agency(EPA)Inspector,the Civil Society Head,and the Gender Coordinator of Maryland County.The team cross-checked information with formal documents as much as possible and took photographs and global positioning system(GPS)locations of areas of deforestation,pollution,and conflict.The team also used observation to monitor environmental pollution,such as affluent into water bodies and planting oil palm in wetlands.The team used narrative analysis and geospatial landscape analysis to analyze the data.The research finds that land conflict and deforestation have several negative impacts on communities.MOPP has not respected land tenure rights or followed Free Prior and Informed Consent(FPIC)standards,including resettlement without reparation and destruction of farms and old towns without(sufficient)compensation or restitution.During MOPP land acquisition and clearance,communities experienced the loss of their farms and the identification of villages as“village de squatters”,leading to restricted access to farmland,heightened food insecurity,and reduced income from cash crops to support families.MOPP destroyed high conservation value areas and destroyed secondary forest regrowth,which affected important biodiversity areas.MOPP is one of the four large-scale industrial palm oil plantations in Liberia in Maryland County.It has a palm oil mill in a joint venture with Golden Veroleum Liberia(GVL).Its 2011 concession agreement includes 8,800 hectares for industrial palm oil plantations.

Land Use and Forest Dynamics in the Context of Redd+: Pathway to Sustainable Environmental Management*

Reverting to nature as a major arsenals in a universal fight against Climate Change impact and loss of biodiversity, the United Nations Convention to Combat Desertification (UNCCD), views sustainable Land use and Forest (the main crux of the Glasgow declaration 2021) as the way to go. Forest conservation, protection and management in the context of REDD+ would guarantee sustainable ecosystem and mitigate climate change impacts. At National and subnational levels, the Nigerian REDD+ readiness scheme holds out hope for environmental sustainability. This study throws light into the historical background of trends in land use forest change in Nigeria, and places Nigeria on a “red” stage 3 (Low Forest Cover, High Deforestation Rate-LFHD) status while maintaining optimism that with REDD+ properly implemented in Nigeria, Stage 4: Low forest cover, Low Deforestation Rates (LFLD) and Stage 5: Low forest cover, Negative Deforestation Rates (LFND) can be achieved by 2030 and 2050 respectively, if the trio of reforestation, afforestation and natural restoration is practiced as a matter of national policy and subnational implementation within the context of REDD+. Four (4) broad drivers of deforestation and forest degradation were identified as direct, indirect, pre-disposing and planned /unplanned. The paper concludes that a viable pathway to sustainable environmental management is appropriate monitoring and evaluation of land use and forest dynamics in the context of REDD+.

Mapping and Floristic Diversity of the Nakpadjouak Community Forest, Tami Canton, Togo (West Africa)

Since 2015, community forests have been promoted in Togo as an alternative to protect areas from degradation and as a means of contributing to forest landscape restoration. The study focuses on the Nakpadjouak Community Forest (NCF) in Tami (Togo, West Africa) which contributes to community forests sustainable management. It aims in (i) mapping forest ecosystems and analysing their dynamic and (ii) characterizing the floristic diversity of the NCF. The ecosystems were mapped and their dynamic was evaluated based on Google Earth images of 2014 and 2020. Floristic and forestry inventories were carried out using the transect technique in a sample of 20 plots of 50 m × 20 m. The NCF was made up mainly by wooded/shrub savannahs (95.37%) and croplands/fallow (4.63%) in 2014. These two land use types undergone changes over the 6 years prior to 2020. By 2020, the NCF had 3 land use types: wooded/shrub savannahs (77.59%), open forest/wooded savannahs (22.23%), and croplands/fallows (0.18%). A total of 89 plant species belonging to 70 genera and 28 families were recorded within the NCF. The dominant species are: Heteropogon contortus (L.) P.Beauv. and Combretum collinum Fresen. followed by Pteleopsissuberosa Engl. & Diels, Annona senegalensis Pers. The most common species are: Lannea acida A.Rich. s.l., A. senegalensis, Vitellaria paradoxa C.F.Gaertner subsp. paradoxa, C. collinum and Acacia dudgeonii Craib ex Holland. Due to its small area of just 40 hectares and its diverse plant life, this community forest of Savannahs Region is a significant biodiversity hotspot and warrants conservation efforts.

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.

Risk Assessment Mapping of Landscape Development Based on Ecological Service and Goods in Malaysia Lowland Tropical Rainforest

Amongst the impacts of converting forest to agricultural activities are soil erosion and degradation of ecology service values and goods （ESVG）. The soil erosion can be seen as on-site impacts, such as the problems of decreasing soil fertility and also its off-site impact such as the problems of sedimentation of the nearby rivers, whilst the degradation of ESVG are more holistie in nature, These impacts can be devastating in environmental, biological, and socio-economic manners. This paper reports the study undertaken on the impacts of agricultural development in 0.8 million ha of forest dominated landscape in Pasoh Forest Region （PFR）, Malaysia, within period of 8 years from 1995 to 2003. Three folds of impacts on agricultural development examined and analysed, are： （i） relationship of total soil loss and changes in land use pattern, （ii） mapping trends of ESVG for PFR in 1995 and 2003, and （iii） risk assessment of ESVG based on simulation of converting 339,630 ha of primary forest into mass-scale oil palm plantation. Results of this study indicated that although only minor changes of about 1464 ha （about 0.2% of PFR） of primary forest was converted to agricultural activities, it have significantly increased the total soil loss from 59 to 69 million ton/ha/yr. The mean rate of soil is loss for PFR is 0.8 mil ton/ha/yr and if translated into ESVG term, the soil loss costs about US$ 4.8mil/yr. However, majority of the soil loss within all land use classes are within range of very low-low risk categories （〈10 ton/ha/yr）. ESVG for PFR were costing US$ 179 millions in 1995, declined to US$114 millions in 2003 due to 0.2% reduction of forested land. The ESVG of converting 339,630 ha primary forest into mass plantation cost less than original forest within period of 20 years examined; the 20th year of conversion, the ESVG of plantation and to-remain as forest cost US$ 963 and US$ 575 millions, respectively. However, this difference is only marginal when full attributes of ESVG are considered.

Information Collection of Land Use Status Based on Large Scale Topographic Map

With large scale topographic map charted in accordance with Topographic Map Symbols of 1:500 1:1 000 1:2 000(GB/T 20257.1-2007) as the base map of land survey,the land use status information was collected from the map based on the standard in Present Status Classification of Land Utilization(GB/T 21010-2007).The study discussed in details the information of some land types including water system,residential sites,facilities,transportation,pipeline,vegetation,soils and so on,and pointed out problems on extracting land use status information from large scale topographic map.In order to share resources and save social costs,it suggested unifying the standard to classify land types and define all kinds of land types by quantitative values.

Strategies for Safe Land Use of the Three Gorges Reservoir Area (Chongqing Section)——With the Restoration of Degraded Forest Landscapes as a Study Case

As a key for constructing ecologically safe scenery, forest landscape pattern in Chongqing Section of the Three Gorges has shown degradation, fragmentation and revision of varying degrees. To ensure normal and safe operation of the Three Gorges, and meet requirements of integrated development strategies for ecological and economic effects in Chongqing City on the ecological security of land use in Chongqing section of the Three Gorges, the following points should be attached more importance in making future strategies for an ecologically safe land use pattern with the restoration of degraded forest landscape as its starting point:a. Ecological effects and economic functions that can be supplied by forest landscape elements in different restoration patterns should be understood to obtain background effect of the ecological security pattern scenario of land use in the reservoir region; b. Relationship between restoration of degraded forest landscapes and serious ecological interference factors such as degradation background, artificial disturbance and engineering stress, should be simulated to figure out the influence of natural or artificial driving factors on landscape pattern, determine the future restoration mode for the degraded forest landscapes in the reservoir region, so as to facilitate the construction of great ecological-economic security pattern of the Three Gorges. The findings will provide scientific basis for the decision-making in building an ecologically safe land use pattern in the Three Gorges reservoir area (Chongqing), when using the degradation of forest landscape restoration as a carrier at present or in the future. Further, they will help realize the development goals of "Livable Chongqing, Expedite Chongqing, Forestry Chongqing, Safe Chongqing and Healthy Chongqing".

Quantitative Simulation of Dynamic Changes in Cultivated Land in Areas of Reclamation and Returning Cultivated Land to Forest or Pastures under RCPs Climate Scenarios

Northeast China as one of important agricultural production bases is an area under reclamation and returning cultivated land to forests or pastures. Therefore, it is of great practical significance in guaranteeing the sustainable development and national food security to study the spatial and temporal variation of cultivated land in Northeast China under future climate scenarios. In this study, based on data of land use, natural environment and social-economy, dynamics of land system（DLS） model was used to to simulate the spatial distribution and changing trends of cultivated land in the typical areas of reclamation and returning cultivated land to forest or pastures in Northeast China during 2010-2030 under land use planning scenario and representative concentration pathways（RCPs） scenarios quantitatively.The results showed that the area of cultivated land had an overall decreasing trend under the land use planning scenario, but the area of upland field increased slightly from 2000 to 2010 and then declined greatly, while the area of paddy field continuously declined from 2000 to 2030. Under the Asia-Pacific Integrated model（AIM）scenario, the total area of cultivated land had a tendency to increase considerably,with the upland field expanding more obviously and the paddy field declining slightly.In addition, the cultivated land showed a greater decreasing trend under the model for energy supply strategy alternatives and their general environmental impact（MESSAGE） scenario compared to the land use planning scenario. Moreover, analysis on the conversion between different land use types indicated that the reclamation and returning cultivated land to forests or pastures was likely to continue under future scenarios, but the frequency of occurrence could decrease as the time goes by. The conclusions can provide significant decision-making information for the rational agricultural planning and cultivated land protection in Northeast China to adapt to the climate change.

Land cover changes and fragmentation in mountain neotropical ecosystems of Oaxaca, Mexico under community forest management

Changes in land cover have a direct impact on forest ecosystem goods and services. In this study, changes in land cover in Sierra de Juarez–Oaxaca ecosystems were estimated using a consistent processing of Landsat images and OBIA methodology. Additionally, landscape analyses using FRAGSTAT were conducted. In 2014, Sierra de Juarez–Oaxaca was covered by approximately 84% of forests, mainly pine-oak and cloud forests. After extensive deforestation until 2001, this trend was reversed and the forest cover surface area in 2014 was slightly higher than in 1979. The comparison of the landscape structure of the forested and agricultural lands suggests an increase in habitat heterogeneity. However, interspersion and juxtaposition indices, showing the patch shape by patch area and perimeter, were similar throughout the study period(1979–2014). Social and economic drivers can explain this situation: namely, community organization, forest enterprises, payment for ecosystem services programs, and changes of agricultural activity. Communities in the Sierra of Oaxaca have reforested degraded lands, created community forest enterprises, and preserved the forest under conservation schemes like those proposed by the Mexican payment for ecosystem services programs. However, their sustainable management faces internal challenges and has become highly dependent on political and institutional decisions beyond their control.

Integrated use of GIS, remote sensing and multi-criteria decision analysis to assess ecological land suitability in multi-functional forestry

The Zagros forests are a treasure of valuable oak forests, but they have been severely degraded from long-term misuse. Geographic information systems （GIS） and multi-criteria decision analysis （MCDA） have been increasingly used to improve the management of vulnerable ecosystems to prevent further degradation and increase the sustainability of land use. This study presents a methodology to assess land suitability using remote sensing （RS） to obtain wall-to-wall data for the calculations, GIS to analyze the data, and MCDA to rank alternative land uses. The criteria and subcriteria affecting the suitability of land for different uses were identified and weighted using an analytic hierarchy process. Variables used as subcriteria were assessed using satellite data and other sources of information such as existing maps and field surveys. Numerical values for the subcriteria were classified, and each class was given a priority rating according to expert judgments. Based on the ratings and weights of the subcriteria, a priority map was created for each land use using the weighted linear combination method. The priority maps for different land uses were overlaid to obtain a preliminary land use map, which often indicated several simultaneous land uses for the same location. The preliminary map was further edited by removing unrealistic, mutually exclusive land-use combinations. The study tested and demonstrated the potential of integrating RS, G1S and MCDA techniques for solving complicated land allocation problems in forested regions using a scientifically sound and practical approach for efficient and sustainable allocation of forestland for different uses.