Synergizing community-based forest monitoring with remote sensing: a path to an effective REDD+ MRV system

Background

The reliable monitoring, reporting and verification (MRV) of carbon emissions and removals from the forest sector is an important part of the efforts on reducing emissions from deforestation and forest degradation (REDD+). Forest-dependent local communities are engaged to contribute to MRV through community-based monitoring systems. The efficiency of such monitoring systems could be improved through the rational integration of the studies at permanent plots with the geospatial technologies. This article presents a case study of integrating community-based measurements at permanent plots at the foothills of central Nepal and biomass maps that were developed using GeoEye-1 and IKONS satellite images.

Results

The use of very-high-resolution satellite-based tree cover parameters, including crown projected area (CPA), crown density and crown size classes improves salience, reliability and legitimacy of the community-based survey of 0.04% intensity at the lower cost than increasing intensity of the community-based survey to 0.14% level (2.5 USD/ha vs. 7.5 USD/ha).

Conclusion

The proposed REDD+ MRV complementary system is the first of its kind and demonstrates the enhancement of information content, accuracy of reporting and reduction in cost. It also allows assessment of the efficacy of community-based forest management and extension to national scale.

     

Research on auto-classification method of remote sensing images in mountainous areas—An application in Southwest of China

In mountainous areas, it is the undulant terrain, various types of geomorphic and land use that make the remote sensing images great metamorphism. Moreover, due to the elevation, there are many areas covered with shadow, clouds and snow that make the images more inaccurate. As a result, it would be very difficult to carry out auto-classification of RS images in these areas. The study took Southwest China as the case study area and the TM images, SPOT images as the basic information sources assisted by the auxiliary data of DEM, NDVI, topographical maps and soil maps to preprocess the images. After preprocessing by topographic correction and wiping off clouds, snow and shadows, all the image data were stacked together to form the images to be classified. Then, the research used segmentation technology and hierarchical method to extract the main types of land use in the area automatically. The results indicated that the qualitative accuracies of all types of land use extracted in Southwest China were above 90 percent, and the quantitative accuracies was above 86 percent. The goal of reducing workloads had been realized. Supported by the National Public Welfare Project on Environmental Protection (2007KYYW21), the Program of National Science and Technology research( 2006BAC01A01-05).     

Ramgarh crater,Rajasthan, India: Study of Multispectral images obtained by Indian remote sensing satellite (IRS‐IA)

Ramgarh Crater, Rajasthan, India is a potential impact crater that has not been studied so far. The proximity of Ramgarh Crater to the Deccan flood basalt terrain makes it important to examine the spatial and temporal relationship of this crater to Deccan Volcanism because recent studies propose a strong link between impact cratering and major flood basalt eruptions. A detailed multidisciplinary study is necessary to evaluate the structure and lithology of Ramgarh Crater and its temporal relationship to the emplacement of Deccan eruptions in India .Application of the IRS‐IA data to study the lithologic/surface characteristics of Ramgarh Crater (attempted for the first time) indicates the potential application of remote sensing data in these studies. The IRS‐IA data are of good quality and resolution. Our preliminary assessment has shown that these data are helpful in generating lithology soil vegetation profiles of Ramgarh Crater region. These “profile maps” would be useful for targeting the specific areas in the region for a closer look and ground truth verification during the field work and sample collection in the region.     

The application of remote sensing data to diagnose soil degradation in the Dakhla depression – Western Desert,Egypt

Dakhla depression in Egypt’s Western Desert is experiencing two soil degradation processes, notably: soil salinization and sand encroachment. The present study aimed to diagnose the severity of these processes using remote sensing. Soil salinity was determined by spectral regression analysis between tasselled cap spectral transform extracted from a Landsat-8 image acquired in September 2013 along with synchronized soil salinity measurements. Assessment of sand advance rate was conducted by temporal change detection of brilliant crescentic sand dune visualized by Google Earth in old (2002) and recent (2013) images. Results showed that salinized soils (dS/m4<) represent 91% of bare lands and salinization is attributed to aridity, topography and poor drainage. Barchan dunes north and south of Abu Tartur escarpment moved at rates of 5.9 and 3.6 m/year, respectively. The escarpment protected the majority of the depression from massive dune invasion. However, sand encroachment is clearly observed west of the depression.     

How well do we understand Earth observation electro-optical sensor parameters?

Since the launch of LANDSAT-1, the Earth observation systems' capability and utilisation has increased substantially. The past decade has seen a number of remote sensing satellites with improved capabilities. Understanding the sensor parameters from a user's standpoint is not that easy. In the context of increasing relevance and dependence on fusion of data from various sensors, there is an urgent need to standardise the sensor parameters specified. The present paper is an attempt to raise some of the concerns regarding specifying spatial, radiometric, spectral and temporal resolution. I solicit inputs from the readers on the points raised to formulate a consolidated recommendation for the ISPRS Council to propose to the international community.     

Monitoring and analysing the Emirate of Dubai’s land use/land cover changes: an integrated,low-cost remote sensing approach

This study presents a modified low-cost approach, which integrates the spectral angle mapper and image difference algorithms in order to enhance classification maps for the purpose of monitoring and analysing land use/land cover change between 2000 and 2015 for the Emirate of Dubai. The approach was modified by collecting 320 training samples from QuickBird images with a spatial resolution of 0.6 m, as well as carrying out field observations, followed by the application of a 3?×?3 Soble filter, sieving classes, majority/minority analysis, and clump classes of the obtained classification maps. The accuracy assessment showed that the targeted 2000, 2005, 2010 and 2015 classification maps have 88.1252%, 89.0699%, 90.1225% and 96.0965% accuracy, respectively. The results showed that the built-up area increased by 233.721?km² (5.81%) between 2000 and 2005 and continues to increase even up and till the present time. The assessment of changes in the periods 2000–2005 and 2010–2015 confirmed that net vegetation area losses were more pronounced from 2000 to 2005 than from 2010 to 2015, dropping from 47,618 to 40,820?km², respectively. This study is aimed to assist urban planners and decision-makers, as well as research institutes.     

Monitoring recovery after earthquakes through the integration of remote sensing,GIS, and ground observations: the case of L’Aquila (Italy)

The usefulness of remote sensing (RS), geographical information systems, and ground observations for monitoring changes in urban areas has been demonstrated through many examples over the last two decades. Research has generally focused on the relief phase following a disaster, but we have instead investigated the subsequent phases involving early recovery, recovery, and development. Our aim was to determine to what extent integration of the available tools, techniques, and methods can be used to efficiently monitor the progress of recovery following an earthquake. Changes in buildings within the Italian city of L’Aquila following the 2009 earthquake were identified from Earth observation data and are used as indicators of progress in the recovery process. These changes were identified through (1) visual analysis, (2) automated change detection using a set of decision rules formulated within an object-based image analysis framework, and (3) validation based on a combination of visual and semiautomated interpretations. An accuracy assessment of the automated analysis showed a producer accuracy of 81% (error of omission: 19%) and a user accuracy of 55% (error of commission: 45%). The use of RS made it possible for the identification of changes to be spatially exhaustive, and also to increase the number of categories used for a recovery index. In addition, using RS allowed the area requiring extensive fieldwork (to monitor the progress of the recovery process) to be reduced.     

Monitoring mangrove forests: Are we taking full advantage of technology?

Mangrove forests grow in the estuaries of 124 tropical countries around the world. Because in-situ monitoring of mangroves is difficult and time-consuming, remote sensing technologies are commonly used to monitor these ecosystems. Landsat satellites have provided regular and systematic images of mangrove ecosystems for over 30 years, yet researchers often cite budget and infrastructure constraints to justify the underuse this resource. Since 2001, over 50 studies have used Landsat or ASTER imagery for mangrove monitoring, and most focus on the spatial extent of mangroves, rarely using more than five images. Even after the Landsat archive was made free for public use, few studies used more than five images, despite the clear advantages of using more images (e.g. lower signal-to-noise ratios). The main argument of this paper is that, with freely available imagery and high performance computing facilities around the world, it is up to researchers to acquire the necessary programming skills to use these resources. Programming skills allow researchers to automate repetitive and time-consuming tasks, such as image acquisition and processing, consequently reducing up to 60% of the time dedicated to these activities. These skills also help scientists to review and re-use algorithms, hence making mangrove research more agile. This paper contributes to the debate on why scientists need to learn to program, not only to challenge prevailing approaches to mangrove research, but also to expand the temporal and spatial extents that are commonly used for mangrove research.     

Land use and land cover changes over a century (1914–2007) in the Neyyar River Basin,Kerala: a remote sensing and GIS approach

Abstract

Land use and land cover change, perhaps the most significant anthropogenic disturbance to the environment, mainly due to rapid urbanization/industrialization and large scale agricultural activities. In this paper, an attempt has been made to appraise land use/land cover changes over a century (1914–2007) in the Neyyar River Basin (L=56 km; Area = 483.4 km²) in southern Kerala – a biodiversity hot spot in Peninsular India. In this study, digital remote sensing data of the Indian Remote Sensing satellite series I-D (LISS III, 2006–2007) on 1:50,000 scale, Survey of India (SOI) toposheet of 1914 (1:63,360) and 1967 (1:50,000) have been utilized to map various land use/land cover changes. Maps of different periods have been registered and resampled to similar geographic coordinates using ERDAS Imagine 9.0. The most notable changes include decreases in areas of paddy cultivation, mixed crops, scrub lands and evergreen forests, and increases in built-up areas, rubber plantations, dense mixed forests, and water bodies. Further, large scale exploitation of flood plain mud and river sand have reached menacing proportions leading to bank caving and cut offs at channel bends. Conservation of land and water resources forms an important aspect of ecosystem management in the basin.     

Comparing the fractality of European urban neighbourhoods: do national contexts matter?

The objective of this paper is to show that morphological similarities between built-up urban surfaces are greater across borders than within cities in Europe: living, architectural and planning trends are international. The spatial arrangement of built-up areas is analysed here by means of fractal indices using a set of 97 town sections selected from 18 European urban agglomerations. The fractal dimension is estimated by correlation techniques. Results confirm that morphological similarities are higher across countries/cities than within. Moreover, two types of fractal laws are considered: one uses the basic fractal scaling law; the other introduces a prefactor a that is often called a “form factor” in the fractal literature. Differences in the results obtained by both laws are explained empirically as well as theoretically, and suggestions are made for further measurements.     

Extracting urban water bodies from high-resolution radar images: Measuring the urban surface morphology to control for radar’s double-bounce effect

Satellite remote sensing is an effective method for extracting water bodies on a large scale. Radar imagery, such as synthetic aperture radar (SAR) imagery, can penetrate clouds and provide opportunities for water body identification when in situ observations are difficult to obtain because of severe weather conditions. However, when using SAR images in urban areas to extract water bodies, the radar’s double-bounce effect results in complicated backscatter patterns of water near urban features such as buildings due to the side-looking properties of SAR sensors and the vertical urban structures. Therefore, the objective of this study is to propose a reliable urban water extraction framework for SAR images that integrates urban surface morphological features for controlling radar’s multiple bounces. Statistical (logistic regression) and machine-learning (random forest) models were used to explore how radar’s double-bounce effect influences the prediction performance of urban water extraction. Our findings indicate that when extracting urban water bodies, urban water’s backscatter values could be significantly interfered by the neighboring building density above a threshold height that contributes to radar’s multiple bounces. Without model calibration, our framework incorporating urban surface morphology demonstrates high prediction ability with an Area Under the Curve (AUC) of 0.914 and with 97.0% of urban water cells correctly identified by testing in another city sharing similar urban forms. In summary, our study provides a better understanding of the role of the urban surface morphology in the double-bounce effect in SAR images, specifically for differentiating urban water and land, thereby improving the accuracy of urban water extraction and enhancing the feasibility of further applications of SAR imagery under complex urban landscapes.     

Species’ habitat use inferred from environmental variables at multiple scales: How much we gain from high-resolution vegetation data?

Spatial resolution of environmental data may influence the results of habitat selection models. As high-resolution data are usually expensive, an assessment of their contribution to the reliability of habitat models is of interest for both researchers and managers. We evaluated how vegetation cover datasets of different spatial resolutions influence the inferences and predictive power of multi-scale habitat selection models for the endangered brown bear populations in the Cantabrian Range (NW Spain). We quantified the relative performance of three types of datasets: (i) coarse resolution data from Corine Land Cover (minimum mapping unit of 25 ha), (ii) medium resolution data from the Forest Map of Spain (minimum mapping unit of 2.25 ha and information on forest canopy cover and tree species present in each polygon), and (iii) high-resolution Lidar data (about 0.5 points/m²) providing a much finer information on forest canopy cover and height. Despite all the models performed well (AUC > 0.80), the predictive ability of multi-scale models significantly increased with spatial resolution, particularly when other predictors of habitat suitability (e.g. human pressure) were not used to indirectly filter out areas with a more degraded vegetation cover. The addition of fine grain information on forest structure (LiDAR) led to a better understanding of landscape use and a more accurate spatial representation of habitat suitability, even for a species with large spatial requirements as the brown bear, which will result in the development of more effective measures to assist endangered species conservation.     

“You know you can do this,right?”: developing geospatial technological pedagogical content knowledge and enhancing teachers’ cartographic practices with socio-environmental science investigations

Many barriers exist to K–12 classroom teachers’ adoption and implementation of geospatial technologies with their students. To address this circumstance, we have developed and implemented a geospatial curriculum approach to promote teachers’ professional growth with curriculum-linked professional development (PD) to support the adoption of socio-environmental science investigations (SESI) in an urban school environment that includes reluctant learners. SESI focus on social issues related to environmental science. The pedagogy is inquiry-driven, with students engaged in map-based mobile data collection and subsequent analysis with Web-based dynamic mapping software to answer open-ended questions. Working with four science and social studies teachers, we designed and implemented a sequence of three locally oriented, geospatial inquiry projects that were implemented with 140 9th grade students. We investigated how the geospatial curriculum approach impacted the teachers’ geospatial pedagogical content knowledge (PCK), their cartographic practices, and promoted geospatial thinking and analysis skills with their students. Findings revealed strong growth in teachers’ geospatial PCK, increased map use by teachers, use of maps as media for inquiry and not didactic instruction, and modeling to guide students’ geospatial analysis using GIS. Implications for PD to promote teachers’ geospatial PCK and in-class cartographic practices are discussed.     

Compactness,connectivity, and walking accessibility on the neighborhood level according to sustainability certifications: improvement or downgrade? A case study of Cairo,Egypt

Urban sustainability certifications (USCs) urge developers to exceed the local norms and regulatory requirements to attain sustainability. USCs are gaining international recognition as planning and policy support tools. This study aims to assess the relevance of four USCs (LEED for Neighborhood Development, BREAAM communities, CASBEE for Urban Development, and Pearl Community Rating System) in contexts outside their country of origin using Cairo Governorate as a case study. The study focuses on compactness, street connectivity, and walking accessibility as prominent components for sustainable mobility and urban form at the neighborhood level. The study examines 202 neighborhoods in Cairo in terms of compactness and then focuses on eight urban areas in different locations and with different characteristics to assess their connectivity and walking accessibility. Different analyses were performed with ArcGIS software using data about neighborhoods’ population, residential units, street networks, established buildings, buildings’ outlines and heights, and detailed uses. Results show that USCs’ indicators and thresholds are generally lenient and insensitive to the context of formal areas in Cairo Governorate, which are significantly more compact, mixed (horizontally and vertically), and connected. This study adds to the currently limited empirical evidence refuting the use of some USCs as global tools and questioning their utilization in different contexts either as they are or even through an adaptation process.