A simple method for developing near real-time nationwide forest monitoring for Indonesia using MODIS near- and shortwave infrared bands

Regarding to the deforestation and forest degradation issue in Indonesia, it is an urgent need to develop near real-time forest monitoring that can be seen by the public to make sure the transparency of forest resources management. Remote-sensing technology seems to be a powerful tool for monitoring and assessing the changes in forest cover immediately. We explored 250 m multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) data sets to assign a change of temporal land surface dynamics based on two indices; normalized difference vegetation index (NDVI) and open area index (OAI). The data sets were filtered by two filtering approaches, median moving window and linear interpolation, in order to reduce the overall noise so as not to lose useful information from the time-series data. Our results indicated that the use of MODIS data as a basic information in the near real-time system offers great promise to detect the forest cover change in Indonesia’s forestland, since about 90.07% of area assigned to be a change area had actually changed. Meanwhile, about 34.97% of the forest cover change was not assigned to be a change area by the system. This result showed the need to evaluate the threshold in detecting forest cover change. Although the threshold issue is quite problematic in forest cover change detection, the results show that the methodology proposed in this study provides sufficient and useful information in forest monitoring; this includes the location, time and trajectories of the changes. The results of the detection system developed in this article will be available for potential users.     

Assessment of boreal forest height from WorldView-2 satellite stereo images

WorldView-2 (WV2) satellite stereo images were used to derive a digital surface model, which together with a high-resolution digital terrain model from airborne laser scanning (ALS) were used to estimate forest height. Lorey’s mean height (H_L) could be estimated with a root mean square error of 1.5 m (8.3%) and 1.4 m (10.4%), using linear regression, at the two Swedish test sites Remningstorp (Lat. 58°30?N, Long. 13°40?E) and Krycklan (Lat. 64°16?N, Long. 19°46?E), which contain hemi-boreal and boreal forest. The correlation coefficients were r = 0.94 and r = 0.91, respectively. The 10 m sample plots were 175 in Remningstorp and 282 in Krycklan. It was furthermore found that WV2 data are sometimes unstable for canopy top height estimations (ALS height percentile 100, p100) and that the reconstructed heights are generally located below the actual top height. The WV2 p60 was found to correlate best with ALS p70 in Remningstorp, while WV2 p95 was found to correlate best with ALS p70 in Krycklan, and it moreover reached the highest correlation for all other estimated variables, at both test sites. It was concluded that WV2 p95 height data overall represent approximately the forest height ALS p70. The overall high correlation coefficients above 0.90 at both test sites, with different forest conditions, indicate that stereo matching of WV2 satellite images is suitable for forest height mapping.     

Combining optical satellite data and airborne laser scanner data for vegetation classification

The aim of this study was to investigate to which degree the accuracy of vegetation classification could be improved by combining optical satellite data and airborne laser scanner (ALS) data, compared with using satellite data only. A Satellite Pour l'Observation de la Terre (SPOT) 5 scene and Leica ALS 50-II data from 2009, covering a test area in the mid-Sweden (latitude 60° 43′ N, longitude 16° 43′ E), were used in maximum likelihood and decision tree classifications. Training and validation data were obtained from the interpretation of digital aerial photo stereo models. Combination of SPOT and ALS data gave classification accuracies up to 72%, compared with 56% using only SPOT data. This indicates that integrating features from large area laser scanning may lead to significant improvements in satellite data-based vegetation classifications.     

Feasibility of using mobile phone to estimate forest Leaf Area Index: a case study in Yunnan Pine

Remotely-sensed Leaf Area Index (LAI) is vital to describe the vegetation canopy and assess plant growth condition and healthy status. However, sufficient instant ground LAI samples are pre-required for calibration or validation, which is generally difficult to collect. We proposed a method, LAI-Mobile, to use mobile phones with low-cost fisheye lens to take fisheye photos and to invert LAIs, which may be popularized for ordinary people to generate big volume of LAI sample data. The feasibility of LAI-Mobile was tested by comparing with LAI?2200 and GF-1 satellite data (GF = high resolution) in a pest-invaded Yunnan pine forest area in Yunnan province of China. Results show significant correlation between LAI-2200 and LAI-Mobile data for forest plots with coefficient of determination (R²) = 0.706 and Root Mean Square Error (RSME) = 0.241, and GF-1 satellite images (R² = 0.659 and RMSE = 0.268). The linear regression shows a good agreement between the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and the inverted GF-1 LAI, with R² = 0.649, RMSE = 0.795. Despite larger uncertainty for single fisheye image than LAI-2200, LAI-mobile can provide fast and convenient method to collect large amount of LAI, which will support remote sensing inversion of LAI at large scale.     

Mapping rice extent and cropping scheme in the Mekong Delta using Sentinel-1A data

Synthetic aperture radar (SAR)-based multi-temporal backscatter analysis is a common approach that has been widely used for rice mapping. Co-polarized C-band microwave backscatter (HH or VV) and the co-polarization ratio (HH/VV) are the most commonly used data sets for rice mapping due to their high data availability while the utilization of cross-polarized backscatter (HV or VH) has received less attention. In this study, Sentinel 1A time series – acquired in the dual-polarized (VV/VH) Interferometric Wide (IW) swath mode during the spring growing season (October 2015 to March 2016) in the Mekong Delta – were used to analyse the relationship between the growing cycle of rice plants and the temporal variation of SAR backscatter at different polarizations. Results show that VH backscatter is more sensitive to rice growth than VV backscatter. Several vegetation phenological parameters including beginning date, heading date and the length of the growing season were extracted from the VH backscatter time series. A decision tree approach was applied to delineate rice-cultivated areas based on seasonal phenological parameters. The classification result was validated against a 2015 land use map. The overall classification accuracy is 87.2% (kappa coefficient – κ = 0.71). In addition, the SAR-derived rice area was compared against ground statistical data at the provincial level (coefficient of determination R² = 0.98).     

Fire exclusion and the changing landscape of Queensland’s Wet Tropics Bioregion 1. The extent and pattern of transition

The vegetation and geology of the Wet Tropics Bioregion of North Queensland, covering 1?998?150 ha, were mapped at a scale of 1:50?000. The resulting geographic information system (GIS) data base provided an unprecedented opportunity to examine vegetation condition across the entire bioregion. Mapping used colour aerial photography at 1:25?000, informed by ground truthing. Vegetation type, nature of the understory and ground cover, degree and type of disturbance, and the presence of secondary vegetation were described by a coding system, with codes marked directly on the aerial photos.

Analysis of these data has confirmed a picture, which emerged from ground truthing, of large areas of sclerophyll woodland and forest being invaded by a rainforest understory that prevents regeneration of the sclerophyll canopy. Fifty-three per cent of the native vegetation of the bioregion consists of non-rainforest vegetation types, dominated in both area and number by sclerophyll woodlands and forests. Seventeen per cent of the 735?713 ha of sclerophyll woodland and forest types were assessed as having suffered irreversible change. Between 25% and 79% of individual forest vegetation types were judged to have been affected by irreversible change. No climatic changes, or changes in the environment, apart from those related to changing fire regimes, were identified as causative factors. Changed fire regimes, predominantly fire exclusion, are considered to be the most likely cause.     

New spectral metrics for mangrove forest identification

This study proposed three spectral metrics, namely spectral match degree (SMD), normalized difference mangrove index (NDMI) and shortwave infrared absorption depth (SIAD), to enhance the separability between mangrove forests and terrestrial vegetation in remote sensing imagery. The Landsat 8 OLI image of an interest area in Beilunhekou National Nature Reserve was used to test the spectral metrics. The derived spectral metrics and raw band reflectance data were classified using a support vector machine classifier. Mangrove forest maps were then identified from the classified images. Identification accuracies were compared and evaluated by determining the user’s accuracy (UA), producer’s accuracy (PA), overall accuracy (OA) and by conducting McNemar’s test. Results showed that the use of spectral metrics (UA = 85%, PA = 94%, OA = 95%) outperformed the use of raw band reflectance data (UA = 72%, PA = 82%, OA = 90%). McNemar’s test confirmed that the spectral metrics were significantly better than the raw band reflectance data (Z = 4.63, p < 0.05). Therefore, the proposed spectral metrics could improve the accuracy of mangrove forest identification.     

Monitoring anthropogenic disturbance trends in an industrialized boreal forest with Landsat time series

Human transformation of the terrestrial biosphere via resource utilization is a critical impetus for monitoring and characterizing anthropogenic change to vegetation condition. The primary objective of this research was to detect anthropogenic forest disturbance for a recent Landsat time series. A novel combination of an autonomous change detection procedure and spectral classification scheme was applied and tested in a landscape that has undergone significant resource development over the last 30 years. Anthropogenic disturbance was detected with greater than 93% accuracy. Most disturbances were correctly classified as within ±1 year. The signal of anthropogenic disturbance was significant in the landscape, accounting for more than 91% of all disturbances and 86% of total disturbed area during the 23-year study period. The study demonstrated a robust approach for examining historical disturbance trends related to human-modification of the environment.     

A morphology-based method for building change detection using multi-temporal airborne LiDAR data

The detection of building changes plays an important role for land management and urban planning. This study proposes an automatic method that applies the morphology-based method for detection of building changes with multi-temporal airborne Light Detection and Ranging (LiDAR) data. In this method, three types of land use are classified as ground, building and vegetation by Triangulated Irregular Network (TIN) filter and Support Vector Machine (SVM) with the two sets of point clouds acquired at different times. Thereafter, the morphological algorithm is applied to distinguish the changed buildings from trees, which significantly reduces the identification of errors. The changed building objects are classified as three types: ‘newly built’, ‘taller’ and ‘demolished’, with the use of rule analysis. The case study area is located in Liujia village, Shenyang, China. The two sets of LiDAR test data were acquired by Leica ALS 60 (3 points/km²) and ALS 80 (15 points/km²) from September 2014 and February 2017, respectively. Field investigation proves that the accuracy of the identified changed buildings of ‘newly built’, ‘demolished’ and ‘taller’ are 95.4%, 92.9% and 100%. The proposed method has the advantages of high accuracy and reliability, which will provide a reference for the detection of building changes.     

Estimating the vertical distribution of chlorophyll in winter wheat based on multi-angle hyperspectral data

ABSTRACT

Chlorophyll plays an important role in crop photosynthesis, which is closely related to nitrogen (N). N deficiency first occurs in the lower leaves, but the spectral detection of the lower layer is insufficient due to leaf shading. The aim of this paper was to investigate the feasibility of estimating the chlorophyll content of leaves (LCC) and the vertical distribution of LCC in wheat using multi-angle hyperspectral data. Three winter wheat layers were divided, and the multi-angle hyperspectral data of the different layers were obtained by removing the leaves from the lower layer to the top layer. The multi-angle vegetation index and LCC linear models were established, and the estimated model based on nadir view angle (i.e., conventional observation angle) was compared. Results show that (1) the best observation angle for the first layer, the second layer, and the third layer are 60°, 60°, 50°, respectively. (2) The accuracy of multi-angle-based estimation models (R² = 0.87, RMSE = 2.86 μg cm^?2) are higher than nadir-based ones (R² = 0.72, RMSE = 4.24 μg cm^?2). This study proved that vertical distribution has a positive influence on the estimation results, and multi-angle hyperspectral data could be promising in improving estimation accuracy.     

2015 Land cover map of Southeast Asia at 250 m spatial resolution

In this letter, we present the methodology and accuracy assessment results of a new 250 m spatial resolution land cover map of Southeast Asia (covering Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste and Vietnam). The map is based on semi-automated classification of Moderate Resolution Imaging Spectroradiometer data (acquired January to November 2015) supported by various auxiliary data sets. The classification scheme with 18 land cover classes reflects the characteristics of land cover in Southeast Asia and aims to provide useful input for various regional-level analyses requiring information on land cover distribution. The accuracy of the map is assessed separately for the seasonal (north of 9° N) and humid (south of 9° N) parts of the region, reaching 82% and 86% overall accuracies respectively. The map will be made available at: https://ormt-crisp.nus.edu.sg/ormt/Home/Disclaimer     

Satellite-based detection of evacuation-induced land cover changes following the Fukushima Daiichi nuclear disaster

The Great East Japan Earthquake and Tsunami on 11 March 2011 led to the Fukushima Daiichi nuclear disaster. The Japanese government subsequently outlined an evacuation zone around the power plant, and all residents were evacuated. In the absence of cropland or urban vegetation management, land cover was expected to change. The changes in vegetation cover following the nuclear disaster are presented using long-term time series data obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. Utilizing MODIS 250 m spatial resolution observations, clear signals of vegetation changes were detected following the disaster in 2011. The areas affected were non-forest regions (mostly paddy fields) within the 20 km radius evacuation zone around the power plant. Multi-year comparisons of vegetation seasonality indicated that the changes can be explained by the natural succession of abandoned cropland. The affected area within the 20 km radius is equivalent to about 20% of the total area affected by the tsunami.     

Forest Structure and Community Composition of Cochin Mangroves,South-West Coast of India

This study presents the structural characteristics of Cochin mangroves in Kerala for proposing suitable management and rejuvenation measures of degrading mangrove habitats. The floristic diversity of mangroves revealed 14 species of true mangroves belonging to six families. Multivariate analysis of true mangroves belonging to selected mangrove forests of the study area based on density could be classified into four floristic groups, a water front or low–tide zonation, mid-tide preferring species, high tide and landward zonation. Shannon Weiner index of the three stations revealed that Site I was having higher value (H′ = 2.66) followed by Site II (H′ = 2.01) and Site III (H′ = 1.595). The density of the mangroves varied significantly with sites and species (Global R = 0.537, P < 0.001). The diameter at breast height (DBH) in the study area revealed that most of the species came under 1–10 cm DBH class. The overall structural data (including Importance Value Index, DBH and basal area) showed that Site III, the Mangalavanam forest was having more structural development and could be considered as matured forest whereas, Site I, Aroor is a maturing forest and Site II, Malippuram is the least matured forest from the study. The Importance Value Index and basal area of each species could be used for analysing the maturity of the forest and habitat preferences for restoration programmes of the degraded ecosystems.     

A comparison between three surface type data sets adopted by the community radiative transfer model

The accuracy of simulated radiances for surface-sensitive visible and infrared channels is affected by the specification of surface type. The current version of Community Radiative Transfer Model (CRTM) employs three surface type data sets, which are referred to as the National Polar-orbiting Operational Environmental Satellite System (NPOESS), the U. S. Geological Survey (USGS), and the International Geosphere-Biosphere Programme (IGBP) data sets, respectively. In this study, the consistencies and discrepancies among these three surface type data sets are examined. The surface type is specified consistently among the three data sets in equatorial and polar regions, but not in other regions especially around 60° N. The desert and snow types have the highest consistency among the three surface type data sets. The tundra in the IGBP data set, the needle-leaf forecast and mixed forest in the NPOESS data set, and the mixed forest in the USGS data set have the greatest discrepancy from the other two data sets. Finally, a canonical case is used to demonstrate that the impacts of the three surface type datasets on CRTM simulations are significant, pointing to a need for a rigorously defined surface type data set.     

Long-term estimates of live above-ground tree carbon stocks and net change in managed uneven-aged mixed species forests of sub-tropical Queensland,Australia

Estimates of carbon stocks and their annual change for extensive Australian sub-tropical forests are based on indirect estimates or on data derived from temperate forests. We estimated live above-ground tree carbon (LAC) stocks at landscape level from 355?000 measurements of 94?127 tree stems from 604 permanently monitored plots representing 2.6 million ha of managed uneven-aged mixed-species native forests in sub-tropical Queensland. These plots were established between 1936 and 1998 and re-measured every 2 to 10 years up to 2011. Landscapes were represented by 16 broad vegetation groups growing across a mean annual rainfall range of 500 to 2000 mm. Landscape-mean LAC stocks varied from 20.8 ± 4.3 t C ha^?1 in inland eucalypt woodlands to 146.4 ± 11.1 t C ha^?1 in coastal wet tall open forests. Landscape maximum LAC stock, defined as the mean of maximum LAC stocks over the entire measurement history for a specified landscape under prevailing environmental conditions and disturbance regimes, including sustainable forest management, ranged from 34.0 ± 7.2 t C ha^?1 in inland eucalypt woodlands to 154.9 ± 19.4 t C ha^?1 in coastal wet tall open forests. Annual live above-ground net carbon flux (C-flux) across all forests types ranged from 0.46 to 2.92 t C ha^?1 y^?1 with an overall mean of 0.95 t C ha^?1 y^?1 (n = 2067). Comparison of our results with Intergovernmental Panel on Climate Change (IPCC) estimates shows that in all cases, except for the sub-tropical steppe, the IPCC over-estimated stocks by between 13% and 34%. Conversely, the IPCC estimated C-fluxes were between 14% and 40% less than the Queensland estimates. These results extend statistically valid estimates of landscape LAC stocks and fluxes to the sub-tropical regions of Australia.     

Wildfires in Russia in 2000–2008: estimates of burnt areas using the satellite MODIS MCD45 data

Fire activity in Russia in 2000–2008 was analysed on the basis of the newest satellite MODIS MCD45 Burned Area data. The estimated annual burned areas exhibited strong variability in magnitude, geographical location and affected vegetation, varying in total from 5.4 Mha in 2000 to 33.0 Mha in 2008. The total area burned in 2000–2008 in forests and woodlands (the areas with percent tree canopy cover >40%) was 43.4 Mha, of which about 90% was burned in the eastern Siberia and the Far East, and 53% was burned during the large fire years 2003 and 2008. The forest burned areas were mainly localized within 50–55°N, approximately along the southern edge of the area of boreal forests. The reliability of the resultant estimates was tested by comparison with the known early published results. Some applications of the results in problems of atmospheric composition and air quality are also discussed.     

The ability of a new continuous cardiac output monitor to measure trends in cardiac output following implementation of a patient information calibration and an automated exclusion algorithm

A new non-invasive continuous cardiac output (esCCO) monitoring system solely utilizing a routine cardiovascular monitor was developed, even though a reference cardiac output (CO) is consistently required. Subsequently, a non-invasive patient information CO calibration together with a new automated exclusion algorithm was implemented in the esCCO system. We evaluated the accuracy and trending ability of the new esCCO system. Either operative or postoperative data of a multicenter study in Japan for evaluation of the accuracy of the original version of esCCO system were used to develop the new esCCO system. A total of 207 patients, mostly cardiac surgical patients, were enrolled in the study. Data were manually reviewed to formulate a new automated exclusion algorithm with enhanced accuracy. Then, a new esCCO system based on a patient information calibration together with the automated exclusion algorithm was developed. CO measured with a new esCCO system was compared with the corresponding intermittent bolus thermodilution CO (ICO) utilizing statistical methods including polar plots analysis. A total of 465 sets of CO data obtained using the new esCCO system were evaluated. The difference in the CO value between the new esCCO and ICO was 0.34?±?1.50 (SD) L/min (95?% confidence limits of ?2.60 to 3.28?L/min). The percentage error was 69.6?%. Polar plots analysis showed that the mean polar angle was ?1.6° and radial limits of agreement were ±53.3°. This study demonstrates that the patient information calibration is clinically useful as ICO, but trending ability of the new esCCO system is not clinically acceptable as judged by percentage error and polar plots analysis, even though it’s trending ability is comparable with currently available arterial waveform analysis methods.     

Delayed sample arrival at the laboratory does not lead to more false negatives in the Danish population screening for colorectal cancer

In Denmark, biennial population screening for colorectal cancer was introduced in 2014 for all aged 50–74 years. Five laboratories representative for the regional division of Denmark perform the immunochemical testing of faecal occult blood in the screening samples (iFOBT, OC-Sensor (Eiken Chemical, cut-off 100 µg/L)). In July 2016, a new agreement on the public post-delivery entailed an increased lag time (five days) from the screening participant drops the screening sample into a mail-box until sample arrival at the laboratories. Previous work had reported that a lag time above five days led to more false negative iFOBT tests. We investigated if this was true also under Danish conditions. We performed two stability tests; one with sample storage at 30?°C for 14 days (N?=?60), and another with sample storage at room temperature for 13 days (N?=?10). We extracted data from our laboratory information system (LABKA) on all iFOBT tests performed in the entire Central Denmark Region (N?=?104,328 patients) during the last six months for each calendar year 2014–16. For each year, we computed the distribution of iFOBT tests below and above cut-off. Our stability tests showed no positive samples switching to false negative after storage; however, some negative samples turned false positive, especially at 30?°C. The data showed no change in the distribution of iFOBT tests below and above cut-off after July 2016. We found no evidence that an enhanced lag time increased the number of false negative iFOBT tests in the Danish screening program for colorectal cancer.     

Converting diameter measurements of Pinus radiata taken at different breast heights

Two compatible conversion factors for converting diameter measurements taken at different breast heights were derived for Pinus radiata using taper data from more than 3000 trees. The two breast heights used for conversion were 1.3 and 1.4 m above ground, as defined in Australia and New Zealand, two major radiata-growing countries in the world. The conversion factors were estimated through three alternative statistical methods including simple least squares regression, seemingly unrelated regression and errors-in-variables models. The three sets of estimates were almost identical and had similar conversion accuracy, although the second method was slightly better. The conversion factors were more accurate than overbark taper equations used for the same purpose. The factor was 0.9916 for converting diameter measured at 1.3 to that at 1.4 m above ground, and the inverse of this value, 1.0084, was for the vice versa. When calculating tree and stand volume and biomass using equations with diameter at a different breast height as a predictor to that of the input data, the bias, either over or under estimation, could be between 1.67% and 2.00% without conversion. These conversion factors will facilitate the sharing of data among radiata growing countries with different definitions of breast height, but more importantly it will help correct the bias in stand volume and biomass estimation caused by the seemingly negligible difference in breast height when software for forest resource management and decision support developed in one country is applied in another. Such bias when accumulated over a large management area may not be financially insignificant for an astute forest management agency.